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Summary: The wireless sensor network (WSN) has been assimilated into modern society and is utilized in many crucial application domains, including animal monitoring, border surveillance, asset monitoring, and so forth. These technologies aid in protecting the place of the event's occurrence from the adversary. Maintaining privacy concerning the source location is challenging due to the sensor nodes' limitations and efficient routing strategies. Hence, this research introduces a novel source location privacy preservation using the awareness‐based Gannet with random‐Dijkstra's algorithm (AGO‐RD). The network is initialized by splitting the hotspot and non‐hotspot region optimally using the proposed awareness‐based Gannet (AGO) algorithm. Here, the multi‐objective fitness function is utilized to initialize the network based on factors like throughput, energy consumption, latency, and entropy. Then, the information is forwarded to the phantom node in the non‐hotspot region to preserve the source location's privacy, which is far from the sink node. The proposed random‐Dijkstra algorithm is utilized to route the information from the phantom node to the sink with more security. Analysis of the proposed AGO‐RD‐based source location privacy preservation technique in terms of delay, throughput, network lifetime, and energy consumption accomplished the values of 6.52 ms, 95.68%, 7109.9 rounds, and 0.000125 μJ. [ABSTRACT FROM AUTHOR]

Due to the influence of the errors of time difference of arrivals (TDOA) and the premeasured velocity in the propagation medium, the existing methods always hold the low location accuracy and efficiency. To increase the location accuracy, the joint inversion of AE/MS sources and propagation velocity is proposed using the full measurements and residual estimation. First, the control equation of the unknown wave velocity is constructed using sensor coordinates and full TDOA measurements, and it is linearized by introducing the additive variables. Second, considering the influence of TDOA errors, the residuals of the TDOA equations are calculated to obtain weights. Finally, the orthogonal projection is applied to separate the additive variables and solve the weighted linear equations for source coordinates. The pencil–lead breaks experiments are carried out to verify the effectiveness and accuracy of the proposed method. The results show that the novel method holds better accuracy and wave velocity inversion than the pre-existing methods. The average positioning error of the novel method is only 3.49 mm, while the errors of the traditional methods are 4.62 mm, 5.08 mm, 6.57 mm, and 11.93 mm, respectively. The positioning performance of the proposed method under different TDOA errors is further proved by simulation tests. The simulation results demonstrate that the positioning performance of the proposed method is better than that of the existing methods under different errors. Moreover, the blast vibration experiment validates that the proposed method holds the location accuracy of 16.65 m, which presents good prospects in engineering applications. Highlights No prior measurement of wave speeds and selection of reference sensors reduce the effect of velocity and arrival errors. Analytical solution being given by linearizing the control equation, complex computations and local convergence are circumvented. Estimating the noise term in the arrival time difference, the tolerance to TDOA errors is improved. Redundant time-difference-of-arrival measurements are fully used to provide new constraints. Blasting experiments reveal that positioning accuracy of the method is 72% more accurate than the traditional method. [ABSTRACT FROM AUTHOR]

Monitoring sensors in complex engineering environments often record abnormal data, leading to significant positioning errors. To reduce the influence of abnormal arrival times, we introduce an innovative, outlier-robust localization method that integrates kernel density estimation (KDE) with damping linear correction to enhance the precision of microseismic/acoustic emission (MS/AE) source positioning. Our approach systematically addresses abnormal arrival times through a three-step process: initial location by 4-arrival combinations, elimination of outliers based on three-dimensional KDE, and refinement using a linear correction with an adaptive damping factor. We validate our method through lead-breaking experiments, demonstrating over a 23% improvement in positioning accuracy with a maximum error of 9.12 mm (relative error of 15.80%)—outperforming 4 existing methods. Simulations under various system errors, outlier scales, and ratios substantiate our method’s superior performance. Field blasting experiments also confirm the practical applicability, with an average positioning error of 11.71 m (relative error of 7.59%), compared to 23.56, 66.09, 16.95, and 28.52 m for other methods. This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies. It also provides a practical solution for real-world engineering and safety monitoring applications.

Distributed acoustic sensing (DAS) using fiber-optic cables has potential for landslide and rockfall monitoring along linear infrastructure but faces challenges for accurate seismic source localization due to signal nonuniformity and attenuation during propagation. This limits the applicability of traditional seismic location methods with DAS. We present a novel computer vision–based approach to overcome these limitations. Field experiments simulating landslide quakes and rockfall impacts were conducted near dedicated DAS arrays to validate the method. Results demonstrate the computer vision technique outperforms short-to-long-term average ratio and cross-correlation algorithms in both location accuracy and constraint of seismic sources, with locations also agreeing well with a colocated nodal seismic array. Key factors influencing performance include the type of signal processing used on the DAS data and cable array geometry. The envelope function best handled noise while L-shape and parallel dual-cable geometries proved most effective. Overall, this computer vision method provides an improved solution for seismic source location of landslides and rockfalls monitored by DAS networks, enhancing safety along vulnerable linear infrastructure like transportation corridors through mountainous terrain. [ABSTRACT FROM AUTHOR]

Purpose: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. Methods: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. Results: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

The failure of buried pipelines can lead to serious consequences such as explosions, environmental pollution, settlement, as well as economic loss. To prevent these outcomes, it is crucial to identify the causes of failure and monitor their signs. One of the main causes of failure is unexpected third-party interference (TPI), which is particularly challenging to detect. Regarding to this issue, this study proposes a new algorithm for monitoring impact damage, which can be used for prompt response and damage prevention. The algorithm is integrated into the system using two approaches. The first approach focuses on detecting the location of the damage (referred to as source location). A kurtosis-based transfer function was newly proposed to selecting the optimal frequency band for time-difference-of-arrival based source location, resulting in accurate pinpointing of damage, even in a noisy environment. The second approach is used to determine whether impact damage has actually occurred by observing newly suggested features in both the time and frequency domains (referred to as anomaly detection). These features evaluate the presence of damage and the similarity between signals. As a result, it was evaluated that the field applicability was higher than that of conventional methods, and the superiority of the proposed method was also verified through field experiments. The method proposed in this study is expected to enable immediate response when the integrity of the buried pipelines is on the line of failure due to TPI. [ABSTRACT FROM AUTHOR]

Acoustic emission is widely used in engineering structural health monitoring, where damage location based on acoustic emission have great advantages. Currently, most of the acoustic emission location work requires extensive experimental studies. In this study, acoustic emission source location model was proposed based on BP neural network and acoustic emission wave propagation simulation, thus reduced the experiments required for acoustic emission localisation and improved the location accuracy. Initially, finite element simulation was conducted to study the propagation of acoustic emission waves in composite tubes. The calculated findings showed a high level of agreement with the experimental data. Additionally, the first arrival time of the signal was extracted according to the Akaike information criterion, and the neural network was trained and tested using the simulated data, while the trained network model was validated using the experimental data. The results show that the finite element method is a reliable alternative test method. More than 94% of the sound source location results have absolute location error within ± 3 mm, and the all points is less than ± 5 mm. The deviation of the location results showed significant improvement compared to the traditional time difference of arrival method, verifying the feasibility of the method. [ABSTRACT FROM AUTHOR]

In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor's ability to capture an acoustic emission signal is demonstrated using a Hsu–Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors' knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor's low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor's ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity. [ABSTRACT FROM AUTHOR]

Accurate evaluation of the impact range of hydraulic fracturing (HF) in coal seams is the key to optimizing HF design schemes. In this study, multistage HF processes in Well No. 1 of the Xinji No. 2 coal mine were monitored and analyzed using ground microseismic (MS) monitoring technology. An improved particle swarm optimization algorithm and waveform similarity superposition method were used to locate HF-induced MS events with different signal-to-noise ratios. The results showed that the seismic source location points were distributed horizontally within a 120 m range on both sides of the fracturing well, and vertically distributed from − 615 to − 675 m. The MS events envelope method was used to calculate the stimulated reservoir volume (SRV) of nine fracturing sections, totaling approximately 1,977,000 m3, with the largest SRV in Sections Nos. 2 and 6. Through continuous fracture network (CFN) modeling, the overall fracture extension of the HF was revealed to be rectangular and evenly distributed on both sides of the fracturing well. The main direction of the fracture extended outward along the vertical direction, in line with the direction of the injection hole. By calculating the CFN branch index (BI) and CFN length, the results showed that the fracture networks of Sections Nos. 1, 4, and 6 were more complex, and the CFN lengths of Sections Nos. 1, 2, 6, and 9 were longer. Combining the fracture network size, MS event parameters, and SRV, we determined that Sections Nos. 2 and 6 have longer fracture networks, larger HF ranges, and the best HF effect. This study is important for the evaluation of the effective range of HF in coal seams and for the design and parameter optimization of HF schemes. Highlights: Microseismic events with different signal-to-noise ratios were accurately located. The stimulated reservoir volume and the number of microseismic events in the fractured area are positively correlated. The fracture extension pattern of hydraulic fracturing was obtained by continuous fracture network modeling. [ABSTRACT FROM AUTHOR]

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Acoustic emission (AE) and digital image correlation (DIC) technology were used in this study to monitor the behavior of reinforced concrete pipe (RCP) in the optimized three-edge bearing test (TEBT). At the same time, the AE source location of different pipe sections was simulated. The results show that the whole compressive loading process for RCP could be divided into 3 to 4 stages. Both the AE counts and AE energy could well characterize the cracking of concrete, and AE energy was more sensitive to the breaking of steel bar. The amplitude and duration of AE signal produced by the breaking of steel bar were larger than those produced by the cracking of concrete. The AE frequency for concrete cracking was relatively scattered. The AE frequency for steel bar breaking was small and concentrated, and this frequency distribution showed an exponential decreasing trend. The results of optimized AE source location showed that the generation and development of cracks occur in the initial loading stage. The results of multi-index analysis showed that the cracks in the top (loading position) and the bottom (bearing position) of the RCP show that the cracks extend from the inner wall to the outer wall, while the cracks on the left and right sides perpendicular to the loading direction show that the cracks extend from the outer wall to the inner wall. [ABSTRACT FROM AUTHOR]

In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor’s ability to capture an acoustic emission signal is demonstrated using a Hsu–Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors’ knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor’s low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor’s ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity.

Legnica-Głogów Copper District (LGCD) is one of the most active seismic regions in Europe. Several thousand seismic events induced by underground copper mines are recorded there each year, with the strongest reaching magnitudes above 4. Seismicity in LGCD is monitored by the LUMINEOS surface seismic network and the mine's underground networks. While the horizontal location uncertainty of the LUMINEOS network is about 300–600 m, the declared epicenter uncertainty of dense mine networks is less than 50 m. It gives us a unique opportunity to test the location accuracy of seismic events recorded by the LUMINEOS network determined with various algorithms and automatic procedure. In our work, we compared the location accuracies of traveltime-based algorithms LocSAT and NonLinLoc as well as waveform-based algorithm BackTrackBB. The iterative, Geiger-type LocSAT algorithm is used in routine daily processing for the LUMINEOS. Its downside is the need to use the S wave onset times, which in the case of the LUMINEOS network are very uncertain. As an alternative, we tested the probabilistic NonLinLoc algorithm, and the waveform based BackTrackBB algorithm. The BackTrackBB algorithm is known to work well with local, high noise seismic networks. We aimed to find out if it could be used routinely with the LUMINEOS data. In addition, we conducted a comparative analysis of the location uncertainty of these algorithms to determine the effectiveness of this parameter in assessing accuracy. [ABSTRACT FROM AUTHOR]

Acoustic emission/microseismic (AE/MS) source location plays a vital role in the safety monitoring of circular hole-contained structures (e.g., tunnels, pipelines, and pressure vessels). However, traditional methods may obtain a bad P-wave travel path and poor source location accuracy. To handle this, an analytical solution-based shortest P-wave travel path and a grid search-based AE/MS event source location method are proposed for circular hole-contained structures. Moreover, the objective function of AE/MS source location considers the P-wave arrival time system error. Both synthetic tests and pencil-lead break (PLB) application tests in 2D and 3D circular hole-contained structures show the outperformance of the proposed location methods. First, the analytical method obtains an equal or smaller P-wave travel distance than that of the Dijkstra and A* algorithms, which means the analytical method generates better theoretical data. Then, the average location errors of PLB events for the 2D circle-contained rectangle, 3D full circular hole-contained cuboid, and 3D part circular hole-contained cuboid are 0.57 cm, 1.87 cm, and 2.25 cm, respectively. These location results are better than results based on the shortest P-wave paths of the straight line, Dijkstra, and A* algorithms. Moreover, the proposed method solves that former analytical solutions do not consider the thickness of cylindrical shell structures. Highlights: An analytical solution-based shortest P-wave travel path has been proposed for circular hole-contained structures. The acoustic emission/microseismic source location objective function considers the P-wave arrival time system error. The average location errors of pencil-lead break events for circular hole-contained structures are within 2.25 cm. The analytical solution-based location method obtains better location results than methods based on the straight line, Dijkstra and A* algorithms. [ABSTRACT FROM AUTHOR]

Objective: To explore the association between high‐frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. Methods: Magnetoencephalography (MEG) ripples of 63 drug‐resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source‐level betweenness centrality. Results: Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). Conclusion: This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source‐level functional connectivity is a promising approach for presurgical epilepsy evaluation. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology is a non-destructive testing (NDT) technique that is able to monitor the process of hydrogen-induced cracking (HIC). AE uses piezoelectric sensors to convert the elastic waves generated from the growth of HIC into electric signals. Most piezoelectric sensors have resonance and thus are effective for a certain frequency range, and they will fundamentally affect the monitoring results. In this study, two commonly used AE sensors (Nano30 and VS150-RIC) were used for monitoring HIC processes using the electrochemical hydrogen-charging method under laboratory conditions. Obtained signals were analyzed and compared on three aspects, i.e., in signal acquisition, signal discrimination, and source location to demonstrate the influences of the two types of AE sensors. A basic reference for the selection of sensors for HIC monitoring is provided according to different test purposes and monitoring environments. Results show that signal characteristics from different mechanisms can be identified more clearly by Nano30, which is conducive to signal classification. VS150-RIC can identify HIC signals better and provide source locations more accurately. It can also acquire low-energy signals better, which is more suitable for monitoring over a long distance. [ABSTRACT FROM AUTHOR]

Buried pipelines, such as waterworks pipelines, are critical for transmitting essential resources and energy in modern cities, but the risk of pipeline failure, especially due to third-party interference, is a major concern. While various studies have focused on leak detection in waterworks pipelines, research on preventing impact damage is limited. To address this issue, this study proposes a novel algorithm that utilizes energy and similarity measurements for impact detection and compares it theoretically to existing leak-detection methods. The proposed algorithm utilizes frequency-domain kurtosis to determine the frequency band on which the energy of the impact signals is concentrated, along with a time-shift coherence function to measure the similarity of the signals. The application of the source location using the filtered signals enables accurate detection of the location of third-party interference. The proposed algorithm aims to ensure the safety and to prevent failures of buried pipelines. To verify the feasibility of the proposed algorithm, an excavation experiment using a backhoe was conducted on an in-service waterworks pipeline with a diameter of 2200 mm and a burial depth of 3 m. This experiment confirmed the effectiveness of the proposed algorithm in preventing failures of buried pipelines and demonstrated its practical applicability in the field. The experiment also validated the algorithm's ability to detect third-party interference damage at various points. [ABSTRACT FROM AUTHOR]

Infrasound (<20 Hz) can propagate thousands of kilometers through the atmosphere, enabling global source location using networks of arrays such as the International Monitoring System infrasound network. However, atmospheric spatiotemporal variability poses a major challenge to locating infrasound sources. Strong horizontal cross‐winds deviate the observed infrasound arrival azimuths, producing source mislocations that can be on the order of hundreds of kilometers. We introduce a method that combines empirical climatologies (HWM14/NRLMSIS2.0) and 3D ray tracing (infraGA) to obtain first‐order, robust, and rapid estimates of the backazimuth deviations for source location procedures. For each trial source node and receiver path, day of the year and time, we perform an automatic iterative search for infrasound ground intercepts around the target station and obtain the corresponding backazimuth deviation. We test the method using IMS infrasound data from stations up to ∼5,000 km range for two similar explosive eruption case studies: 2011 Puyehue‐Cordón Caulle and 2015 Calbuco. We obtain a source mislocation reduction up to ∼84% (242–38.7 km) and ∼75% (366–93.1 km) for Puyehue‐Cordón Caulle and Calbuco eruptions, respectively. To evaluate the approach, we repeat the procedure using more realistic hybrid atmospheric descriptions; we obtain comparable results (up to ∼75% mislocation reduction for both eruptions). Potential applications of the approach include long‐range volcano monitoring in near‐real time by using pre‐computed look‐up tables or large‐scale, multi‐year reanalyses of infrasound waveform archives. Plain Language Summary: Acoustic waves below the human audible frequency range (20 Hz) are called infrasound. Favorable stratospheric wind conditions allow explosive volcanic infrasound to propagate hundreds to thousands of kilometers. The International Monitoring System (IMS) infrasound network can be used to detect and locate volcanic infrasound sources globally, but the atmospheric winds introduce deviations in the location of the sources that can be up to hundreds of kilometers from true. We introduce a method to generate rapid first‐order azimuth corrections to tackle this issue, and test it with data from 2011 Puyehue‐Cordón Caulle and 2015 Calbuco eruptions, Chile. We use infrasound detections from the nearest eight IMS stations to both volcanoes (∼1,500 to ∼5,000 km), obtaining significant improvements of the source locations (up to ∼84% mislocation reduction). Upon further testing, this method could be used to improve long‐range volcano monitoring in near‐real time or large‐scale, multi‐year reanalyses of infrasound waveform archives. Key Points: We develop a rapid first‐order method to correct the infrasound propagation backazimuth deviation for source location proceduresWe evaluate the method using long‐range International Monitoring System data (∼700–5,000 km) from the eruptions of Puyehue‐Cordón Caulle (2011) and Calbuco (2015)We achieve a considerable source mislocation reduction for both eruptions (∼75%–85%) [ABSTRACT FROM AUTHOR]

Source location is a constant issue of importance of both theoretical study and practical engineering. Many pioneers have put out the corresponding solutions for near- or far-field signals, and preferred contributions are suggested. To our best knowledge, there are currently few focused approaches to the complicated situation where both near- and far-field signals exist with an unknown number of sources. Additionally, the robustness of the method must be taken into account when the additive background noise does not follow Gaussian or super-Gaussian distribution. To solve these problems, a novel method based on phased fractional lower-order moment (PFLOM) is proposed to simultaneously better preserve the signal and suppress the noise. Secondly, the whole procedure of the method containing direction of arrival (DOA) estimation, range estimation, separation of near-and far-field sources, and crucial parameter settings are studied in detail. Finally, comprehensive Monte Carlo experiments are carried out in the simulation to demonstrate the superiority of the proposed method compared to the existing competitive methods. Due to the novel method's effectiveness with an unknown number of sources and robustness against various noises, it is believed that it could be fully utilized in more fields. [ABSTRACT FROM AUTHOR]

The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation's abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time. [ABSTRACT FROM AUTHOR]

The structure of a high-performance engine is becoming more and more complex, so it is very important to accurately and quickly locate the faults to ensure its operation safety. The acoustic emission (AE) signal which is caused by the structural damage of engines contains important structural integrity information; however, the accuracy of existing AE location methods is affected by complex structures, such as stiffeners, holes, variable wall thickness, and interface coupling. Therefore, this paper proposes a new AE source location method that combines the two-step Akaike information criterion (AIC) based on the dispersion curve and the time difference matrix (TDM). This method can precisely locate the faults of complex structures without considering the wave velocity. Through theoretical calculation and numerical simulation, this paper intends to show that the proposed method is superior to the traditional AIC and can track the arrival time of the AE signal more accurately. In addition, experiments on different structures illustrate that the proposed method has higher location accuracy in complex structures. This paper also analyzes the location sensitivity to the number and array of sensors in a complex structure and puts forward an optimal scheme of sensor layout on the condition of high location accuracy. The results show that the proposed method can be used as a reliable tool for AE source location and fault monitoring of complex structures. It will have a wide application prospect in the engine and other fields. [ABSTRACT FROM AUTHOR]

Displacement ventilation (DV) is considered an energy saving air conditioning system and is especially recommended for classrooms for providing cool clean air in the occupied zone. However, in the current pandemic situation, ventilation systems should be assessed in terms of infection‐spread prevention as well. Hence, this study aims at assessing the efficiency of displacement ventilation in minimizing infection spread in lecture halls through investigating the effect of source position on the contaminant concentration distribution. The settings of a mid‐size DV lecture hall in Osaka University, Japan was chosen as a case‐study. Ten single‐contaminant‐source cases were simulated using CFD software. The cases were analyzed in terms of temperature distribution, contaminant diffusion pattern and vertical distribution, and breathing air quality using indices, such as local air quality index and scale of ventilation efficiency. The results demonstrated a considerable variation between cases in terms of contaminant distribution pattern, number of occupants affected, average inhaled‐air quality, and diffusion radius. Accordingly, given the ventilation system design, best and worst case‐scenarios were pointed out and factors affecting the variation were highlighted and further investigated to conclude the impact of each factor. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) method enables real-time monitoring of damage initiation and progression. Recently, AE analysis using machine learning has become widely popular; however, the AE source location is often located manually to ensure reliability and accuracy. Therefore, it is desirable that the detection of AE source location is fully automated with a high accuracy. This study proposes a novel arrival time identification method for AE source location that can accurately and automatically locate AE sources. First, a wavelet transform is applied to an AE signal to extract the wavelet coefficient of a specific frequency. Subsequently, the Akaike information criterion is applied to the time transient of wavelet coefficient to identify the initial wave arrival time. The localized AE source accuracy is compared with conventional arrival time identification methods: visual initial wave arrival time identification, visual S0 mode arrival time identification at the time transient of the wavelet coefficients, automatic peak identification at the time transient of the wavelet coefficients, and automatic AIC wave arrival time identification. The proposed method reduces the number of events with incorrect initial arrival detections. Moreover, the correct detection rate increases by 1.5 times compared to a normal AIC. In addition, the proposed method is approximately 30 times faster than the conventional visual method and is an excellent AE wave arrival time identification in terms of both accuracy and speed of analysis. Lastly, we verify that the proposed method can be effectively applied to anisotropic materials. [ABSTRACT FROM AUTHOR]

To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p <0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy. • High frequency oscillations enhance precision in identifying epileptogenic zone. • Long-range temporal correlations across bands aid in localizing epileptogenic zone. • Combining the above two points is more effective than using them separately. [ABSTRACT FROM AUTHOR]

Abstract Displacement ventilation (DV) is considered an energy saving air conditioning system and is especially recommended for classrooms for providing cool clean air in the occupied zone. However, in the current pandemic situation, ventilation systems should be assessed in terms of infection‐spread prevention as well. Hence, this study aims at assessing the efficiency of displacement ventilation in minimizing infection spread in lecture halls through investigating the effect of source position on the contaminant concentration distribution. The settings of a mid‐size DV lecture hall in Osaka University, Japan was chosen as a case‐study. Ten single‐contaminant‐source cases were simulated using CFD software. The cases were analyzed in terms of temperature distribution, contaminant diffusion pattern and vertical distribution, and breathing air quality using indices, such as local air quality index and scale of ventilation efficiency. The results demonstrated a considerable variation between cases in terms of contaminant distribution pattern, number of occupants affected, average inhaled‐air quality, and diffusion radius. Accordingly, given the ventilation system design, best and worst case‐scenarios were pointed out and factors affecting the variation were highlighted and further investigated to conclude the impact of each factor.

Locating cracks in a solid object using acoustic emission (AE) is useful both for detecting defects during safety monitoring and for basic laboratory studies of fractures. We developed an acoustic source location (ASL) method without the use of velocity information with AE in anisotropics plates, such as carbon fiber-reinforced polymers. Assuming that the propagation velocity of an unknown elastic wave is constant in anisotropic materials, the objective function to be minimized is defined based on the elliptic wavefront shape-based technique. The objective function is minimized using an iterative method, such as the gradient descent method. As a result of the numerical experiments and PLB testing on a carbon fiber-reinforced polymer plate, the method is accurate within 5% and is stable against noise. [ABSTRACT FROM AUTHOR]

Point cloud of sources in the form of acoustic emission (AE) or seismicity at both field‐ and laboratory‐scales are indicative of causative structures. The error ellipsoid clustering (EC) method, whose underlying premise is reducing the amount of discarded data, is helpful for seeking the causative structure(s) from field‐scale seismic sources. However, a proper EC method does not exist for laboratory sources. Interpretation of laboratory AE sources usually involves systematic discarding of the assumed "low‐quality" data. We seek the successful transfer of the field‐scale EC method into the investigation of laboratory‐scale sources. For that, a new semi‐empirical EC method (serendipity method) is devised, and we have systematically modified the previous EC method (standard method) in (1) error ellipsoid formation, (2) iterative clustering algorithm, and (3) clustering stopping criterion. In the literature, the standard method and its limitations were validated using synthetic data. We validate the modifications by comparing its post‐clustering results with the original source locations and with the in‐situ and post‐mortem inspections of the laboratory rock fractures; and similarly for the serendipity method. We apply the serendipity method to a laboratory hydraulic fracturing test. The serendipity method implies the existence of a 3D damage zone as the second‐order feature caused by the hydraulic fracturing in addition to the primary hydraulic fracture. This complex damage zone provides a logical explanation to the previously observed reverse permeability‐distance relationship for hydraulic fracture. Plain Language Summary: Point cloud source locations of acoustic emission (AE) or seismicity at both field‐ and laboratory‐scales are indicative of their underlying causative structures. In efforts to reduce the amount of data discarded throughout the analysis of point cloud source data, the source location errors have been used to modify/re‐locate events based on their probable locations, therefore helping to identify first and second order structural features of the subsurface. Here, we transfer a field‐scale method for error ellipsoid based source relocation to the laboratory‐scale, and in doing so we identify modifications that must be implemented based on our validation exercises. The error ellipsoids and the statistical collapsing of source locations has successfully identified both large scale fracture structure as well as secondary fracture features that would otherwise not be recognized with acoustic emission or seismicity data. Key Points: Novel semi‐empirical error ellipsoid clustering method for identifying second‐order structure for laboratory AE source locationMethod implies 3D path of circling damage zone in a laboratory hydraulic fracture test as the second‐order featureThe counter‐intuitive reverse permeability‐AE density relationship can be interpreted via the identified first‐ and second‐order structures [ABSTRACT FROM AUTHOR]

Microseismic source location is the core of microseismic monitoring technology in coal mining; it is also the advantage of microseismic monitoring technology compared with other monitoring methods. The source location method directly determines the accuracy and stability of the source location results. Based on the problem of non-benign arrays of microseismic monitoring sensors in the coal mining process, a fast location method of microseismic source in coal mining based on the NM-PSO algorithm is proposed. The core idea of the NM-PSO algorithm is to use the particle swarm optimization (PSO) algorithm for global optimization, reduce the size of the solution space and provide the optimized initial value for the Nelder Mead simplex algorithm (NM), and then use the fast iteration characteristics of the NM algorithm to accelerate the convergence of the model. The NM-PSO algorithm is analyzed by an example and verified by the microseismic source location engineering. The NM-PSO algorithm has a significant improvement in the source location accuracy. The average location errors in all directions are (5.65 m, 5.01 m, and 7.21 m), all Within the acceptable range, and they showed good universality and stability. The proposed NM-PSO algorithm can provide a general fast seismic source localization method for different sensor array deployment methods, which significantly improves the stability and result in the accuracy of the seismic source localization algorithm and has good application value; this method can provide new ideas for research in microseismic localization in coal mining. [ABSTRACT FROM AUTHOR]

The hypocenters of microseismic events induced by hydraulic fracturing are conventionally located with an initial model established from well logs or perforation shots. In most geological settings, the arrival times are insufficiently explained without accounting for the velocity changes introduced by the reservoir stimulation process. The model parameters and source locations should be inverted simultaneously with arrival time information. Therefore, the joint inversion of event locations and velocity model requires the information of anisotropy parameters, which leads to the problem of the selection of degree of symmetry of anisotropic media in the inversion process. Since it is not possible to retrieve all elastic moduli from limited passive seismic data, the joint inversion is constrained to layered vertical transversely isotropic (VTI) media. Various methods have been proposed to invert the velocity model and source locations from the arrival times in anisotropic media, but the number of retrievable parameters in different parametrization types and acquisition scenarios have not been decisively discussed. We analyze the sensitivities for event locations and anisotropic parameters by the singular value decomposition (SVD) of the Fréchet derivatives in a layered anisotropic medium with vertical axis of symmetry. The singular values and eigenvectors obtained from SVD can be used to predict which unknown parameters are better constrained by the available traveltimes. The comparison of different parametrizations and monitoring array configurations allows to design a better inversion strategy to provide microseismic event locations and anisotropic parameters.

Location of earthquakes is a primary task in seismology and microseismic monitoring, essential for almost any further analysis. Earthquake hypocenters can be determined by the inversion of arrival times of seismic waves observed at seismic stations, which is a non-linear inverse problem. Growing amounts of seismic data and real-time processing requirements imply the use of robust machine learning applications for characterization of seismicity. Convolutional neural networks have been proposed for hypocenter determination assuming training on previously processed seismic event catalogs. We propose an alternative machine learning approach, which does not require any pre-existing observations, except a velocity model. This is particularly important for microseismic monitoring when labeled seismic events are not available due to lack of seismicity before monitoring commenced (e.g., induced seismicity). The proposed algorithm is based on a feed-forward neural network trained on synthetic arrival times. Once trained, the neural network can be deployed for fast location of seismic events using observed P-wave (or S-wave) arrival times. We benchmark the neural network method against the conventional location technique and show that the new approach provides the same or better location accuracy. We study the sensitivity of the proposed method to the training dataset, noise in the arrival times of the detected events, and the size of the monitoring network. Finally, we apply the method to real microseismic monitoring data and show that it is able to deal with missing arrival times in efficient way with the help of fine tuning and early stopping. This is achieved by re-training the neural network for each individual set of picked arrivals. To reduce the training time we used previously determined weights and fine tune them. This allows us to obtain hypocenter locations in near real-time.

As a 3D real-time monitoring method, microseismic (MS) monitoring technique has been widely used in various underground engineering applications. However, in such applications, it is still challenging to acquire precise and efficient MS locations. Here, we examined the applicability and accuracy of a fully convolutional neural network for source localization, where the modified loss function was utilized. The Shuangjiangkou underground powerhouse in southwestern China served as the engineering background. The dataset was made of the MS events that occurred near the main powerhouse from September 2018 to December 2019. A fully convolutional neural network, named MS-location Net, was then built. The original waveform data were directly used as the input of the neural network, while 3D Gaussian distribution functions of the monitoring area were used as the output of the neural network. The epicenter error, focal depth error and absolute error were applied as indicators to evaluate the model. The results show that all the three indicators, namely the epicenter error, focal depth error and absolute error, were less than 5 m for all the MS events in the test set. The average time for locating an MS event was 0.01435 s using a usual computer configuration, which greatly improves the positioning efficiency. The proposed location method in this paper overcomes the shortcomings of the traditional localization methods, e.g., the inaccuracy of velocity model and arrival picking. Highlights: A fully convolutional neural network, named MS-location Net, was built for microseismic source localization. The applicability and efficiency of the proposed location method were validated via a case study. The proposed location method overcomes the shortcomings of the traditional localization methods, e.g., the inaccuracy of velocity model and arrival picking. [ABSTRACT FROM AUTHOR]

Source location is a fundamental problem in acoustic emission (AE) monitoring. It usually requires configuring two pieces of prior information: wave velocity structure in the medium and arrival-time picking. The primary objective of this study was to propose an approach that would not require prior information to locate the AE source in rocks in which prior information is difficult to obtain. Pencil-lead break (PLB) tests were implemented on a granite cube to collect AE full waveforms and corresponding PLB coordinates. A deep learning model, an fully-connected network (FCN)-based asymmetric auto-encoder, was proposed to map the relationship between the raw AE full waveform and the PLB location. This location was represented by a spatial Gaussian probability distribution. The trained deep learning model was employed to predict the PLB locations using AE waveforms that were not involved in model training. The proposed model achieved the prediction accuracy for AE source of 0.65, whose location errors are much less than the conventional location approach with prior information configuration. Moreover, we implemented an occlusion experiment for demonstrating that the deep learning model automatically recognized the valuable waveform segment from the raw AE signal. Even though the proposed model's performance had room for improvement in terms of the quantity and diversity of training data, the results had verified that the model is successful in locating the AE source, especially regarding those mediums such as rocks in which it is difficult to obtain prior information. Highlights: An asymmetric autoencoder was built to achieve AE source locations on a granite cube using only the full waveform generated from PLB tests. The location accuracy for the proposed autoencoder was much higher than that from the method which requires prior information. An occlusion experiment was conducted to demonstrate that the autoencoder can recognize the valuable waveform segment in raw AE signals. [ABSTRACT FROM AUTHOR]

Finding a rumor source is a major issue in the analysis of social networks. In this problem, the rumor source is usually estimated from a given diffusion snapshot. How to estimate the rumor source accurately is a challenging problem. Usually, the rumor source location problem is regarded as a node ranking problem. However, most of the existing algorithms ignore the structure of the infected subgraph or the randomness of the rumor spread. Therefore, they have defects in applicability and accuracy. To solve this problem, this paper takes into account the above two aspects at the same time, and propose a new algorithm to locate the rumor source, which is called Back Propagation Source Location(BPSL). The proposed algorithm contains an estimation method which is based on the time-stamp back propagation. This method makes the proposed algorithm's accuracy outperform previous algorithms' accuracy. Moreover, the susceptible-infected model is used to simulate the information spread of the networks. The steps of the proposed algorithm can be stated as follows. First, a new method based on the influence maximization is proposed to determine the observer set, which can greatly reduce the number of observer nodes. Second, a new estimation method based on the time-stamp back propagation is proposed to locate the source, which makes the proposed algorithm more accuracy and doesn't change the structure of infected subgraph at the same time. Finally, the experimental results on two artificial networks and four real-world networks show the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Elevated exposure to airborne particulate matter is linked to deleterious health and well-being outcomes. Exposure assessment can be improved through enhanced understanding of source-receptor relationships, for example as expressed in the inhalation intake fraction metric. This study provides new knowledge about how inhalation intake of airborne particles varies with spatially varying indoor emissions. In a controlled environmental chamber with low background particle levels, we monitored the time- and size-resolved particle concentrations at multiple locations including the subject's breathing zone. We investigated two types of particle emissions: (i) controlled releases from several specific indoor locations; and (ii) natural release from skin and clothing for a range of simulated occupant activities. Findings show that particles released proximate to the human envelope caused a total inhalation intake fraction of 7–10 per thousand, which was 1.5–16 × higher than the intake fraction for other indoor release locations. These outcomes reflect the influence of emissions-receptor proximity combined with the efficient transport of particles by means of the thermal plume to the breathing zone. The results show that the well-mixed representation of an indoor environment could underestimate the inhalation intake by 40–90% for various localized indoor emissions, and by up to 3 × for particles emitted from the human envelope. The post-release exposure period contributed substantially to total inhalation intake. For particles released naturally from the human envelope, inhalation intake fractions varied with activity type and were higher for a subject when seated rather than walking.

Elevated exposure to airborne particulate matter is linked to deleterious health and well-being outcomes. Exposure assessment can be improved through enhanced understanding of source-receptor relationships, for example as expressed in the inhalation intake fraction metric. This study provides new knowledge about how inhalation intake of airborne particles varies with spatially varying indoor emissions. In a controlled environmental chamber with low background particle levels, we monitored the time- and size-resolved particle concentrations at multiple locations including the subject's breathing zone. We investigated two types of particle emissions: (i) controlled releases from several specific indoor locations; and (ii) natural release from skin and clothing for a range of simulated occupant activities. Findings show that particles released proximate to the human envelope caused a total inhalation intake fraction of 7–10 per thousand, which was 1.5–16 × higher than the intake fraction for other indoor release locations. These outcomes reflect the influence of emissions-receptor proximity combined with the efficient transport of particles by means of the thermal plume to the breathing zone. The results show that the well-mixed representation of an indoor environment could underestimate the inhalation intake by 40–90% for various localized indoor emissions, and by up to 3 × for particles emitted from the human envelope. The post-release exposure period contributed substantially to total inhalation intake. For particles released naturally from the human envelope, inhalation intake fractions varied with activity type and were higher for a subject when seated rather than walking.

Acoustic emission (AE) technology is a non-destructive testing (NDT) technique that is able to monitor the process of hydrogen-induced cracking (HIC). AE uses piezoelectric sensors to convert the elastic waves generated from the growth of HIC into electric signals. Most piezoelectric sensors have resonance and thus are effective for a certain frequency range, and they will fundamentally affect the monitoring results. In this study, two commonly used AE sensors (Nano30 and VS150-RIC) were used for monitoring HIC processes using the electrochemical hydrogen-charging method under laboratory conditions. Obtained signals were analyzed and compared on three aspects, i.e., in signal acquisition, signal discrimination, and source location to demonstrate the influences of the two types of AE sensors. A basic reference for the selection of sensors for HIC monitoring is provided according to different test purposes and monitoring environments. Results show that signal characteristics from different mechanisms can be identified more clearly by Nano30, which is conducive to signal classification. VS150-RIC can identify HIC signals better and provide source locations more accurately. It can also acquire low-energy signals better, which is more suitable for monitoring over a long distance.

Buried pipelines, such as waterworks pipelines, are critical for transmitting essential resources and energy in modern cities, but the risk of pipeline failure, especially due to third-party interference, is a major concern. While various studies have focused on leak detection in waterworks pipelines, research on preventing impact damage is limited. To address this issue, this study proposes a novel algorithm that utilizes energy and similarity measurements for impact detection and compares it theoretically to existing leak-detection methods. The proposed algorithm utilizes frequency-domain kurtosis to determine the frequency band on which the energy of the impact signals is concentrated, along with a time-shift coherence function to measure the similarity of the signals. The application of the source location using the filtered signals enables accurate detection of the location of third-party interference. The proposed algorithm aims to ensure the safety and to prevent failures of buried pipelines. To verify the feasibility of the proposed algorithm, an excavation experiment using a backhoe was conducted on an in-service waterworks pipeline with a diameter of 2200 mm and a burial depth of 3 m. This experiment confirmed the effectiveness of the proposed algorithm in preventing failures of buried pipelines and demonstrated its practical applicability in the field. The experiment also validated the algorithm’s ability to detect third-party interference damage at various points.

Poor wind environment in residential areas leads to the accumulation of odor from domestic waste, affecting pedestrian health. A reasonable arrangement of waste collection points can reduce pedestrian exposure risks. This study aims to investigate the hydrogen sulfide (H2S) dispersion and residents' exposure risk at the pedestrian level for five different locations of waste collection points in a residential building array. Simulation results are consistent with the benchmark wind tunnel experiment, validating that the used turbulence model and numerical methods show good agreement with the predictions of the aforementioned problem. Results indicate that the dimensionless concentration of H2S and personal intake fraction in a residential area are lower when the collection point is at the corner of the building array periphery. When the collection point is located in the middle of the periphery of the building array or between two adjacent buildings in the center of the array, the local dimensionless concentration of H2S is 50 at the pedestrian level, and the personal intake fraction is three orders of magnitude higher than that at the corner of the building array periphery. The findings provide a reference for the layout of waste collection points in high-density residential areas and reduction in outdoor exposure risk. [ABSTRACT FROM AUTHOR]

We study the problem of identifying the source of a stochastic diffusion process spreading on a graph based on the arrival times of the diffusion at a few queried nodes. In a graph G = (V , E) , an unknown source node v ⁎ ∈ V is drawn uniformly at random, and unknown edge weights w (e) for e ∈ E , representing the propagation delays along the edges, are drawn independently from a Gaussian distribution of mean 1 and variance σ 2. An algorithm then attempts to identify v ⁎ by querying nodes q ∈ V and being told the length of the shortest path between q and v ⁎ in graph G weighted by w. We consider two settings: non-adaptive , in which all query nodes must be decided in advance, and adaptive , in which each query can depend on the results of the previous ones. Both settings are motivated by an application of the problem to epidemic processes (where the source is called patient zero), which we discuss in detail. We characterize the query complexity when G is an n -node path. In the non-adaptive setting, Θ (n σ 2) queries are needed for σ 2 ≤ 1 , and Θ (n) for σ 2 ≥ 1. In the adaptive setting, somewhat surprisingly, only Θ (log ⁡ log 1 / σ ⁡ n) are needed when σ 2 ≤ 1 / 2 , and Θ (log ⁡ log ⁡ n) + O σ (1) when σ 2 ≥ 1 / 2. This is the first mathematical study of source identification with time queries in a non-deterministic diffusion process. [ABSTRACT FROM AUTHOR]

One of the most significant benefits of Acoustic Emission (AE) testing over other Non-Destructive Evaluation (NDE) techniques lies in its damage location capability over a wide area. The delta-T mapping technique developed by researchers has been shown to enable AE source location to a high level of accuracy in complex structures. However, the time-consuming and laborious data training process of the delta-T mapping technique has prevented this technique from large-scale application on large complex structures. In order to solve this problem, a Finite Element (FE) method was applied to model training data for localization of experimental AE events on a complex plate. Firstly, the FE model was validated through demonstrating consistency between simulated data and the experimental data in the study of Hsu-Nielsen (H-N) sources on a simple plate. Then, the FE model with the same parameters was applied to a planar location problem on a complex plate. It has been demonstrated that FE generated delta-T mapping data can achieve a reasonable degree of source location accuracy with an average error of 3.88 mm whilst decreasing the time and effort required for manually collecting and processing the training data. [ABSTRACT FROM AUTHOR]