Local Method Research Articles

DMLoc: Automatic Microseismic Locating Workflow Based on Deep Learning and Waveform Migration

Abstract During hydraulic fracturing, real-time acquisition of the spatiotemporal distribution of microseismic in the reservoir is essential in evaluating the risk of induced seismicity and optimizing injection parameters. By integrating deep learning with migration-based location methods, we develop an automatic microseismic locating workflow (named DMLoc). DMLoc applies deep learning to automate phase picking and leverage the phase arrival probability function generated by a convolutional network as the input for waveform migration. The proposed workflow is first applied to the continuous data of the Dawson-Septimus area. Compared with a reference catalog generated by the SeisComP3 software, our method automatically locates 57 additional seismic events (accounting for 43% of the events in the obtained catalog). We further evaluate the performance of DMLoc by applying it to a 35-day continuous microseismic dataset from the Tony Creek Dual Microseismic Experiment. The spatiotemporal distribution of our detected events is consistent with results reported in prior catalogs, demonstrating the effectiveness of our method. In contrast to using raw microseismic records for stacking, DMLoc addresses the issue of inaccurate locating caused by low signal-to-noise ratios and polarity changes. The use of GPUs has substantially accelerated the calculations and enabled DMLoc to output locating results in minutes. This fast and efficient metric could be easily extended to any microseismic monitoring scenario that requires (near) real-time locations and assists in site-based risk mitigation and industrial operation optimization.

A Vision/Inertial Navigation/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual-inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual-inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Traveling-wave-based Fault Location using Time Delays between Modal Components in Electrical Distribution Systems

Background: The distribution feeders usually include many laterals, too many sensors need to be installed to locate the fault positions in electrical distribution systems by using the traditional double-ended traveling-wave-based fault-location methods Objective: Fault location based on the time delays between the moments of the first wavefronts of zero-mode voltages and that of aerial-mode voltages arriving at one end of the distribution feeder. Methods: Large decomposition level in wavelet transformation is adopted in this paper to obtain the zero-mode velocity. Results: Both theoretical research and simulation experiments have proved that the proposed fault location method can greatly improve the accuracy of the fault location in the electrical distribution systems without many sensors. Conclusion: The proposed fault location method can provide one feasible way to locate the faults in the electrical distribution systems.

Fourth-order exponential time differencing Runge–Kutta scheme and local meshless method to investigate unsteady diffusion–convection problems of anisotropic functionally graded materials

The current article is devoted to proposing a local meshfree method for simulating the unsteady diffusion–convection problems of anisotropic functionally graded materials (AFGM). The first stage of the proposed numerical algorithm is based on discretizing the spatial variables by utilizing the direct meshless local Petrov–Galerkin (DMLPG) method. Then a system of ODEs is extracted which is associated to the time variable. Furthermore, the fourth-order exponential time differencing Runge–Kutta method is applied to solve the system of ODEs. The main aim of this work is to introduce a flexible and low-cost numerical procedure to solve unsteady diffusion–convection problems of AFGM on the complicated geometry. The new numerical technique has admissible resultants to simulate the studied model. This numerical formulation is applied to several examples with complex domains to check its ability.

The Impact of COVID-19 on Consumer Perceptions of Local Food Market Channels

The COVID-19 pandemic had a profound impact on food systems. Despite disrupting conventional markets—such as grocery stores—farmers that focused on short supply chains found new market opportunities. As consumers increasingly became concerned with the safety of in-person shopping, some turned to purchasing directly from farmers and markets that carried products marketed as local, organic, and/or sustainable. With these changes in mind, I ask how consumer perceptions of local food systems (LFSs) changed in the two years after COVID-19. If consumers are more aware of local markets, farmers might reorient production to focus on local supply chains and sustainable production methods. To evaluate consumer perceptions, I use survey responses from residents in five communities in the US South in 2019 and 2022. Using a treatment effects model with propensity score matching, I show that residents became more aware of most local market channels, such as specialty retail stores and Community Supported Agriculture (CSA). Some residents were also more likely to have a positive perception of markets that provide fresh vegetables to low-income residents. Finally, residents wanted more investment into markets that make fresh, local products more available, such as community-owned groceries and CSA.

Error analysis for local discontinuous Galerkin semidiscretization of Richards’ equation

Abstract This paper concerns an error analysis of the space semidiscrete scheme for the Richards’ equation modeling flows in variably saturated porous media. This nonlinear parabolic partial differential equation can degenerate; namely, we consider the case where the time derivative term can vanish, i.e., the fast-diffusion type of degeneracy. We discretize the Richards’ equation by the local discontinuous Galerkin method, which provides high order accuracy and preserves stability. Due to the nonlinearity of the problem, special techniques for numerical analysis of the scheme are required. In particular, we combine two partial error bounds using continuous mathematical induction and derive a priori error estimates with respect to the spatial discretization parameter and the Hölder coefficient of the nonlinear temporal derivative. Finally, the theoretical results are supported by numerical experiments, including cases beyond the assumptions of the theoretical results.

Research on J wave detection based on transfer learning and VGG16

The J wave in the electrocardiogram indicates that the patient may suffer from malignant arrhythmia, ventricular tachycardia and other diseases, and the probability of sudden death is greatly increased. Due to the low incidence of J wave syndrome, J wave data in public databases are scarce, which hinders the research progress of J wave detection. Therefore, this study aims to automatically identify J wave signals, construct relevant datasets, and combine transfer learning with the pre-trained VGG16 model to detect J wave signals. Given the limited availability of J wave data, we utilized a local transformation method to simulate the generation of J wave signals. Subsequently, the simulated J wave signals underwent a continuous wavelet transform, converting them into grayscale images suitable for input into the pre-trained network. Finally, we fine-tuned the VGG16 network structure, which was initially trained on ImageNet, and modified the network for binary classification output for J wave data training. According to experimental results, this method achieves a 99.34 % accuracy rate, 99.35 % sensitivity, and 99.32 % specificity for the identification of J wave data. The aforementioned results outperform existing J wave research methodologies, effectively facilitating the identification of J wave signals.

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose.

Nonsimilar analysis of forced convection radially magnetized ternary hybrid nanofluid flow over a curved stretching surface

This study focuses on analyzing the rheological properties of the Carreau–Yasuda ternary hybrid nanofluid model on a curved stretched sheet considering the molybdenum disulfide ( Mo S 2 ) , graphene oxide ( GO ) , and Nichrome ( NiCr ) immersed in the base fluid ethylene glycol ( EG ) . This study employs a radial magnetic force that is directed perpendicular to the direction of flow to manipulate the motion characteristics of a nonNewtonian fluid. Augmenting the value of the magnetic number leads to a decrease in the velocity of the flow as resistance is introduced against its principal direction. Thermal radiation and viscous dissipation effects are considered in the energy equation. Assumptions that are fundamental to fluid flow theories are incorporated into the mathematical formulation of this problem. Following this, the governing equations are transformed into dimensionless nonsimilar partial differential equation (PDE) by applying nonsimilarity transformation to a nonsimilarity transformation, and subsequently analyzed as ordinary differential equations utilizing the local nonsimilarity method up to second level of truncation. By using the MATLAB-integrated bvp4c solver, numerical solutions are obtained. The results of these various solutions are examined using visual and tabular representations to thoroughly evaluate the variances across different parameter settings. Furthermore, the flow velocity is reduced by the Lorentz force caused by a more curved surface, and we gain insight into the impact of different physical quantities under different parametric considerations related to the stretching curved surface, which adds to our knowledge of this complex fluid dynamic phenomenon.

Simulating time-harmonic acoustic wave effects induced by periodic holes/inclusions on surfaces

This paper introduces a localized meshless method to analyze time-harmonic acoustic wave propagation on curved surfaces with periodic holes/inclusions. In particular, the generalized finite difference method is used as a localized meshless technique to discretize the surface gradient and Laplace-Beltrami operators defined extrinsically in the governing equations. An absorbing boundary condition is introduced to reduce reflections from boundaries and accurately simulate wave propagation on unclosed surfaces with periodic inclusions. Several benchmark examples demonstrate the efficiency and accuracy of the proposed method in simulating acoustic wave propagation on surfaces with diverse geometries, including complex shapes and periodic holes or inclusions.

Post-processing of a distributed source method for the localization of somatosensory cortex in a cohort of epilepsy patients

Localizing eloquent cortices is crucial for many neurosurgical applications, such as epilepsy and tumor resections. Clinicians may use non-invasive methods such as magnetoencephalography (MEG) to localize these cortical regions using equivalent current dipoles (ECDs). While dipoles are clinically validated, they provide the estimated strength, location, and orientation of only one or a few sources that best describe the recorded neuromagnetic data, requiring clinicians to make subjective decisions on the spatial extent of the underlying cortical area. More accurate delineation of eloquent cortical areas using distributed source localization methods would provide additional pre-surgical information on these regions’ location and spatial distribution, which could lead to reduced post-surgical complications associated with damage to or removal of eloquent cortices. Our objective in this paper was to present a method to post-process the distributed source localization results to yield a directly interpretable, distributed region of activation. As a test case, we selected somatosensory stimulation in a retrospective cohort of focal and multi-focal epilepsy patients. Our algorithm performs source localization using a distributed method (sLORETA), followed by post-processing and blind source separation to identify the area and boundary of the cortical tissue that primarily activates in response to somatosensory stimulation. We calculated the statistical significance of localization by comparing the identified region to an anatomical atlas and random chance. While examining patients who received left (upper left, UL) and right (upper right, UR) sided median nerve stimulation, the cortical areas identified by the algorithm were in anatomically appropriate areas with a median overlap of 97.6% and 94.7%, respectively. We observe that our algorithm localized somatosensory responses better than random chance in 57/58 (98%) patients who performed the UL task (p < 10 × 10−10, binomial test) and 49/50 (98%) patients who performed the UR task (p < 10 × 10−10, binomial test). We compared the localization of our algorithm to current clinical methods and found that our algorithm is not inferior to dipole localization. The algorithm can successfully localize somatosensory responses on the cortical surface in anatomically appropriate regions while providing the spatial extent of cortical activation, reducing subjectivity associated with dipole localization.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

Myomucosal composite graft: A simple technique for full-thickness vermilion defects of the lower lip.

Reconstruction of vermillion defects of the lower lip requires careful consideration of functional and aesthetic aspects. Traditionally, various local flap methods involving tissue advancement from the corner of the mouth, lateral chin, and medial cheek have been commonly employed to fill lower lip defects. However, these approaches have inherent limitations, which include technical complexity, disruption of the orbicularis oris muscle, lip tightening, microstomia, and visible scarring. To overcome these limitations, we employed a free myomucosal composite graft from the lower lip to reconstruct small to medium vermilion defects. Our technique is based on a simple and reproducible surgical approach that facilitates natural volume rearrangement of tissues. Moreover, this method enables precise inset and tension-free repair, prevents lip tightening, and offers excellent aesthetic outcomes with no vertical scarring and appropriate color matching with surrounding tissues.

A discrete Jaya algorithm for vehicle routing problems with uncertain demands

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete Jaya (DJaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in DJaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed DJaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD.

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 &amp; v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images.

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

Signal-based fault location method using time and frequency domain

This research introduces an innovative approach to fault detection in high-voltage alternating current (HVAC) systems by integrating temporal and frequency domain characteristics of signals. It utilizes Traveling Wave (TW) detection and analysis, focusing on a precise window of just 100 microseconds surrounding the TW's arrival at the measurement point. Mathematical Morphology is applied to identify time-domain properties, while the Stationary Wavelet Transform is employed to extract frequency-domain features. Additionally, the integration of a Dynamic Mode Decomposition-based technique enhances TW detection accuracy. The efficacy of this approach is extensively assessed for fault site classification and regression tasks, demonstrating superior performance compared to utilizing time or frequency-domain features separately. The study also examines the method's resilience to noisy measurements and the sufficiency of training data. Comparative analysis against existing signal-based approaches within the IEEE 34 node system underscores its heightened accuracy. Furthermore, the proposed technique showcases minimal fault location estimate errors along the feeder length and competes favorably with slower phasor-based fault detection methods in performance. Regarding the distance of the fault, its proximity to the generator, transmission line, or load significantly influences the parameters of positive, negative, and zero sequence components. When a fault occurs, these parameters undergo distinct changes, serving as indicators of the fault's severity and location. If the fault occurs near the generator or substation, there may be alterations in the voltage and current magnitudes, affecting the sequence components accordingly. In the case of a fault in the transmission line, impedance changes can lead to variations in sequence parameters. Similarly, a fault near the load can cause disturbances in the voltage and current profiles, impacting the sequence components. Analyzing these changes in positive, negative, and zero sequence parameters during a fault provides valuable insights into the fault's severity and location, aiding in effective fault detection and mitigation strategies. Key Words – fault detection, high-voltage alternating current (HVAC) systems, Traveling Wave (TW), temporal domain, frequency domain, Mathematical Morphology, Stationary Wavelet Transform, Dynamic Mode Decomposition, fault site classification,

An evaluation of variable selection methods using Southern Africa solar irradiation data

Dimensionality poses a challenge in developing quality predictive models. Often when modelling solar irradiance (SI), many covariates are considered. Training such data has several disadvantages. This study sought to identify the best variable embedded selection method for different location and time horizon combinations from Southern Africa solar irradiance data. It introduced new variable selection methods into solar irradiation studies, namely penalised quantile regression (PQR), regularised random forests (RRF), and quantile regression forest (QRF). Stability analysis, performance and accuracy metric evaluations were used to compare them with the common lasso, elastic and ridge regression methods. The QRF model performed best in all locations followed by the shrinkage methods on hourly data. However, it was found that QRF is not sensitive to associations through correlations, thereby ignoring the relevance of variables while focusing on importance. Among the shrinkage methods, the lasso performed best in only one location. On the 24-hour horizon, elastic net dominated the performances among the shrinkage methods, but QRF was best in three locations of the six considered. Results confirmed that variable selection methods performed differently on different situational data sets. Depending on the strengths of the methods, results were combined to identify the most paramount variables. Day, total rainfall, and wind direction were superfluous features in all situations. The study concluded that shrinkage methods are best in cases of extreme multicollinearity, while QRF is best on data sets with outliers or/and heavy tails.

Local instance-based transfer learning for reinforcement learning

Similarity-based transfer learning for reinforcement learning has garnered attention for its potential to enhance target task learning. However, it faces significant challenges in efficiency and effectiveness, primarily stemming from issues such as sparse reward, long trajectory, and strict similarity. To solve these problems, this paper proposes a local instance-based transfer learning method for reinforcement learning. Instead of relying on sparse reward and long trajectory, this approach leverages the Q value of the local trajectory to evaluate similarity, thereby significantly enhancing transfer efficiency. Furthermore, by relaxing the strictness of the similarity, three transfer policies are proposed to facilitate positive transfer. Extensive experimental results demonstrate that the effectiveness and efficiency of the proposed method in comparison with traditional similarity-based transfer learning methods.

Risk of deep venous thrombosis associated with peripherally inserted central catheter: A retrospective cohort study of 11.588 catheters in Brazil.

Deep Venous Thrombosis (DVT) due to Peripherally Inserted Central Catheter (PICC) is one of the most threatening complications after device insertion. To assess the rate of PICC-associated DVT and analyze the risk factors associated with this event in cancer and critically ill patients. We conducted a descriptive, retrospective cohort study with 11,588 PICCs from December 2014 to December 2019. Patients ≥ 18 years receiving a PICC were included. Pre-and post-puncture variables were collected and a logistic regression was used to identify the independent factors associated with the risk of DVT. The DVT prevalence was 1.8% (n = 213). The median length of PICC use was 15.3 days. The median age was 75 years (18; 107) and 52% were men, 53.5% were critically ill and 29.1% oncological patients. The most common indications for PICC's were intravenous antibiotics (79.1%). Notably, 91.5% of PICC showed a catheter-to-vein ratio of no more than 33%. The tip location method with intracavitary electrocardiogram was used in 43%. Most catheters (67.9%) were electively removed at the end of intravenous therapy. After adjusting for cancer profile ou chemotherapy, regression anaysis revealed that age (OR 1.011; 95% CI 1.002-1.020), previous DVT (OR 1.96; 95% CI 1.12-3.44) and obstruction of the device (OR 1.60; 95% CI 1.05-2.42) were independent factors associated with PICC-associated DVT, whereas the use of an anticoagulant regimen was a protective variable (OR 0.73; 95% CI 0.54-0.99). PICC is a safe and suitable intravenous device for medium and long-term therapy, with low rates of DVT even in a cohort of critically ill and cancer patients.