Local Method Research Articles

Precision localization method for fake news detection across multiple domains

Precision localization method for fake news detection across multiple domains

A CAM-UNet for pose determination of supercapacitor cell module assembly based on monocular vision

Purpose This paper aims to solve the problems of low stacking efficiency and long production time in the supercapacitor module assembly process, a stacking system based on monocular vision is proposed, including bracket visual positioning, grasping and stacking, and it is applied in actual production. Design/methodology/approach To enhance the robustness of the workpiece location method and improve the location accuracy, the improved U-Net network and image processing algorithms are used to segment the collected images. In addition, for the extracted feature points, the objective function that can be globally optimized is obtained by parameterizing the rotation matrix to construct a polynomial equation system and, finally, the equation system is solved to obtain the final pose estimation, which could improve the accuracy of workpiece location. Findings The result indicates that the proposed method is successfully performed on the manipulator. Besides, this method can well solve the problem of object reflection on the conveyor belt. The Intersection over Union of the image segmentation of the object is 0.9948, and the Pixel Accuracy is 0.9973, which has a high segmentation accuracy for the image. The error range between the method proposed in this paper and the pose estimation is within 2 mm, and the qualified rate of supercapacitor module stacking products is over 99.8%. Originality/value This paper proposes a method of accurately extracting feature points by integrating an improved U-Net network and image processing and uses the workpiece positioning algorithm of the optimal solution PnP problem algorithm. The calculation results show that the algorithm improves the positioning accuracy of the workpiece, realizes the assembly of stacked supercapacitor modules and is applied in industrial production.

A 2D-LiDAR-based localization method for indoor mobile robots using correlative scan matching

Abstract Precise pose estimation is crucial to various robots. In this paper, we present a localization method using correlative scan matching (CSM) technique for indoor mobile robots equipped with 2D-LiDAR to provide precise and fast pose estimation based on the common occupancy map. A pose tracking module and a global localization module are included in our method. On the one hand, the pose tracking module corrects accumulated odometry errors by CSM in the classical Bayesian filtering framework. A low-pass filter associating the predictive pose from odometer with the corrected pose by CSM is applied to improve precision and smoothness of the pose tracking module. On the other hand, our localization method can autonomously detect localization failures with several designed trigger criteria. Once a localization failure occurs, the global localization module can recover correct robot pose quickly by leveraging branch-and-bound method that can minimize the volume of CSM-evaluated candidates. Our localization method has been validated extensively in simulated, public dataset-based, and real environments. The experimental results reveal that the proposed method achieves high-precision, real-time pose estimation, and quick pose retrieve and outperforms other compared methods.

First-principles investigation of oxygen repulsion behavior on the Ir–Hf alloy surface

Enhancing the oxidation resistance of hot-end components under extreme conditions is a key focus in aerospace equipment research. We used first-principles simulations to investigate a series of IrxHf100−x (at. %) coatings. By employing density functional theory and the local density approximation method, we studied the surface oxygen repulsion energy and estimated the optimal composition ratio of IrxHf100−x for the strongest oxidation resistance. The results indicate that as the Hf content increases, the oxygen repulsion energy first rises and then falls, reaching a maximum value of 3.04 eV at x = 10 at. %, with active oxygen adsorption occurring when X exceeds 45 at.%. To understand the mechanism of the oxygen repulsion process, we analyzed the electronic characteristics between Ir, Hf, and O. O preferentially hybridizes its atomic orbitals with Ir, and the improvement in oxidation resistance of the Ir–Hf coating is attributed to the reduction in Ir atomic spacing due to Hf doping, which reduces O–Ir orbital hybridization.

Bio-filtration of Organic Pollutants: Simulation and Mathematical Modelling of Toluene and n-propanol

Volatile organic compounds (VOCs) are chemical substances that evaporate quickly, releasing harmful fumes into the atmosphere. Among the various VOCs, toluene and n-propanol are particularly interesting due to their widespread occurrence and considerable industrial impact. This study uses a bio-reactor with microbial biofilm formation to present the bio-degradation of a composition of toluene (methylbenzol) and n-propanol (1-propanol). It predicts the coupled inhibition effect of n-propanol on the toluene removal efficiency. The bio-filtration process is modelled as a dynamical system using a set of deterministic nonlinear partial differential equations. The differential equation with its appropriate conditions describes the bio-filtration processes occurring in two distinct phases: the biofilm phase and the gaseous phase. The differential equation is solved using the Chebyshev pseudospectral method and the local linearization method to understand the physical dynamics of the mathematical model. The effectiveness of the spectral-based method was evaluated by comparing its accuracy to known results in the limiting cases of the current dynamical model. A parametric and partial rank correlation coefficient (PRCC) analysis of the dimensionless parameters is conducted. It was found that toluene removal is enhanced by the additional desorption rate constant, ζ2. Increasing the air-biofilm coefficient for n-propanol supports the removal of n-propanol but not that of toluene. The findings of this study contribute to improvements in bio-degradation modelling and enhance our understanding of volatile organic compounds.

Damage localization using low-frequency bridge acceleration component under a moving vehicle

Monitoring the health of short-span and medium-span bridges in urban transportation systems is a crucially important issue in civil engineering, as is locating damage. Earlier studies have attempted to use bridge deflection or acceleration to locate damage. However, the deflection is not so sensitive to enable damage location. Few studies have investigated low-frequency acceleration responses. For this study, a proposed model-free method is evaluated for damage localization using low-frequency bridge acceleration under a moving vehicle. First, the bridge dynamic responses in a vehicle–bridge interaction (VBI) system are derived theoretically, where the bridge is modeled as a simply supported beam, the vehicle as a moving sprung mass, and damage as a reduction of the beam’s bending stiffness. A partially moving loading model is proposed to explain the influence of the structural damage on the dynamic behavior of the beam. A simple relation was found between low-frequency bridge acceleration and the damage location. Specifically, an abnormal “bump” occurred on the low-frequency bridge acceleration when a vehicle passes over the damaged area. Using this relation, one can directly locate potential damage from the low-pass filtered acceleration time history of the beam. A series of numerical analyses and laboratory and field experiments were conducted to validate the proposed damage localization method.

Accuracy and Precision of Earthquake Location Programs: Insights from a Synthetic Controlled Experiment

Abstract Earthquake location programs employ diverse approaches to address the challenges posed by incomplete knowledge and simplified representation of complex Earth structures. Assessing their reliability in location and uncertainty characterization remains challenging as benchmark datasets with known event locations are rare, and usually confined to particular sources, such as quarry blasts. This study evaluates eight earthquake location methods (GrowClust, HypoDD, Hypoinverse, HypoSVI, NonLinLoc, NonLinLoc_SSST, VELEST, and XCORLOC) through a controlled synthetic computational experiment on 1000 clustered earthquakes based on the setting of the 2019 Ridgecrest, California, earthquake sequence. We construct a travel-time dataset using the fast-marching method and a 3D velocity model extracted from the Community Velocity Model, supplemented with a von Karman perturbation to represent small-scale heterogeneity, and including elevation effects. Picking errors, phase availability, and outliers are introduced to mimic difficulties encountered in seismic network monitoring. We compare the location results from eight programs applied to the same travel-time dataset and 1D velocity structure against the ground-truth locations. For this aftershock sequence, our results reveal the superior accuracy and precision of differential time-based location methods compared to single-event location methods. The results validate the significance of compensating for deviations from assumed 1D velocity structure either by path or site correction modeling or by cancellation of unmodeled structure using differential arrival times. We also evaluate the uncertainty quantification of each program and find that most of the programs underestimate the errors.

Optimal local truncation error method on unfitted Cartesian meshes for solution of 3-D wave and heat equations for heterogeneous materials

In the paper we develop the optimal local truncation error method (OLTEM) with the non-diagonal and diagonal mass matrices on unfitted Cartesian meshes for the 3-D time-dependent wave and heat equations for heterogeneous materials with irregular interfaces. 27-point stencils that are similar to those for linear finite elements are used with OLTEM. There are no unknowns for OLTEM on interfaces between different materials; the structure of the global discrete equations is the same for homogeneous and heterogeneous materials. The calculation of the unknown stencil coefficients is based on the minimization of the local truncation error of the stencil equations, includes the use of the interface conditions and the entire PDE in derivations, and yields the optimal fourth order of accuracy of OLTEM with the non-diagonal mass matrix. This is two order higher than for linear finite elements with similar 27-point stencils. OLTEM with the diagonal mass matrix includes the rigorous calculation of the diagonal mass matrix and provides the optimal second order of accuracy. The 3-D numerical results for heterogeneous materials with irregular interfaces show that at the same number of degrees of freedom, OLTEM is even much more accurate than high-order (up to the 7-th order - the highest order in the commercial code COMSOL) finite elements with much wider stencils. Compared to linear finite elements with similar 27-point stencils, at an accuracy of 0.1% OLTEM decreases the number of degrees of freedom by 550–7300 times. This leads to a huge reduction in computation time.A new 3-D post-processing procedure has been developed with OLTEM for the calculation of the spatial derivatives of time-dependent numerical solutions. The spatial derivatives for each grid point are calculated with the help of one compact 27-point stencil (the same as that in basic computations). In contrast to known post-processing procedures, the new approach includes also the use of the time derivatives of the function, the interface conditions and the original PDE. The spatial derivatives of the OLTEM solutions calculated with the new post-processing procedure are much more accurate compared to those obtained by high-order (up to the 7-th order) finite elements with much wider stencils. At an accuracy of 0.1% for the spatial derivatives, OLTEM decreases the number of degrees of freedom by 1.9⋅106–6⋅109 times compared to linear finite elements. The new post-processing procedure can be equally applied to the calculation of the partial derivatives obtained by other numerical methods as well as to the numerical results for other PDEs.Due to the huge reduction in the computation time compared to existing methods and the use of trivial unfitted Cartesian meshes that are independent of irregular geometry, the proposed technique does not require remeshing for the shape change of irregular geometry and it will be effective for many design and optimization problems as well as for multiscale problems without the scale separation.

Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.

NetQDA: Local Network-Guided High-Dimensioanl Quadratic Discriminant Analysis

Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods.

Conceptual Model of Digitization of the Municipal Wastewater Disposal Systems

In the modern world, intelligent and digital wastewater disposal systems are increasingly in demand for real-time decision-making on the environmental efficiency of wastewater disposal. The aim of the study is to develop wastewater management processes for monitoring and predicting the parameters of sewerage networks. This paper presents the results of physical modeling of changes in the properties of aqueous solutions transported through the sewerage network to the treatment plant. It was found that the quality of wastewater without additional pollutants is stable, but under the influence of complex, disturbing factors, significant fluctuations in parameters are observed, requiring preventive control to prevent secondary pollution. In order to eliminate the disadvantages of prototypes and improve the environmental safety of wastewater disposal, a conceptual model of digitalization of the wastewater disposal system of water supply and sewerage facilities in the segment “Transport—wastewater treatment” based on the criteria of environmental efficiency of treatment facilities was justified and created. This model for regulating wastewater discharge parameters considers the quality indicators of domestic and industrial wastewater, which excite technological processes at municipal wastewater treatment plants and makes corrections through local treatment methods. This will reduce the risk of secondary pollution and increase management efficiency and environmental compliance of treatment facilities but, at the same time, requires significant investment, infrastructure modernization, qualified personnel and solutions to the issues of integrating processes into a single system. Also, a conceptual scheme of monitoring and forecasting sewerage network parameters and the sequence of sewerage system digitalization using the example of a settlement was created. Further research will be aimed at building a digital system of regulation of water supply and sewerage facilities in the segment based on the criterion of the ecological efficiency of treatment facilities with regard to disturbance.

Fault localization using sliding adaptive data segmentation and improved k-nearest neighbour with application to railway track circuits

This paper addresses the challenges of disorganized sampling data, high misjudgement rates in manual fault localization, lengthy analysis periods, and inefficiencies in jointless track circuits (JTCs), it introduces a novel fault localization method for JTC compensated capacitors, which is based on sliding adaptive data segmentation (SADS) and fast dynamic time warping (FDTW), to enhance the K-nearest neighbour (KNN) algorithm. The approach models the track circuit to understand voltage signal changes and establishes a universal fault discrimination model, this is followed by applying the SADS approach, which breaks down the measured induced voltage signal into distinct segments reflecting the characteristics of compensation capacitor faults. The method then employs FDTW to refine the traditional K-nearest neighbour algorithm for effective fault localization. Experimental results show that the segmented dataset achieves accuracy rates of 97.8% on simulated datasets and 97.1% on measurement datasets, offering a powerful tool for locating compensation capacitor faults in rail circuits.

Clinical application of mobile CT combined with procedural sedation and analgesia in the preoperative localization of multiple pulmonary nodules.

To evaluate the safety and efficacy of mobile CT combined with procedural sedation and analgesia for the preoperative localization of multiple nodules. The clinical data of 200 patients who underwent CT-guided localization before single-port thoracoscopic pulmonary lobe surgery at our hospital from July 2023 to September 2023 were retrospectively analyzed. The patients were divided into two groups according to the localization method: Group A consisted of 100 patients who were localized under local anesthesia, and Group B consisted of 100 patients who were localized under procedural sedation and analgesia combined with local anesthesia. The general clinical data and localization data of the two groups were compared and analyzed. The incidence of localization complications in Group B was significantly lower than that in Group A (4% vs. 13%, P = 0.04). The localization success rate in Group B was significantly greater than that in Group A (98% vs. 92%, P = 0.04). The localization time in Group B was significantly shorter than that in Group A (15.23 ± 5.96min vs. 19.90 ± 8.66min, P<0.01), and the pain score in Group B was significantly lower than that in Group A (2.01 ± 2.09min vs. 3.29 ± 2.54min, P<0.01). Mobile CT combined with procedural sedation and analgesia for preoperative puncture localization of multiple pulmonary nodules is safe and effective, with significant clinical application value.

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

Structural and electronic properties of GaN: Ab initio study within LDA and LDA+U methods

Structural and electronic properties of the GaN were simulated based on Density Functional Theory implementing Local Density Approximation methods. Hubbard U correction gives us an opportunity to find the correct energy gap for GaN in agreement with known experimental results. Choosing more accurate investigation methods leads to calculating accurate electronic band structure and in the future predicting some physical properties of related material. The bottom of the conduction band and the top of the valence band are formed mainly by p-orbitals of host Ga and N atoms. The present study shows the direct band gap character of GaN with a wurtzite structure

The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks

Due to the increasing integration of new energy sources, the power system now exhibits low inertia, in which the broadband oscillation problem is increasingly significant in the face of the strong coupling of complex and variable power systems, and the current lack of uniform and effective mathematical models and analysis methods. To solve this major problem, a broadband oscillation localization method based on the combination of compressed perception and graph attention network (GAT) is proposed. The method firstly uses the principle of compression perception to compress and transmit the oscillation time series data of the sub-station, reconstructs the compressed signal at the master station and aggregates the grid topology and node characteristic information to effectively reduce the redundancy of the oscillation data; reconstruction error is only 0.031, takes into account the balance of the samples and the effectiveness of the computation, and adopts the multi-attention mechanism and the cross-entropy loss function to improve the performance of the model training. Finally, the offline training and online evaluation model based on the GAT algorithm is constructed, and the accuracy of the model is up to 98.5%; and the results show that the method has a high positioning accuracy and a certain anti-noise ability at the same time.

Mobile robot localization method based on point-line feature visual-inertial SLAM algorithm

Mobile robot localization method based on point-line feature visual-inertial SLAM algorithm

The effectiveness and safety of computed tomography-guided hook-wire localization for secondary video-assisted thoracoscopic surgery: a retrospective study.

Computed tomography (CT)-guided hook-wire localization is currently the most commonly used technique for preoperative localization of pulmonary nodules in clinical practice. With the increasing incidence of multiple primary lung cancers and the increasing occurrence of second primary lung cancers or local recurrences after lung cancer surgery, some patients need to undergo an ipsilateral or contralateral second pulmonary resection. In order to reduce the surgical difficulty of the second operation and accurately guide the surgical resection, preoperative localization of some nodules is necessary. This study retrospectively analyzed the CT-guided hook-wire localization information of patients undergoing a second ipsilateral or contralateral video-assisted thoracoscopic surgery (VATS), discussed the safety and effectiveness of hook-wire localization for ipsilateral and contralateral surgeries, and analyzed the risk factors for complications after localization. This study retrospectively collected data from 113 patients with isolated solitary pulmonary nodules who underwent a second pulmonary resection and completed CT-guided hook-wire localization at The First Affiliated Hospital of Soochow University from January 2020 to June 2024. The patients were divided into contralateral surgery group and ipsilateral surgery group to compare clinical characteristics, localization time, incidence of complications, and other information between the two groups. Logistic regression analysis was used to identify the risk factors for complications associated with the localization methods in each group. Compared to the contralateral group, the ipsilateral group had a longer interval between the two surgeries, which was 28.0±9.3 months (P<0.001). The maximum diameter of nodules in the ipsilateral group was 10.8±1.7 mm, higher than that in the contralateral group (P<0.001). There were no statistical differences between the two groups in terms of localization time, number of CT scans, depth of the release position, and incidence of complications. In the contralateral group, 25 patients (30.5%) developed pneumothorax, while in the ipsilateral group, there were 2 cases (6.5%), showing a statistically significant difference (P=0.008). In the contralateral group, 3.7% patients developed hemopneumothorax, while the incidence in the ipsilateral group was as high as 16.1% (P=0.04). Localization time [odds ratio (OR) =1.306, P=0.006] and depth of the release position (OR =1.202, P<0.001) were independent risk factors for the overall occurrence of complications and pneumothorax, while thoracic adhesions (OR =4.115, P=0.03) was an independent risk factor for hemopneumothorax. History of localization (OR =0.109, P=0.02) was identified as a protective factor for pneumothorax. CT-guided hook-wire localization can effectively complete the localization of isolated nodules on the ipsilateral or contralateral side in patients requiring a second pulmonary resection, with similar safety. In the future, it is more advisable to promote more precise and personalized localization methods for different patients in clinical practice.

A Deep Reinforcement Learning-Based Shortwave Multistation Autonomous Cooperative Direction Finding and Localization Method

A Deep Reinforcement Learning-Based Shortwave Multistation Autonomous Cooperative Direction Finding and Localization Method

Моделирование движения нефтяного загрязнения в районе западного побережья Калининградского полуострова

The relevance of the study is determined by the need to improve forecasting and assessment of oil contamination in the Southeastern Baltic area based on the application of mathematical modeling methods for environmental contamination processes in order to increase the safety level in case of accidents in water bodies. In the sphere of oil spills, the liquidation of consequences of emergencies, increasing efficiency, and the development of new localization methods and mechanical sampling using forecasting systems are prioritized. At the same time, the tendency to increase volumes of maritime transportation remains, which causes a high risk of environmental contamination from ships and cargo terminals classified as hazardous production facilities. The most important area of environmental protection in this sphere is forecasting the consequences of oil contamination and the development of protection measures based on its results. Notably, the Southeastern sector is the most contaminated area of the Baltic Sea with petroleum products. Therefore, the study's objective is to forecast the movement of oil spills and compare the obtained results with the earlier investigations in the Southeastern Baltic area based on the example of oil contamination in the Western part of the Kaliningrad (Sambi) Peninsula. For such purposes, a situational mathematical model of oil spills has been built using the Pisces II software and compared with the forecast results using the Seatrack Web software; emergency maps have been developed. Methods of georeferencing, numerical simulation modeling of oil spills, and decomposition of emergencies have been used.