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This paper presents a weld seam tracking system using cross mark structured light. The hardware of the proposed system consists of a two degrees of freedom (DOF) welding robot, a camera with cross mark structured light, and two computers. The system has two parts namely visual sensing and motion control. In the visual sensing part, the cross mark of the structured light is utilized to set a region of interest (ROI). In the ROI, an adapted line fitting algorithm is employed to estimate the lines. Then, intersections of the lines are computed and used as the pin points for templates creating. During the matching process, a modified template matching is used to detect the edges of V-groove weld seam. By using this technique, a huge computational cost in image processing can be reduced, and therefore the tracking can be made in real time. The position based visual servoing with proportional-derivative(PD) and velocity feedback controller is designed for seam tracking. The experimental results show that the proposed method performs the real-time tracking efficiently with sufficient accuracy.

The detection and recognition of objects in images is a key research topic in the computer vision community.  Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems.

Traditional premature ventricular contraction (PVC) detection algorithms based on template matching use fixed templates which is sensitive to the variability of electrocardiogram (ECG) and is likely to reduce detection accuracy. This paper proposes an effective PVC detection algorithm based on adaptive template matching and characteristic recognition. An adaptive template update rule has been developed to construct the optimal normal heartbeat template library, which is used to identify normal and abnormal beats. And the matching result directed the subsequent characteristic classification. Four features were extracted and used for PVC recognition based on feature knowledge. The algorithm is evaluated on the MIT-BIH arrhythmia dataset, and the results show that the accuracy was 99.48%, and the sensitivity and specificity were 98.39% and 99.54%, respectively. And it is compared with those of other recent approaches algorithms, which shows higher sensitive and accurate, and its low complexity is suitable for real-time ECG monitoring in wearable ECG devices.

The fast and precise positioning of lithium battery is crucial for effective manufacturing of mass production. In order to acquire position information of lithium batteries rapidly and accurately, a novel dual-template matching algorithm is proposed to properly locate and segment each battery for fast and precise mass production. Initially, an image down-sampling method is applied to build up a multi-layer image pyramid for speeding up target search, and a novel mixed matching template is designed to increase the matching precision. A row of lithium batteries is likely tilt during rolling, and the images of batteries captured by the CCD camera are distorted, which may generate a negative effect on next procedure. Hence, a two-level correction algorithm for battery angle and location is applied to obtain rough areas of the batteries and improve the accuracy of template matching. Lastly, the comparison with other state-of-the-art algorithms is done to locate each battery in a row with high speed and precision. The precision rates of the proposed algorithm, improved SAD algorithm, and YOLOv3 algorithm are 99.44%, 95.98%, and 93.64 for normal battery images and 97.86%, 89.19%, and 85.10 for tilted battery images, respectively. Compared with improved SAD matching algorithm and YOLOv3 algorithm, the positioning accuracy of the proposed method is significantly increased, and the matching robustness is improved in spite of large battery inclination angle.

The identification of seismic clusters is essential for many applications of statistical analysis and seismicity forecasting: uncertainties in cluster identification leads to uncertainties in results. However, there are several methods to identify clusters, and their results are not always compatible. We tested different approaches to analyze the clustering: a traditional window-based approach, a complex network-based technique (nearest neighbor—NN), and a novel approach based on fractal analysis. The case study is the increase in seismicity observed in Molise, Southern Italy, from April to November 2018. To analyze the seismicity in detail with the above-mentioned methods, an improved template-matching catalog was created. A stochastic declustering method based on the Epidemic Type Aftershock Sequence (ETAS) model was also applied to add probabilistic information. We explored how the significant discrepancies in these methods’ results affect the result of NExt STrOng Related Earthquake (NESTORE) algorithm—a method to forecast strong aftershocks during an ongoing cluster—previously successfully applied to the whole Italian territory. We performed a further analysis of the spatio-temporal pattern of seismicity in Molise, using the Principal Component Analysis (PCA), the ETAS algorithm, as well as other analyses, aimed at detecting possible migration and diffusion signals. We found a relative quiescence of several months between the main events of April and August, the tendency of the events to propagate upwards, and a migration of the seismicity consistent with a fluid-driven mechanism. We hypothesize that these features indicate the presence of fluids, which are also responsible for the long duration of the sequence and the discrepancies in cluster identification methods’ results. Such results add to the other pieces of evidence of the importance of the fluid presence in controlling the seismicity in the Apennines. Moreover, this study highlights the importance of refined methods to identify clusters and encourages further detailed analyses when different methods supply very different results. Graphical Abstract

Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

This study analyzes seismic activity related to the 2025 ML 6.4 Dapu earthquake sequence in Chiayi County and Tainan City, Taiwan. By integrating a machine learning-based earthquake catalog with the Weighted Template Matching Algorithm (WTMA), over 40,000 microseismic events were detected, many of which were previously undetected due to waveform overlap with other seismic events. These microseismic detections enhance the understanding and interpretation of detailed aftershock distributions. Additionally, Centroid Moment Tensor (CMT) solutions were analyzed for further insights into the underlying seismogenic structures. In particular, the mainshock region exhibits complex structural features characterized by both east-dipping detachment faults and reactivated west-dipping basement faults occurring at varying depths. These findings highlight the intricate structural dynamics within Taiwan's actively deforming orogenic belt. The results also suggest that interactions within the fault system triggered progressive seismic activity, gradually propagating to adjacent areas. Such insights are critical for refining seismic hazard assessments and contribute to enhanced understanding of regional tectonic processes. Key points WTMA provides a more complete earthquake catalog, refining the spatial distribution of seismicity. The Dapu earthquake sequence reveals both east-dipping faults and reactivated west-dipping basement faults contributing to seismic activity. The delayed activation of seismicity in adjacent regions suggests complex post-seismic fault interactions.

In the virtual geographic environment, conducting status analysis on urban structures and similar objects is crucial for enhancing their detailed management level. However, it is challenging to directly convert the same object across various software systems with different modalities (such as spatial analysis, BIM design, numerical simulation etc.). Therefore, the effective conversion of multi-modal models becomes pivotal. Due to the characteristics of inconsistent spatial description and complex association relationship among multimodal models, resulting in low knowledge reuse rate, poor accuracy of unit mapping, and low efficiency of state sharing in the process of model conversion. Aiming at these problems, this article delves into the knowledge-driven semantic conversion techniques for multi-modal models from a geospatial viewpoint. The mapping relationships between multimodal models in terms of spatial, geometric, and semantic information were first clarified. Subsequently, a structural matching template based on knowledge reuse was established, and a knowledge-guided algorithm for multimodal model transformation was designed. Finally, using a suspension bridge as a case study, a prototype system was developed and experimental analysis was conducted. The experimental results show that the method proposed in this article can accurately convert between BIM models, numerical analysis models, and GIS scene models, with spatial coordinate accuracy controlled within 1 mm and a conversion efficiency increase of more than 10 times. This can effectively enhance the integrated performance of models in applications such as digital geospatial twin scenarios.

In Germany, the highest seismic hazard is associated with the Albstadt Shear Zone (ASZ) in the western Swabian Jura, a low mountain range in southwest Germany. The region is affected by continuous micro-seismic activity with the potential for damaging earthquakes (nine events with ML≥ 5 in the 20th century). Within the AlpArray and StressTransfer projects nine temporary seismic stations have been installed in the region of the ASZ to densify the permanent seismic monitoring. In October 2018 and September 2019, the state seismological survey (LED) detected two low-magnitude earthquake sequences with hundreds of events in the area. The temporarily densified local network allows us to systematically analyze these sequences and to search for other sequences by applying a template-matching routine on data from 2018 to 2020. In total, six earthquake sequences could be identified with at least 10 events. The four largest sequences (> 50 events) consist of two fore- and aftershock sequence and two earthquake swarms. Earthquake swarms were so far not observed around the ASZ. Precise relative hypocenter relocations and the determination of fault-plane solutions allow us to propose a seismotectonic model based on the three imaged fault types: (a) The well-known NNE-SSW striking sinistral strike-slip ASZ at depths of 5-10 km, (b) a NW-SE striking dextral strike-slip fault zone at depths of 11-15 km beneath the Hohenzollerngraben (HZG), a shallow, apparently aseismic NW-SE striking graben structure; this NW-SE fault zone possibly is an inherited zone of weakness in the basement and facilitated the development of the HZG and (c) at the intersection of the ASZ with the NW-SE fault zone, complex faulting in form of NNW-SSE striking sinistral strike-slip and normal faulting possibly to accommodate local stresses.

This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m2) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO.Graphical abstract