Traditional Matching Research Articles

Ensemble-based assisted history matching of DTS monitoring data in HT-ATES systems: application to a real-life case study

Underground heat storage is an important element in accelerating the energy transition. It can significantly contribute to CO2 emission reduction and cost savings since it is one of the cheapest forms of energy storage and it enables the seasonal storage of large energy surpluses from sustainable sources, e.g. wind, sun, geothermal. Numerical models are used for the prediction of thermal behavior important in establishing the high efficiency of the high temperature aquifer thermal energy storage (HT-ATES) systems. However, the lack of exact knowledge of the subsurface conditions introduces modeling uncertainty. It is therefore important to employ approaches that reduce subsurface uncertainty. History matching is a methodology where the numerical models are updated to match historical observations that will in turn not only increase understanding of the subsurface but also improve accuracy of the model predictability of future behavior. In this research, models of the first large-scale operational HT-ATES system in Middenmeer, the Netherlands, were used to evaluate the thermal evolution in the storage aquifer and the over- and underburden clay layers. The HT-ATES system, consisting of a hot and warm well, with a monitoring well inbetween, became operational in the summer of 2021. The extensive monitoring program implemented for the first few operational years provided an opportunity to study the performance of such a system from an environmental and operational point of view. A state-of-the-art assisted history matching approach was applied to the first storage cycle, using a coupling between history matching software and the thermal flow simulator. This approach was compared to a more traditional single-model manual history matching method. Rock properties of the aquifer and over- and underburden layers were updated in the randomly generated prior ensemble of models to fit the simulated temperature evolution measured down the monitoring well with the distributed temperature sensing (DTS) data. The observations gathered during the second year of operations were used to validate the accuracy of the prediction capabilities of the updated models. The obtained results indicate the value of history matching to improve understanding of the subsurface conditions for HT-ATES systems and obtain models with better predictability of the future behavior of heat in the storage reservoir and overburden formations. Such improved models are instrumental in providing engineers with a better quantitative grip on the environmentally responsible storage potential and heat deliverability of the target storage site, which is important to achieve cost-effective site-specific design (e.g. number of wells, well placement) and performing operational strategies (e.g. injection/production rates and temperatures) for new HT-ATES systems. Moreover, the benefits of the assisted history matching approach over manual method are highlighted and both approaches are validated where the assisted history matching method produced more accurate predictions than the manual approach.

ISFM-SLAM: dynamic visual SLAM with instance segmentation and feature matching.

Simultaneous Localization and Mapping (SLAM) is a technology used in intelligent systems such as robots and autonomous vehicles. Visual SLAM has become a more popular type of SLAM due to its acceptable cost and good scalability when applied in robot positioning, navigation and other functions. However, most of the visual SLAM algorithms assume a static environment, so when they are implemented in highly dynamic scenes, problems such as tracking failure and overlapped mapping are prone to occur. To deal with this issue, we propose ISFM-SLAM, a dynamic visual SLAM built upon the classic ORB-SLAM2, incorporating an improved instance segmentation network and enhanced feature matching. Based on YOLACT, the improved instance segmentation network applies the multi-scale residual network Res2Net as its backbone, and utilizes CIoU_Loss in the bounding box loss function, to enhance the detection accuracy of the segmentation network. To improve the matching rate and calculation efficiency of the internal feature points, we fuse ORB key points with an efficient image descriptor to replace traditional ORB feature matching of ORB-SLAM2. Moreover, the motion consistency detection algorithm based on external variance values is proposed and integrated into ISFM-SLAM, to assist the proposed SLAM systems in culling dynamic feature points more effectively. Simulation results on the TUM dataset show that the overall pose estimation accuracy of the ISFM-SLAM is 97% better than the ORB-SLAM2, and is superior to other mainstream and state-of-the-art dynamic SLAM systems. Further real-world experiments validate the feasibility of the proposed SLAM system in practical applications.

Advanced simulation of combustion characteristics for hazardous nitrogenous compounds using multi-component gaseous fuels

Nitrogen-containing compounds are widely used as raw materials or intermediates in industries such as pharmaceuticals, dyes, explosives, and plastics. However, there is a lack of reliable and effective research methods for accurately predicting the consequences of accidents involving hazardous nitrogenous chemicals. This paper presents a novel method for simulating the combustion characteristics of nitrogen-containing hazardous chemicals, such as Hexogen (RDX) and Octogen (HMX), using multi-component gaseous small molecule fuels. The method relies on a theoretical modeling approach and numerical simulation to predict the behavior of intermediate combustion products. Key advancements include establishing a standard modeling method identifying 11 different small molecule components, and creating predictive model libraries through combinatorial methods. This approach moves away from traditional target matching by calculating proportion coefficients for each component based on their contribution to ignition characteristics. The feasibility and accuracy of this method were validated through experiments using a microscale calorimeter (MCC), demonstrating a high correlation (0.9981) between experimental results and model predictions. This method was found particularly effective for real-time prediction of thermal hazards in high-rise building scenarios, exemplified by 2-Ethylhexyl nitrate (EHN). The paper concludes with the identification of optimal multi-component models for RDX and HMX, highlighting the significant role of hydrogen cyanide (HCN) and unsaturated hydrocarbons in these models.

Automatic Image Registration Provides Superior Accuracy Compared with Surface Matching in Cranial Navigation.

The precision of neuronavigation systems relies on the correct registration of the patient's position in space and aligning it with radiological 3D imaging data. Registration is usually performed by the acquisition of anatomical landmarks or surface matching based on facial features. Another possibility is automatic image registration using intraoperative imaging. This could provide better accuracy, especially in rotated or prone positions where the other methods may be difficult to perform. The aim of this study was to validate automatic image registration (AIR) using intraoperative cone-beam computed tomography (CBCT) for cranial neurosurgical procedures and compare the registration accuracy to the traditional surface matching (SM) registration method based on preoperative MRI. The preservation of navigation accuracy throughout the surgery was also investigated. Adult patients undergoing intracranial tumor surgery were enrolled after consent. A standard SM registration was performed, and reference points were acquired. An AIR was then performed, and the same reference points were acquired again. Accuracy was calculated based on the referenced and acquired coordinates of the points for each registration method. The reference points were acquired before and after draping and at the end of the procedure to assess the persistency of accuracy. In total, 22 patients were included. The mean accuracy was 6.6 ± 3.1 mm for SM registration and 1.0 ± 0.3 mm for AIR. The AIR was superior to the SM registration (p < 0.0001), with a mean improvement in accuracy of 5.58 mm (3.71-7.44 mm 99% CI). The mean accuracy for the AIR registration pre-drape was 1.0 ± 0.3 mm. The corresponding accuracies post-drape and post-resection were 2.9 ± 4.6 mm and 4.1 ± 4.9 mm, respectively. Although a loss of accuracy was identified between the preoperative and end-of-procedure measurements, there was no statistically significant decline during surgery. AIR for cranial neuronavigation consistently delivered greater accuracy than SM and should be considered the new gold standard for patient registration in cranial neuronavigation. If intraoperative imaging is a limited resource, AIR should be prioritized in rotated or prone position procedures, where the benefits are the greatest.

An Optimized Multi-Level Control Method for Wireless Power Transfer System Using the Particle Swarm Optimization Algorithm

A Wireless Power Transfer (WPT) system, known for its contactless power delivery, is extensively used for power supply in spacecraft applications. Achieving efficient and stable power transfer necessitates the integration of DC/DC converters on both the primary and secondary sides of WPT systems for power conversion and control. Traditional efficiency optimization methods primarily focus on impedance matching within the wireless power resonance network, often neglecting the overall efficiency optimization of multi-stage DC-DC and WPT systems. This oversight results in suboptimal overall system efficiency despite optimal efficiency in the wireless transmission segment. Additionally, the time-varying nature of mutual inductance and load parameters during power transmission in WPT systems presents challenges for maximum efficiency tracking and power control. This paper introduces a multi-level coordinated control efficiency optimization method for WPT systems utilizing the particle swarm optimization (PSO) algorithm. This method takes into account the transmission losses across all power conversion units within the WPT system, establishing a mathematical model for the joint optimization of overall system transmission efficiency and power. The PSO algorithm is then employed to solve this optimization model using estimated mutual inductance and load values. By adjusting the DC/DC converters on both sides, the method ensures optimal overall system efficiency and consistent power transmission. Experimental results indicate that under varying load and mutual inductance conditions, a Series–Series (SS) compensated WPT system using this method achieves a 200 W power output with maximum efficiency tracking, a power output error of 0.63%, and an average transmission efficiency of 86.2%. This demonstrates superior power transmission stability and higher efficiency compared to traditional impedance matching methods.

Data-driven Prediction of Underwater Navigation Fitness Zone Classification

This article presents a pioneering approach to enhancing underwater navigation accuracy and stealth through the utilization of gravity anomaly data. Developed by researchers from leading universities in China, the study aims to revolutionize autonomous marine navigation systems in unknown sea areas. By integrating advanced data processing, feature extraction, model construction, and optimization techniques, a cutting-edge navigation system has been formulated, showcasing remarkable improvements in navigation precision.The core innovation lies in leveraging bicubic interpolation to refine data resolution, enabling a meticulous evaluation of regional suitability and segmentation of sea zones. Subsequently, a gradient-based suitability prediction model is constructed, which is validated through migration prediction, underscoring its effectiveness and reliability. This research not only addresses the limitations of traditional terrain matching navigation methods, plagued by data accuracy and resolution constraints, but also paves the way for advancements in oceanic exploration and technological competitiveness.The introduction of gravity anomaly data, coupled with machine learning and visualization tools, marks a significant step forward in the development of adaptive navigation zones. This innovative system holds immense potential to enhance the autonomy, accuracy, and concealment of underwater vehicles, thereby facilitating safer and more efficient oceanic missions in the future.

High-Precision Disparity Estimation for Lunar Scene Using Optimized Census Transform and Superpixel Refinement

High-precision lunar scene 3D data are essential for lunar exploration and the construction of scientific research stations. Currently, most existing data from orbital imagery offers resolutions up to 0.5–2 m, which is inadequate for tasks requiring centimeter-level precision. To overcome this, our research focuses on using in situ stereo vision systems for finer 3D reconstructions directly from the lunar surface. However, the scarcity and homogeneity of available lunar surface stereo datasets, combined with the Moon’s unique conditions—such as variable lighting from low albedo, sparse surface textures, and extensive shadow occlusions—pose significant challenges to the effectiveness of traditional stereo matching techniques. To address the dataset gap, we propose a method using Unreal Engine 4 (UE4) for high-fidelity physical simulation of lunar surface scenes, generating high-resolution images under realistic and challenging conditions. Additionally, we propose an optimized cost calculation method based on Census transform and color intensity fusion, along with a multi-level super-pixel disparity optimization, to improve matching accuracy under harsh lunar conditions. Experimental results demonstrate that the proposed method exhibits exceptional robustness and accuracy in our soon-to-be-released multi-scene lunar dataset, effectively addressing issues related to special lighting conditions, weak textures, and shadow occlusion, ultimately enhancing disparity estimation accuracy.

Effective Hard Negative Mining for Contrastive Learning-based Code Search

Background. Code search aims to find the most relevant code snippet in a large codebase based on a given natural language query. An accurate code search engine can increase code reuse and improve programming efficiency. The focus of code search is how to represent the semantic similarity of code and query. With the development of code pre-trained models, the pattern of using numeric feature vectors (embeddings) to represent code semantics and using vector distance to represent semantic similarity has replaced traditional string matching methods. The quality of semantic representations is critical to the effectiveness of downstream tasks such as code search. Currently, the state-of-the-art (SOTA) learning method uses the contrastive learning paradigm. The objective of contrastive learning is to maximize the similarity between matching code and query (positive samples) and minimize the similarity between mismatched pairs (negative samples). To increase the reusing of negative samples, prior contrastive learning approaches use a large queue (memory bank) to store embeddings. Problem. However, there is still a lot of room for improvement in using negative examples for code search: ① Due to the random selection of negative samples, semantic representations learned by existing models cannot distinguish similar codes well. ② Since semantic vectors in the memory bank are reused from previous inference results and then directly used for loss function calculation without gradient descent, the model cannot effectively learn the negative sample semantic information. Method. To solve the above problems, we propose a contrastive learning code search model with hard negative mining called CoCoHaNeRe: ❶ To enable the model to distinguish similar codes, we introduce hard negative examples into contrastive training, which are negative examples in the codebase that are most similar to positive examples. As a result, hard negative examples are most likely to make the model make mistakes. ❷ To improve the learning efficiency of negative samples during training, we add all hard negative examples to the model's gradient descent process. Result. To verify the effectiveness of CoCoHaNeRe, we conducted experiments on large code search datasets with six programming languages, as well as similar retrieval tasks code clone detection and code question answering. Experimental results show that our model achieves SOTA performance. In the code search task, the average MRR score of CoCoHaNeRe exceeds CodeBERT, GraphCodeBERT, and UniXcoder by 11.25%, 8.13%, and 7.38%, respectively. It has also made great progress in code clone detection and code question answering. In addition, our method performs well in different programming languages and code pre-training models. Furthermore, qualitative analysis shows that our model effectively distinguishes high-order semantic differences between similar codes.

Automated painting color matching technology based on semantic intelligence understanding

Painting color matching technology is widely used in the production and printing process of products. Traditional painting and color matching have been unable to meet market demands. Based on this, a large-scale corpus under the existing semantic intelligent understanding system is used as the knowledge source. The computer automated painting color matching model is constructed. It is applied in case studies to address issues such as unclear query intentions, mismatched system retrieval terms, and return errors caused by uncertain factors such as synonyms and polysemy. This provides new ideas for the application of semantic intelligence understanding and automated painting color matching technology. The experimental results showed that the precision, recall, and F1 of the method used in the research were 0.8639, 0.8026, and 0.8309, respectively, significantly superior to commonly used methods. This indicates that the proposed automated painting color matching technology based on semantic intelligent understanding has high performance, which can effectively meet the painting color matching requirements.

Machine learning and propensity score matching for evaluating the effect of special education services on childrens later math performances

The goal of the research is to analyze the effect of special education service brought to childrens later math performance. The dataset is derived from Early Childhood Longitudinal Studies(ECLS) program and the children being studied come from diverse socioeconomic and racial/ethnic backgrounds. We applied PSM and modern machine learning methods including OLS, KNN, BART and MLP on the data in order to calculate the average treatment effect of special education services. It turned out that all the listed machine learning algorithms with comparatively low STD outperformed the traditional propensity score matching. KNN, BART and OLS excelled, offering much more stable calculations. The value of ATE computed through all the methods appeared below zero. By applying linear regression and PCA on all the influencing factors, the analysis revealed that the differences of some factors between the controlled and exposed groups led better math performances to appear usually in the absence of special treatment. Thus, special treatment effect led the trained model to predict lower scores, which finally caused the difference to be negative.

Intelligent Dance Motion Evaluation: An Evaluation Method Based on Keyframe Acquisition According to Musical Beat Features.

Motion perception is crucial in competitive sports like dance, basketball, and diving. However, evaluations in these sports heavily rely on professionals, posing two main challenges: subjective assessments are uncertain and can be influenced by experience, making it hard to guarantee timeliness and accuracy, and increasing labor costs with multi-expert voting. While video analysis methods have alleviated some pressure, challenges remain in extracting key points/frames from videos and constructing a suitable, quantifiable evaluation method that aligns with the static-dynamic nature of movements for accurate assessment. Therefore, this study proposes an innovative intelligent evaluation method aimed at enhancing the accuracy and processing speed of complex video analysis tasks. Firstly, by constructing a keyframe extraction method based on musical beat detection, coupled with prior knowledge, the beat detection is optimized through a perceptually weighted window to accurately extract keyframes that are highly correlated with dance movement changes. Secondly, OpenPose is employed to detect human joint points in the keyframes, quantifying human movements into a series of numerically expressed nodes and their relationships (i.e., pose descriptions). Combined with the positions of keyframes in the time sequence, a standard pose description sequence is formed, serving as the foundational data for subsequent quantitative evaluations. Lastly, an Action Sequence Evaluation method (ASCS) is established based on all action features within a single action frame to precisely assess the overall performance of individual actions. Furthermore, drawing inspiration from the Rouge-L evaluation method in natural language processing, a Similarity Measure Approach based on Contextual Relationships (SMACR) is constructed, focusing on evaluating the coherence of actions. By integrating ASCS and SMACR, a comprehensive evaluation of dancers is conducted from both the static and dynamic dimensions. During the method validation phase, the research team judiciously selected 12 representative samples from the popular dance game Just Dance, meticulously classifying them according to the complexity of dance moves and physical exertion levels. The experimental results demonstrate the outstanding performance of the constructed automated evaluation method. Specifically, this method not only achieves the precise assessments of dance movements at the individual keyframe level but also significantly enhances the evaluation of action coherence and completeness through the innovative SMACR. Across all 12 test samples, the method accurately selects 2 to 5 keyframes per second from the videos, reducing the computational load to 4.1-10.3% compared to traditional full-frame matching methods, while the overall evaluation accuracy only slightly decreases by 3%, fully demonstrating the method's combination of efficiency and precision. Through precise musical beat alignment, efficient keyframe extraction, and the introduction of intelligent dance motion analysis technology, this study significantly improves upon the subjectivity and inefficiency of traditional manual evaluations, enhancing the scientificity and accuracy of assessments. It provides robust tool support for fields such as dance education and competition evaluations, showcasing broad application prospects.

Biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics

Biometric authentication has been widely used because of its uniqueness, accuracy, and versatility, but existing methods still have some shortcomings. Because biometric data is stored in the database, once the database is invaded, it will cause a large number of biometric data leakage and illegal use of huge risks; The cost of traditional feature extraction and matching strategy is too high; Biometric is only used for authentication, and its single function cannot meet the increasing security requirements; Methods that rely solely on biometric keys are not secure enough. To overcome these shortcomings, we propose the biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics. The proposed method innovatively combines biometrics and inhomogeneous media optics to achieve dual functions of encryption and authentication. Instead of storing biometric data directly, the encryption and authentication information is encrypted as computer generated holograms in this method and stored in the database to reduce the risk of biometric data leakage. This method has low cost without complicated feature extraction and comparison authentication process. The dual functions of authentication and encryption is more practical. Multiple optics-biology keys improve the security. The security and robustness are analyzed. A graphical user interface (GUI) is designed for the method to promote its application. This work provides new methods and new ideas for the field of optics encryption and authentication, and will certainly have a broad application prospect.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Mechanism of the Terahertz Wave-MXene Interaction and Surface/Interface Chemistry of MXene for Terahertz Absorption and Shielding.

ConspectusOver the past two decades, terahertz (THz) technology has undergone rapid development, driven by advancements and the growing demand for THz applications across various scientific and technological domains. As the cornerstone of THz technology, strong THz-matter interactions, especially realized as high THz intrinsic absorption in nanometer-thick materials, play a highly important role in various applications including but not limited to THz absorption/shielding, detection, etc. The rigorous electromagnetic theory has posited a maximum intrinsic absorption of 50% for electromagnetic waves by thin films, and the succinct impedance matching condition has also been formulated to guide the design of highly intrinsically absorbing materials. However, these theories face challenges when applied to the THz spectrum with an ultrabroad bandwidth. Existing thin films typically achieve a maximum intrinsic absorption within a narrow frequency range, significantly limiting the performance of THz absorbers and detectors. To date, both theoretical frameworks and experimental solutions are lacking in overcoming the challenge of achieving broadband maximum intrinsic absorption in the THz regime.In this Account, we describe how two-dimensional (2D) transition-metal carbide and/or nitride (MXene) films with nanometer thickness can realize the maximum intrinsic absorption in the ultrabroad THz band, which successfully addresses the forementioned longstanding issue. Surprisingly, traditional DC impedance matching theory fails to explain this phenomenon, while we instead propose a novel theory of AC impedance matching to provide a satisfactory explanation. By delving into the microscopic transport behavior of free electrons in MXene, we discover that intraflake transport dominates terahertz conductivity under THz wave excitation, while interflake transport primarily dictates DC conductivity. This not only elucidates the significant disparities between DC and AC impedance in MXenes but also underscores the suitability of AC impedance matching for achieving broadband THz absorption limits. Furthermore, we identify a high electron concentration and short relaxation time as crucial factors for achieving broadband maximum absorption in the THz regime. Although approaching the THz intrinsic absorbing limits, it still faces hurdles to the use of MXene in practical applications. First, diverse and uncontrollable terminations exist on the surface of MXene stemming from the synthesis process, which largely influence the electron structure and THz absorbing property of MXene. Second, MXene suffers from poor stability in the presence of oxygen and water. To address the above issues, we have undertaken distinctive works to precisely control the terminations and suppress the oxidation of MXene even at high temperature through surface and interface chemistry, such as low-temperature Lewis basic halide treatment and building a Ti3C2Tx/extracted bentonite (EB) interface. For practical application consideration, we proposed a copolymer-polyacrylic latex (PAL)-based MXene waterborne paint (MWP) with a strong intermolecular polar interaction between MWP and the substrate provided by the cyano group in PAL. This not only has strong THz EMI shielding/absorption efficiency but also can easily adhere to various substrates that are commonly used in the THz band. These studies may have significant implications for future applications of MXene nanofilms in THz optoelectronic devices.

Evaluation of GLOCK 9 mm Firing Pin Aperture Shear Mark Individuality Based on 3,156 Different Pistols (Manufactured Over a 30 Year Period in Two Countries) Using Additional Pattern Matching and IBIS Pattern Recognition

Over a period of 30 years, a number of fired GLOCK cartridge cases have been evaluated. A total of 3156 GLOCK firearms were used to generate a sample of the same size. Our research hypothesis was that no cartridge cases fired from different 9 mm semiautomatic GLOCK pistols would be mistaken as coming from the same gun (a false match). Using optical comparison microscopy, two separate experiments were carried out to test this hypothesis. A subsample of 617 test-fired cartridge cases were subjected to algorithmic comparison by the Integrated Ballistics Identification System (IBIS). The second experiment subjected the full set of 3,156 cases to manual comparisons using traditional pattern matching. None of the cartridge cases were “matched” by either of these two experiments. Using these empirical findings, an established conservative Bayesian probability model was used to estimate the chance that a 9 mm cartridge case, fired from a GLOCK, could be mistaken as coming from the same firearm when in fact it did not (i.e., the false match probability).

Research on a Matching Method for Vehicle-Borne Laser Point Cloud and Panoramic Images Based on Occlusion Removal

Vehicle-borne mobile mapping systems (MMSs) have been proven as an efficient means of photogrammetry and remote sensing, as they simultaneously acquire panoramic images, point clouds, and positional information along the collection route from a ground-based perspective. Obtaining accurate matching results between point clouds and images is a key issue in data application from vehicle-borne MMSs. Traditional matching methods, such as point cloud projection, depth map generation, and point cloud coloring, are significantly affected by the processing methods of point clouds and matching logic. In this study, we propose a method for generating matching relationships based on panoramic images, utilizing the raw point cloud map, a series of trajectory points, and the corresponding panoramic images acquired using a vehicle-borne MMS as input data. Through a point-cloud-processing workflow, irrelevant points in the point cloud map are removed, and the point cloud scenes corresponding to the trajectory points are extracted. A collinear model based on spherical projection is employed during the matching process to project the point cloud scenes to the panoramic images. An algorithm for vectorial angle selection is also designed to address filtering out the occluded point cloud projections during the matching process, generating a series of matching results between point clouds and panoramic images corresponding to the trajectory points. Experimental verification indicates that the method generates matching results with an average pixel error of approximately 2.82 pixels, and an average positional error of approximately 4 cm, thus demonstrating efficient processing. This method is suitable for the data fusion of panoramic images and point clouds acquired using vehicle-borne MMSs in road scenes, provides support for various algorithms based on visual features, and has promising applications in fields such as navigation, positioning, surveying, and mapping.

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

The Intelligent reflecting surfaces (IRSs) have been established to show the capability to enhance spectral and energy efficiency by using the passive beamforming at the IRS point and joint optimization of the active beamforming at the base station (BS). However, in the smart wireless environments in which mostly passive RISs are deplored the estimation of the channel state estimation is challenging. This paper, therefore, aims to propose a channel estimation scheme with an improved performance in comparison with some other recently proposed estimation schemes. The proposed channel estimation scheme employs the hybrid strategy to combine the improved versions of traditional Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP) compressive sensing algorithms as a basis for the proposed estimator. The proposed estimator, through computer simulations, shows improved performance when compared with the other four channel estimators that were recently documented in literature though with a slightly high computational complexity cost.

Optical flow matching with automatically correcting the scale difference of tunnel parallel photogrammetry

AbstractUsing parallel photography to model tunnels is an efficient method for real scene modelling. Aiming at the problem that the accuracy of optical flow matching in tunnel parallel photography sequence photos is severely affected by the scale deformation of stereo images, a novel optical flow matching method with automatically correcting the scale difference of tunnel parallel photography stereo images is proposed from the perspective of imaging relationships. By analysing the distribution pattern of scale difference in stereo images, a model is obtained in which the scale difference of image points is symmetrically distributed radially on the image and follows a power function growth. Introduce it into traditional optical flow matching to correct image scale differences based on the model to improve matching accuracy. The mean square error of the optical flow matching after correcting scale difference in the experiment is less than 0.3 pixels, which is at least 34.3% higher than before correction and a maximum improvement of 45.5% in the experimental results. The research result indicates that the proposed optical flow matching method with automatically correcting the scale difference has a significant effect on improving the accuracy of tunnel parallel photography image matching and modelling.

Experimental Study for the Matching of Explosives and Rocks Based on Rock Hydrophysical Properties

The study of the hydrophysical properties of rocks is indispensable for the development of hydraulic engineering, especially for blasting operations in water. Reasonable matching between explosives and rocks increases the utilization of explosive energy and improves the blasting performances. Based on the energy law in the rock blasting process, the matching relationship between explosives and rock is studied by combining experimental and theoretical methods for the hydrophysical properties of the rock itself. Firstly, the theoretical solutions for crushing-zone energy, fragmentation energy and fragment-throwing energy are derived. Subsequently, concrete blocks are prepared with four types of cement–sand ratios, and four types of emulsion explosives are used to carry out single-hole blasting tests in which a high-speed camera is used to capture the trajectory of the blasting fragments that are later collected. Finally, the crushing energy, fracturing energy and fragment-throwing energy are calculated according to the test results and the basic parameters of the used explosives and concrete models. The results show that the size and distribution pattern of blasting blocks are significantly affected by the hydrophysical properties of concrete and explosive properties; the higher the energy consumption in the rupture zone, the smaller the size of the fragments and the more uniform the distribution. Moreover, the median utilization efficiency of explosive energy on rock breaking is 26.4%, the energy consumption in the crushing zone is approximately 8.4%, that in the rupture zone is approximately 10.9%, and that in the throwing energy of fragments accounts for approximately 7.1%. It is also found that the traditional wave impedance matching theory fails to obtain the best explosive energy utilization. On the contrary, the concrete specimen had the best fracturing effect and the highest energy utilization of 30.77% when the impedance ratio of concrete to explosives is 1.479.

CFNAM-PG: Bridging Phonetic and Glyphic Information for Chinese Full Name and Abbreviation Matching Based on Simbert and DenseNet

Matching abbreviated names with their full names (full-abbr matching) plays a key role in data integration, address matching, information retrieval, and other fields. Traditional full-abbr matching technology often encounters issues related to near homophones and near homoglyphs. First, a near-homophone full-abbr matching model based on Simbert and VGG was first proposed, which integrates character and speech features, leveraging a speech recognition model and combining a brain-like cognitive learning dual-process mechanism which involves linguistic knowledge and neural network together. Second, to address the problem of near-homoglyph full-abbr matching in Chinese, a DenseNet-based model that fuses glyph structure and image features was proposed, in which statistical feature extractors are employed to extract feature vectors for glyphic features including stroke, Wubi and structural features separately. Lastly, the near-homophone model and the near-homoglyph model are coupled to work together in the full-abbr matching task, in which expert knowledge is used as a component of the feature optimizer. Experimental results showed that the integrated model significantly increased the matching accuracy to 87.5%, demonstrating a 12.3% improvement.