Matching Method Research Articles

Monocular visual detection of coal flow rate in scraper conveyor based on template matching background differencing

Abstract The monitoring of coal flow is a crucial aspect of the intelligent regulation and control of comprehensive mining equipment. In recent years, machine vision technology has become a mainstream method for quickly and efficiently extracting coal flow information. However, the majority of research in this field has focused on belt conveyors, with relatively limited investigation into the use of this technology with scraper conveyors. In order to address the need for monitoring coal flow in scraper conveyors, a monocular visual detection method of coal flow rates based on template matching-background differencing is proposed. First, the region of interet in the images captured using a monocular camera mounted at a specific location is quickly identified using an enhanced template matching method. Second, the image motion region is segmented using interframe and background differencing. Finally, the coal flow rate is calculated on the basis of the number of pixel points in the segmented image. Experimental verification is performed using scraper conveyor test bench and real underground data. The results demonstrate that the proposed coal flow detection method is capable of achieving real-time detection of coal flow in scraper conveyor and provides a theoretical basis for the monitoring of coal flow of the scraper conveyor.

A Multi-Criteria Decision Making for Employee Selection Using SAW and Profile Matching

Multi-criteria decision making process has been one of the fastest growing areas during the last decades depending on the change in the business sector. Multi-criteria decision making is the most important branch of operation research by which people make complex decision daily life. The major steps in the decision- making approach are selecting the most preferred alternative for the decision-maker, ranking alternatives in order of importance for selection problems, and screening alternatives for the final decision. Employees are an important element of a company that determines its progress. Conventional (manual) recruitment methods are vulnerable to non-technical factors, such as frequent duplication or invalid data. In this study, simple additive weighting and profile matching are proposed to solve the employee selection problem. This study was conducted at the (UPT) Career Development and Entrepreneurship Universitas Brawijaya Malang using data collected from written test selection in 2019. The effectiveness of both methods was analyzed using confusion matrix. The SAW method provides an accuracy rate of 94.7%, a precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, profile matching method obtained the Accuracy rate of 90.4%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. Thus, it can be concluded that both methods have high accuracy values accompanied by high precision values when used for the selection process. This system can also effectively reduce the bias rate of the same data very well, as can be seen from the high recall and F-measure rates.

Design and implementation of Golden Eagle optimized cascaded PI and LQR controller for PFC SEPIC converter in EV charging

This research article focuses on designing and implementing a single ended primary inductor converter (SEPIC) for an AC-DC converter, followed by an electric vehicle (EV) charger. It improves the high-power factor at the AC supply with minimum harmonic distortion. The Golden Eagle optimization techniques are adapted to optimize the tuning of Proportional-Integral (PI) and Linear Quadratic Regulator (LQR) controller parameters for better converter performance. The optimization is designed based on the eagle knowledge of hunting methods at various angles of spiral trajectories in capturing the animal. The SEPIC converter is designed and derived from the state space model by state space averaging, and the reduced model is obtained through the moment matching method to reduce computational complexity. The Golden Eagle Optimize the parameters of KP and KI of the PI controller and weighing matrix Q of the linear quadratic controller. The fitness function of the proposed optimization is the sum of the Integral Absolute error (IAE) and Integral Square error (ISE). The proposed optimization is implemented using MATLAB/SIMULINK software, and the simulation outcomes demonstrate an improved settling time, fast recovery against input and output variations, Total Harmonic Distortion (THD) of 1.75 %, and enhanced stability.

The impact of the kingpin strategy on extortion and kidnapping

The ‘kingpin’ strategy focuses on dismantling criminal groups by apprehending their leaders. However, existing evidence indicates that removing a criminal leader may lead to a surge in organised crime-related homicides, and to spatial displacement of organised crime violence. This study explores the strategy’s impact on other organised crimes, specifically extortion and kidnapping. Analysing Mexican data from 2011 to 2015, we employ a novel matching method for cross-sectional time-series data to assess how leadership removals affect the incidence of extortion and kidnapping, and whether there are any displacement effects. Our results reveal a significant rise in extortions within 6 months of a leadership removal in a municipality, while kidnappings show no significant change. Notably, no spatial displacement effects were observed in neighbouring municipalities post-removal. These findings underscore the unintended consequences of the kingpin strategy, emphasising the need for alternative policies to address organised crime-related violence.

The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.

Assessment of ISRO’s S-band polarimetric Doppler Weather Radar (DWR) at SHAR with IMD-DWR and GPM-Ku radar

ABSTRACT This paper describes the first assessment of ISRO’s indigenously developed S-band Polarimetric Doppler Weather Radar (DWR) at Sriharikota High Altitude Range (SHAR), Sriharikota (13.66°N, 80.23°E). The assessment is done during the passage of a tropical cyclone ‘Phethai’ and four different cases of Mesoscale convective systems. A comparison of reflectivity measured from SHAR-DWR with India Meteorological Department (IMD) DWR located at Chennai (13.07°N, 80.28°E) and Ku-band space-borne radar of the Global Precipitation Mission (GPM) is done using the common volume matching method. A good comparison is observed between SHAR-DWR with IMD-DWR and GPM-Ku radar in both space and height. The vertical structure of the cyclone is provided using Contour Frequency by Altitude Diagrams (CFAD) which show the vertical extent up to an altitude of 16 km with a reflectivity between 25 and 30 dBZ. Maximum occurrence of 30 dBZ occurs below 6 km. Bright band signatures show ~ 25 dBZ between 4 and 5 km depicting the stratiform precipitation of the cyclone. Further rain rate is estimated using both reflectivity and the polarimetric product which compared well with rain estimated from IMD-DWR and rainfall observed from rain gauges. Rain rate is observed from 5 to 35 mm hr−1. Hydrometer classification during the passage of tropical cyclone ‘Phethai’ shows the presence of rain below 4 km and graupels, ice crystals, and large droplets above.

The application of matching pursuit spectral blueing in post-stack seismic frequency enhancement

The calculation of the spectral blueing operator in the traditional spectral blueing method has singularity, which leads to poor performance in post-stack seimic frequency expansion. To this end, a frequency spreading technique based on matching pursuit (MP) and spectral blueing is proposed. Through time–frequency analysis processing, it is shown that the seismic signal extracted by matching tracking method has good stability and higher resolution. The specific process of the method in this paper firstly uses the matching tracking method to accurately divide the post-stack seismic data into multiple frequency-division seismic bodies; then, in the process of calculating the spectral blueing ization operators for each frequency band, the weighting idea is used to calculate the weights of the optimized spectral blueing ization operators for each frequency band based on the differences in energy in different frequency bands; finally, the optimized spectral blueing operator is convolved with seismic reflection coefficients to obtain high-resolution seismic data. The actual test results of poststack seismic data have proven that the frequency raising method proposed in this paper is superior to the traditional spectral blueing ization algorithm, greatly improving the high-frequency component information of poststack seismic data. After frequency extension, there are more seismic events and higher resolution. Finally, the practicability and rationality of the seismic data after frequency extraction are verified by a series of operations such as attribute extraction, well seismic calibration and inversion.

The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 − 2N1M0 breast cancer

ObjectiveTo explore the high-risk factors affecting the prognosis of pT1 − 2N1M0 patients after mastectomy, establish a nomogram prediction model, and screen the radiotherapy benefit population.MethodThe clinical data of 936 patients with pT1 − 2N1M0 who underwent mastectomy in the fourth hospital of Hebei Medical University from 2010 to 2016 were retrospectively analyzed. There were 583 patients received postmastectomy radiotherapy(PMRT), and 325 patients without PMRT. Group imbalances were mitigated using the propensity score matching (PSM) method, and the log-rank test was employed to compare overall survival (OS) and disease-free survival (DFS) between the cohorts. The efficacy of PMRT across various risk groups was evaluated using a nomogram model.ResultThe median follow-up period was 98 months, Patients who received PMRT demonstrated significantly improved 5-year and 8-year OS and DFS compared to those who did not (P < 0.001). Multivariate analysis revealed that age, primary tumor site, positive lymph node, stage, and Ki-67 level independently influenced OS, while age, primary tumor site, and stage independently affected DFS. PMRT drastically enhanced OS in the high-risk group (P = 0.001), but did not confer benefits in the low-risk and intermediate risk groups (P = 0.057, P = 0.099). PMRT led to a significant improvement in disease-free survival (DFS) among patients in the intermediate and high-risk groups (P = 0.036, P = 0.001), whereas the low-risk group did not experience a significant benefit (P = 0.475).ConclusionAge ≤ 40 years, tumor located in the inner quadrant or central area, T2 stage, 2–3 lymph nodes metastasis, and Ki67 > 30% were the high-risk factors affecting the prognosis of this cohort of patients. In OS nomogram, patients with a risk score of 149 or higher who received PMRT exhibited improved OS. Similarly, in DFS nomogram, patients with a risk score of 123 or higher who received PMRT demonstrated enhanced DFS.

OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Achieving efficient electromagnetic absorption in multifunctional carbon nanotube aerogels by manipulating radialized network structure

The rapid advancements in communication technology have greatly propelled the widespread applications of artificial intelligence (AI). While AI technology facilitates society, it also brings the unavoidable electromagnetic hazards and the issue of disposal of electronic plastic wastes. So, utilizing electronic plastic wastes to constructing carbon-based electromagnetic wave (EMW) absorption materials, are highly promising. However, how to use CNTs from plastic wastes to build good EMW absorption performance aerogels is a critical challenge. In this work, highly porous and well-interconnected network aerogels constructed by radialized CNT arrays are rationally designed under the guidance of grave-to-cradle strategy, exhibiting altered impedance matching and multiple EMW attenuation methods. Theoretical calculations suggest that a well-designed heterojunction interface structure can enhance the conductivity and interface polarization of CNTAs. The CNT aerogels (CNTAs) realize broad absorption bandwidth covering 8.48 GHz with a minimum reflection loss of −59.5 dB. Moreover, the radialized arrays network of CNTAs also endows it with electromagnetic protection, thermal management, self-cleaning, and air purification functions. These inspiring achievements of multifunctional CNTA-2 provides new help for the safe and stable promotion of the AI era.

Hedge funds network and stock price crash risk

Utilizing a dataset from 2013 to 2022 on China’s listed companies, we explored whether a hedge fund network could help explain the occurrence of Chinese stock crash. First, this study constructs a hedge fund network based on common holdings. Then, from the perspective of network centrality, we explore the impact of hedge fund network on stock crash risk and its mechanisms. Our findings suggest that companies with greater network centrality experience lower stock crash risk. Such results remain valid after alternating measures, using the propensity score matching method, and excluding other network effects. We further document that the centrality of hedge fund network reduces crash risk through two channels: information asymmetry and governance monitoring. In addition, the negative impact of hedge fund network centrality on stock crash risk is more pronounced for non-SOEs firms. In summary, our research shed light on the important role of hedge fund information network in curbing stock crash.

Multi-line laser scanning reconstruction with binocularly speckle matching and trained deep neural networks

A multi-line laser scanning system for 3D topography measurement is proposed. This method not only has the advantages of high precision of laser scanning technology, but also has high reconstruction efficiency. In this paper, speckle reconstruction technique, multi-line laser technique and Biocular reconstruction technique are used to construct a 3D reconstruction system, and test equipment is built, and the problems existing in the system establishment process are actually studied. In order to solve the problem of mismatching in binocular multi-line laser matching, a method to sort out the correspondence of multiple laser lines in binocular images based on speckle matching results is proposed. In order to optimize the multi-line laser matching effect, a speckle matching network based on deep learning is proposed, which integrates the grayscale images of the left and right cameras as supplementary information, and takes the speckle image and grayscale image as the input of the network model to obtain more accurate and edge-complete matching results. Finally, the matching results of the multi-line laser and the camera calibration parameters were used to reconstruct the object point cloud. Experimental results show that the proposed speckle matching method can make binocular multiline laser point cloud reconstruction more robust and stable than the traditional method, and through the accuracy analysis of the system, it is proved that the average measurement accuracy of the proposed method can reach 0.05 mm.

Research on alarm of coal content in gangue based on binocular vision and YOLACT segmentation network

ABSTRACT The coal content in the gangue of coal preparation plants cannot be detected online, especially in the jigging sorting process. There is a large time lag in manual testing. An approach based on the YOLACT segmentation network and binocular vision was proposed. First, the YOLACT segmentation network was used to identify coal and gangue. At the same time, the coal and gangue particle regions are segmented from the background of the bucket elevator. A binocular matching method based on deep learning was proposed to obtain better disparity maps and establish better volume models of coal and gangue particles. A stereo-matching global fusion network model was also presented by improving the Pyramid Stereo Matching Network. Finally, the coal and gangue volume parameters were combined with their respective empirical densities to predict the coal content in gangue of the bucket. When the load in the bucket elevator is small, the average prediction error of coal content in gangue is 9.19%. When the load is large i.e. the coal and gangue are stacked in multiple layers, it is not appropriate to calculate the coal content in gangue due to serious particle coverage. Therefore, a fuzzy alarm strategy was proposed based on actual needs. The situation of coal content in gangue was divided into three levels: normal, warning, and alarm. It meets the alarm requirements for coal content in gangue in most industrial plants.

Region-guided transformer for remote sensing image captioning

ABSTRACT Remote sensing image acquisition is an essential way to obtain information. However, research on remote sensing images mainly focuses on object detection or image classification. The emergence of remote sensing image captioning (RSIC) has enabled understanding and inference of remote sensing images, thus attracting considerable attention. There are still challenges in RSIC: the features used in RSIC are mostly based on grid features, and this form of features makes it difficult for the model to determine the main description targets. Hence, a more effective cross-modal matching method is needed for better text generation. Thus, we propose a region-guided transformer in response to the aforementioned issues. We extracted region features to enhance the ability of the model to focus on the main targets. To address the issue of information loss caused by region feature extraction, we proposed environment features to supplement background information. To improve the matching between text and image features, we propose a region-guided decoder that enhances the model's perception of different features through a weighted cross-attention mechanism. Meanwhile, we introduce region-guided information to guide the text-generation process. The effectiveness and superiority of our model have been demonstrated through extensive experiments.

The impact of startup subsidies on survival, growth, and stakeholder value

ABSTRACT Although the effects of subsidies on the survival, financial performance, and behavioral changes of startups have been extensively studied, their influence on economic value creation and distribution to stakeholders remains underexplored. To address this gap and mitigate publication bias, we examined the entire number of subsidies received by all Spanish startups at founding (51,495) over a decade, analyzing their outcomes 5 years after inception. We employed statistical matching methods controlling for the startups’ founding conditions. The study revealed that subsidies have a positive impact on startups’ growth and provide a significantly higher likelihood of survival; however, no significant effect on profitability was observed. Receiving a subsidy should be considered alongside other factors in explanatory models of business survival. Subsidies are associated with increased economic value creation and distribution to employees but not to the government through taxes. These findings underscore the importance of understanding the long-term consequences of subsidies on stakeholders.

Linking community-acquired images to national data in the absence of interoperable systems and unique identifiers

Objective &amp; ApproachRoutine optometry examinations, which may include retinal images, have been free to everyone in Scotland since 2006. Images and patient data are stored locally on non-interoperable systems, usually without unique identifiers. The Scottish Collaborative Optometry-Ophthalmology Network for e-research (SCONe) aimed to create a secure, longitudinal repository collating community-acquired images linked to national, routinely-collected healthcare data. We developed bulk export processes to retrieve images and patient data from current and archived systems and a series of deterministic and probabilistic algorithms to match images to patients. ResultsWe successfully exported images and patient data from several commonly used systems. Exact matching of available data between image and clinical systems within practices linked 78% of images (IQR 71.2-84.1). Our algorithms increased match rate to 90% (IQR 83.5-98.6) of images, efficiently reducing risk of systemic bias in the cohort. Match method is included in the repository metadata to facilitate quality assurance checks after national linkage and pseudonymisation. To date over 600k images have been processed and delivered to the National Safe Haven for linkage to healthcare data. Conclusions &amp; ImplicationsBreakthrough research into early detection and pre-symptomatic disease can be facilitated by harnessing community-acquired retinal images which capture normal ageing and disease progression. Rather than await interoperability and widespread use of unique identifiers, this has been achieved through innovative solutions to real-world issues around time-pressured clinical data entry and non-standardised data storage. The SCONe repository continues to grow. It is currently supporting ocular disease prediction research and offers the potential for much more.

Public interest or individual benefit: biased perception toward Chinese redistributive policies

PurposeRedistributive policies aim to reduce income disparities and improve social equity. This study investigates whether redistributive effects that successfully diminish objective income inequality also effectively alter people’s perceptions of inequality.Design/methodology/approachUtilizing data from the 2018 China Household Income Survey (CHIP), comprising 56,167 individuals, this study applies ordered probability regression (Oprobit) and ordinary least squares (OLS) for analysis. To address potential biases in estimates, we employed the generalized propensity score matching (GPSM) method to estimate the treatment effect of transfer income on perceptions of inequality.FindingsThe results indicate that while China’s redistribution policies effectively reduce income disparities, they do not improve perceptions of inequality. Individuals exhibit biased attitudes toward redistributive policies. Specifically, perceptions of inequality are insensitive to the overall redistributive effect; the relationship is negative among the poor but positive among the rich. This contradictory pattern may be attributed to perceived income losses among the rich and gains among the poor.Social implicationsThe findings have important implications for policy development. Redistribution policies should not only aim to mitigate income disparities but also address and improve people’s perceptions of inequality.Originality/valueExisting literature has largely overlooked the impact of redistribution on perceived income inequality. This study represents an early effort to explore whether redistributive policies that reduce income inequality also influence people’s perceptions of inequality.

The Impact of Forestry Technological Innovation on the Welfare of Farm Households Managing Jujube Forests (Ziziphus jujuba Mill.) in the Lüliang Mountains of the Yellow River’s Middle Reaches

Jujube (Ziziphus jujuba Mill.) makes up a traditional characteristic industry with ecological significance in the Lüliang Mountain of middle reaches of the Yellow River (LMMRYR). However, low economic efficiency has reduced local farm households’ willingness to continue jujube cultivation, threatening the sustainable maintenance and development of jujube forests and the ecological environment. In response, Lüliang City implemented a technological innovation program, that is, the Jujube Forest High Grafting and Optimization Program (JFHGOP), in 2018. Based on survey data from 302 local farm households, an empirical analysis using propensity score matching and ordinary least squares methods revealed that the program significantly enhanced the economic, ecological, and social benefits for participating farm households, improving their overall welfare. Robustness tests confirmed these findings, and a heterogeneity analysis showed varied impacts across different dimensions. The program improved welfare through government support and cooperatives’ assistance. To further promote green development and farm households’ welfare, recommendations include advancing forestry innovation technology, supporting small farm households with policy, capital, and technology, optimizing subsidy mechanisms, supporting new business entities, and promoting cooperation and benefit-sharing among stakeholders.

Establishing HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method

Establishing HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method

An Underwater Stereo Matching Method: Exploiting Segment-Based Method Traits without Specific Segment Operations

Stereo matching technology, enabling the acquisition of three-dimensional data, holds profound implications for marine engineering. In underwater images, irregular object surfaces and the absence of texture information make it difficult for stereo matching algorithms that rely on discrete disparity values to accurately capture the 3D details of underwater targets. This paper proposes a stereo method based on an energy function of Markov random field (MRF) with 3D labels to fit the inclined plane of underwater objects. Through the integration of a cross-based patch alignment approach with two label optimization stages, the proposed method demonstrates features akin to segment-based stereo matching methods, enabling it to handle images with sparse textures effectively. Through experiments conducted on both simulated UW-Middlebury datasets and real deteriorated underwater images, our method demonstrates superiority compared to classical or state-of-the-art methods by analyzing the acquired disparity maps and observing the three-dimensional reconstruction of the underwater target.