Integrity Evaluation Method of GBAS Based on Extremum Distribution Model

The evaluation of Ground Based Augmentation System (GBAS) integrity performance is critical to the safety of aviation users. However, such evaluation remain challenging due to low integrity risk requirements. This paper proposes an innovative GBAS integrity evaluation method under the fault-free (??0 ) hypothesis with limited samples, which is based on the mixed g-and-h distribution. The mixed g-and-h distribution can well characterize the actual distribution of the normalized error and make full use of sample information to evaluate integrity. Moreover, the credibility of the integrity evaluation result is obtained in this paper. Then, the vertical integrity performance of the GBAS at Dongying Shengli Airport in China is evaluated. The results show that the vertical Misleading Information (MI) probability is less than 2.23×10-9 /150 s and the integrity meets the requirement of GAST-C at a confidence level of at least 99%.

BackgroundThe classical linear model is widely used in the analysis of clinical trials with continuous outcomes. However, required model assumptions are frequently not met, resulting in estimates of treatment effect that can be inefficient and biased. In addition, traditional models assess treatment effect only on the mean response, and not on other aspects of the response, such as the variance. Distributional regression modelling overcomes these limitations. The purpose of this paper is to demonstrate its usefulness for the analysis of clinical trials, and superior performance to that of traditional models.MethodsDistributional regression models are demonstrated, and contrasted with normal linear models, on data from the LIPID randomized controlled trial, which compared the effects of pravastatin with placebo in patients with coronary heart disease. Systolic blood pressure (SBP) and the biomarker midregional pro-adrenomedullin (MR-proADM) were analysed. Treatment effect was estimated in models that used response distributions more appropriate than the normal (Box-Cox-t and Johnson’s Su for MR-proADM and SBP, respectively), applied censoring below the detection limit of MR-proADM, estimated treatment effect on distributional parameters other than the mean, and included random effects for longitudinal observations. A simulation study was conducted to compare the performance of distributional regression models with normal linear regression, under conditions mimicking the LIPID study. The R package gamlss (Generalized Additive Models for Location, Scale and Shape), which implements maximum likelihood estimation for distributional regression modelling, was used throughout.ResultsIn all cases the distributional regression models fit the data well, in contrast to poor fits obtained for traditional models; for MR-proADM a small but significant treatment effect on the mean was detected by the distributional regression model and not the normal model; and for SBP a beneficial treatment effect on the variance was demonstrated. In the simulation study distributional models strongly outperformed normal models when the response variable was non-normal and heterogeneous; and there was no disadvantage introduced by the use of distributional regression modelling when the response satisfied the normal linear model assumptions.ConclusionsDistributional regression models are a rich framework, largely untapped in the clinical trials world. We have demonstrated a sample of the capabilities of these models for the analysis of trials. If interest lies in accurate estimation of treatment effect on the mean, or other distributional features such as variance, the use of distributional regression modelling will yield superior estimates to traditional normal models, and is strongly recommended.Trial registrationThe LIPID trial was retrospectively registered on ANZCTR on 27/04/2016, registration number ACTRN12616000535471.

Aiming at the problems of slow speed and low accuracy in the automatic recognition of traditional robot workpieces, this paper proposes an improved workpiece recognition method based on YOLO V5. First, this paper collects photos of common workpieces by camera, creates data set independently and performs data enhancement to fill the gap of workpiece data set. Since YOLO V5 has the problems of low recognition accuracy and missed detection for small target objects, the feature extraction capability of the network is enhanced by incorporating an attention module to its backbone network. Secondly, the SIoU loss function is used to speed up the convergence speed and improve the regression accuracy, which enhances the network accuracy. Finally, the trained model is compared and evaluated with the original model. The test results show that the improved YOLO V5 target detection algorithm improves 4.58% and 3.40% in accuracy and recall, respectively, compared with the original algorithm, which can effectively avoid the phenomena of missed detection, false detection and overlap, and has significant advantages over existing methods and is more suitable for the detection needs in practical production applications.

Spectrum monitoring often demands a great quantity of spectrum data, and the massive characteristics of spectrum data make it consume a lot of resources in the process of transmission and storage. At the same time, the compressed acquisition system of spectrum data often has the problem of low reconstruction accuracy of the original data, and the reconstruction accuracy and compression performance cannot be achieved simultaneously. This paper studies the factors affecting the reconstruction error in the process of electromagnetic spectrum data utilization. In this paper, an electromagnetic spectrum compression and reconstruction framework called QRK-SVD is proposed. Aiming at the problems of slow dictionary convergence and low accuracy in dictionary learning, QRK-SVD purposely uses k-means clustering to construct the initial dictionary, which effectively improves the compression accuracy and system robustness. QRK-SVD increases the minimum singular value of sensing matrix through QR decomposition to optimize the problem of low accuracy of random observation matrix in compressed system. We designed a set of spectrum data acquisition and compression system based on QRK-SVD. It can adapt to various collection scenarios, greatly reduce the amount of data transmitted and stored, and has high reconstruction accuracy. The measured data proves that the performance of QRK-SVD is better than the traditional K-SVD framework in different data compression situations.KeywordsDictionary learningQRK-SVDElectromagnetic spectrumData compression

The evaluation for rural-urban integration plays an important role in the process of the harmonious development of rural and urban areas. The latest progress of existing researches on the Performance Evaluation of Regional Urban-rural Integration was reviewed systematically. Some local governments used the evaluation system to evaluate the rural-urban integration in their territories. The article tries to summarize the literatures about evaluation method for Urban-rural integration，to investigate the weak point about evaluation of urban-rural integration. The new problems will also be point out and the problems need to be further researched.

In order to solve the problems of low evaluation accuracy and high evaluation time-consuming of traditional business English informatization teaching level evaluation methods, this paper proposes a new business English informatization teaching level evaluation method based on BSC. Firstly, Markov chain is used to collect business English information teaching data. Based on the collected teaching data, this paper uses the Balanced Scorecard to construct the evaluation model of business English informatization teaching level, calculates the index weight, and completes the evaluation of business English informatization teaching level. The experimental results show that compared with the traditional evaluation method, the evaluation accuracy and time-consuming of this method are significantly improved.

Distributional regression represents a modern approach to regression modeling that yields the ability to simultaneously connect multiple parameters beyond the mean of any parametric response distribution to structured additive predictors that can take parametric and non-parametric forms. This thesis proposes contributions to this field in three unique ways: in 1) a framework for the visualization of distributional regression models is developed, which focuses on predicted conditional moments and the shape of the whole distribution, instead of solely relying on distributional parameters as is commonly done. It is implemented as an extensive interactive R package named distreg.vis, focused on usability. The second contribution 2) recognizes a bias in the estimation of distributional regression model coefficients of all parameters if the model equation of one parameter is incorrectly specified. A solution for two-parameter distributions based on a numerically solved system of ordinary differential equations (ODE) created with the parameters' maximum likelihood estimate (MLE) covariance matrix is outlined, implemented and tested in a simulation study. Contribution 3) fills a gap in the interpretation of fitted distributional regression models. Existing metrics for ranking the importance of variables in linear regression models are discussed, with ``relative weights'' and ``hierarchical partitioning'' standing out as the most suitable due to their robustness to the scale of covariates, the consideration of variable cross-correlation, order independency and suitability for effects with more than one degree of freedom. These metrics are subsequently extended to generalized linear models (GLM) and generalized additive models for location, scale and shape (GAMLSS) with linear predictors taking into account the possibly multi-parametric response structure and likelihood-based nature of the fitted regression models. These extensions are implemented in an R package called \texttt{vibe, providing methods compatible with several other packages. The above contributions are showcased using several datasets about wages in the Mid-Atlantic region of the USA, gym visitor numbers in Göttingen, extreme rainfall in Tasmania of Australia, patient satisfaction with a health care provider in North Macedonia and malnutrition scores in India.

The Feynman path integral has revolutionized modern approaches to quantum physics. Although the path integral formalism has proven very successful and spawned several approximation schemes, the direct evaluation of real-time path integrals is still extremely expensive and numerically delicate due to its high-dimensional and oscillatory nature. We propose an efficient method for the numerical evaluation of the real-time world-line path integral for theories where the potential is dominated by a quadratic at infinity. This is done by rewriting the high-dimensional oscillatory integral in terms of a series of low-dimensional oscillatory integrals, that we efficiently evaluate with Picard-Lefschetz theory or approximate with the eikonal approximation. Subsequently, these integrals are stitched together with a series of fast Fourier transformations to recover the lattice regularized Feynman path integral. Our method directly applies to problems in quantum mechanics, the word-line quantization of quantum field theory, and quantum gravity. Published by the American Physical Society 2025

The paper is devoted to description of the device for efficiency rise on conduction of marking operation during manufacture of large-size pipe bends. The existing method of marking applying and possibility of use of universal accessories, which displayed their low efficiency, were analyzed. The existing method of “lofting” is characterized by high labour intensity and low accuracy. Preliminary calculations of fabrication of universal accessories showed that it will be very expensive. The existing problem of low marking accuracy and high labour intensity of this process was solved owing to use of automation equipment. The device for measuring and marking, which is changeable accessories for industrial manipulating robot, was developed and described for this purpose. Architecture of the measuring and marking device and its elementary base, which allows to carry out automatic measuring of pipe bend billet, to conduct the required mathematical calculations and to apply marking, was described. The developed technique for measuring of pipe bend billet, allowing to determine the coordinates of basic points in the coordinate system of manipulating robot, was also described. Automation of transfer of the developed measuring and marking device was implemented via use of the industrial manipulating robot of Kuka Robotics company. Laboratorial example of this device was manufactured using the developed digital model. The experimental data that were presented in this paper displayed that accuracy of the laboratorial device corresponds to accuracy of marking applying via the existing technology and preliminary decrease of labour intensity was achieved about 50 %. The revealed disadvantages of construction of the laboratorial example of device were also described. Finally, the measures for improvement of accuracy of revealing the basic marking points were suggested; they include increase of a number of laser ranging devices and consequent calculation of coordinates on the base of average values of measuring data. This work was realized in the framework of the Program "Priority 2030" on the base of the Belgorod State Technological University (BSTU) named after V. G. Shukhov. The work was realized using equipment of High Technology Center at BSTU named after V. G. Shukhov.

In today's big data era, with the development of the Internet of Things (IoT) technology and the trend of autonomous driving prevailing, visual information has shown a blowout increase, but most image matching algorithms have problems such as low accuracy and low inlier rates, resulting in insufficient information. In order to solve the problem of low image matching accuracy and low inlier rate in the field of autonomous driving, this research innovatively applies spectral clustering (SC) in the field of data analysis to image matching in the field of autonomous driving, and a new image matching algorithm "SC-RANSAC" based on SC and Random Sample Consensus (RANSAC) is proposed. The datasets in this research are collected based on the monocular cameras of autonomous driving cars. We use RANSAC to obtain the initial inlier set and the SC algorithm to filter RANSAC's outliers and then use the filtered inliers as the final inlier set. In order to verify the effectiveness of the algorithm, it shows the matching effect from three angles: camera translation, rotation, and rotation and translation. SC-RANSAC is also compared with RANSAC, graph-cut RANSAC, and marginalizing sample consensus by using two different types of datasets. Finally, we select three representative pictures to test the robustness of the SC-RANSAC algorithm. The experimental results show that SC-RANSAC can effectively and reliably eliminate mismatches in the initial matching results; has a high inlier rate, real-time performance, and robustness; and can be effectively applied in the environment of autonomous driving.

The safety issue during bridge construction and operation has become a major concern for the society. This research focuses on how to conduct integral health-status assessment for bridges in operation on the basis of test and monitoring data. The result of the assessment will be the foundation of bridge maintenance, damage repair and reinforcement decision-making. Bridge state assessment origins in America, where bridge management systems were developed to give comprehensive assessment and help make daily management and maintenance decision for medium and small bridges. Afterward, different bridge health-status evaluation methods have been developed. For small and medium bridges, bridge assessment is usually conducted by applying assessment code. For complicated bridge, such code could not reflect the complexity of the structure. Another assessment method is reliability method. Reliability method is based on theory of probability and fully considers the uncertainty of structures. Analytic Hierarchy Process (AHP) is proposed in the 1970s to conduct multi-criteria decision-making combining qualitative and quantitative factors. (Saaty 2008) Bridge state assessment is a typical multicriteria decision-making problem. In the assessment model, multiple assessment indexes should be taken into consideration, including quantitative indexes and qualitative indexes. When the AHP assessment model is built and the weight of each index is derived, the evaluation value of the base indexes of the model should be derived and then be integrated by a certain relationship to get an integrated evaluation value representing the general state of the bridge. This research focuses on the evaluation value of base level indexes and the integrated evaluation method. The base level indexes of the bridge health-status assessment model are all from field test and monitoring directly. They need to be transformed to unified non-dimensional evaluation value. Considering the fuzziness and uncertainty of the evaluation process and transformation process, fuzzy grading is used to give a quantitative description of the base index’s evaluation. Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is a multiple attribute decision making method, which was developed for ranking and evaluation of limited number of alternatives (Yoon & Hwang 1995). Under AHP assessment framework, an improved fuzzy TOPSIS method has been proposed to conduct overall assessment of bridges. An integrated evaluation method is proposed by improving TOPSIS method and adapting it to bridge health-status assessment. The improvements include: ① absolutization of positive and negative ideal solution value; ② establishment of the virtual ideal solutions; ③ calculation of variable weight of base-level indexes considering the impact of the variation of the base-level indexes ④ fuzzy assessment based on level sets; ⑤ defuzzificaiton and standardization treatment of the assessment result. A case study applying the proposed fuzzy TOPSIS methodis introduced. This implementation verifies the feasibility and utility of the proposed method.

One of the initial measures to planning the conservation strategies is knowing the ecological needs of a species and its favorite habitat. Species Distribution Modeling (SDM) is an applicable tool to achieve this goal. Zarudny's spur-thighed tortoise or Iranian tortoise (Testudo graeca zarudnyi) is one of three subspecies of spur-thighed tortoises found in Iran. To assess the conflicts threatening the habitat of this subspecies, the Maximum Entropy Algorithm (MaxEnt) was used. The purpose of this study was to evaluate the integrity of the T. g. zarudnyi habitat and discuss the conflicts threatening the suitable habitat of this taxon in Iran. First, 25 ecological variables were considered to evaluate the habitat suitability, including topographic, land use land cover, and climatic layers (19 Bios). Among climatic variables, only five of them were selected using Pearson correlation analysis at the level of 75%. Finally, 11 variables contributed to running the model. The cell size were set based on Bios cell sizes to prevent biases, only one presence point kept at each cell; therefore, 108 presence points were remained out of 137 occurrence data. The obtained model had a high degree of predictability (AUC= 0.911). Results showed that Zarudny's tortoise favorites good to moderate vegetation structure. Hence, areas with higher vegetation cover and an annual average temperature of 20°C are optimal, which emphasizes the importance of landuse changes in both the range dynamics and the conservation of this subspecies in Iran. Considering the overlap of the protected areas with the suitable habitat of Zarudny's tortoise revealed that insufficient amounts of suitable habitat are protected. Hence, considering the vulnerable conservation status of Iranian tortoise, the importance of establishing more protected areas through the suitable habitat of his taxon is emphasized.

Multiple Pitch Estimation (MPE) of polyphonic music is one of the most important and difficult issues in the area of the Music Information Retrieval (MIR).The main job of MPE is to estimate both the accurate value and the number of the fundamental frequencies. In this paper, we first discuss harmonic reset algorithm and octave correction algorithm. Harmonic reset algorithm can separate overlapping harmonics of multiple notes and octave correction algorithm can correct Sub frequency and frequency doubling errors. Combined with fundamental frequency candidates, harmonic reset and octave correction modules and preprocessing and post-processing modules, this paper presents a method of multi-fundamental frequency estimation for single frame signal. Using harmonic product spectrum to solve candidate fundamental frequency sets, the computational complexity is greatly reduced. Harmonic reset and octave correction significantly improve the accuracy, thus achieving a balance between low computational complexity and high accuracy. A multi-base frequency estimation method for multi - frame signals is proposed based on a chord identifier and a hidden Markov model.

To overcome the problems of low evaluation accuracy and efficiency existing in traditional human capital value evaluation methods, this paper proposes a new innovative research method of enterprise human capital value evaluation under the background of big data. In order to improve the accuracy of human capital data mining, information extraction method is used to extract the features of human capital information. Eliminate the extreme value of human capital data at both ends of the enterprise to complete the mining of human capital data. The human capital value evaluation of enterprises is divided into four grades. According to the grade division results, combined with the human capital value evaluation index, the correlation degree between the human capital data and the evaluation grade is obtained. The human resource data ontology model is constructed, and the human capital data output control vector set is obtained by ontology mapping. Finally, the relative fuzzy approach method is used to complete the evaluation of human capital value. The experimental results show that compared with the traditional evaluation method, the evaluation accuracy and efficiency of the proposed method are greatly improved, and the highest evaluation accuracy reaches 99.7%. Therefore, the practical application performance of the proposed method is strong.

Geographical differences in blood potassium detected using a structured additive distributional regression model