Average Estimation Research Articles

Sensor placement considering the observability of traffic dynamics: On the algebraic and graphical perspectives

In this paper, we present a new sensor location model that aims to maximize observability of link densities in a dynamic traffic network described using a piecewise linear system of ordinary differential equations. We develop an algebraic approach based on the eigenstructure to determine the sensor location for achieving full observability with a minimal number of sensors. Additionally, a graphical approach based on the concept of structural observability is developed. By exploiting the special property of flow conservation in traffic networks, we derive a simple analytical result that can be used to identify observable components in a partially observable system, which is the main contribution of this paper. The graphical and algebraic properties of observability are then integrated into a sensor location optimization model considering a wide range of traffic conditions. Through numerical experiments, we demonstrate the good performance of our sensor deployment strategies in terms of the average observability and estimation errors.

On-chip based power estimation for CMOS VLSI circuits using support vector machine

Power estimation has a major impact on the reliability of very-large-scale integration (VLSI) circuits. As a results power estimation is highly needed in VLSI circuits at the early stages. One of the evident challenges in integrated circuit (IC) industry is development and investigation of techniques for the reduction of design complexity due to the growing process variations and reduction of chip manufacturing turnaround time. Under these conditions, the higher design levels of average power estimation before the chip manufacturing process is highly essential for the calculation of power budget and to take the necessary steps for the reduction of power consumption. Over the years, most of the approaches were designed to estimate the power usage, however, most of the conventional techniques are time consuming, resource-intensive and largely manual. Machine learning techniques have received much attention in many of the engineering applications and are capable for modelling the complex systems through historical data. Hence, in this work on chip-based power estimation for complementary metal-oxide-semiconductor (CMOS) VLSI using support-vector machine (SVM) is presented to estimate the power. The SVM is employed to estimate the usage of power at runtime. The performance of this model is evaluated in terms of Power usage, delay, data accuracy and error rate.

Multi-polygenic score prediction of mathematics, reading, and language abilities independent of general cognitive ability.

Specific cognitive abilities (SCA) correlate genetically about 0.50, which underpins general cognitive ability (g), but it also means that there is considerable genetic specificity. If g is not controlled, then genomic prediction of specific cognitive abilities is not truly specific because they are all perfused with g. Here, we investigated the heritability of mathematics, reading, and language ability independent of g (SCA.g) using twins and DNA, and the extent to which multiple genome-wide polygenic scores (multi-PGS) can jointly predict these SCA.g as compared to SCA uncorrected for g. We created SCA and SCA.g composites from a battery of 14 cognitive tests administered at age 12 to 5,000 twin pairs in the Twins Early Development Study (TEDS). Univariate twin analyses yielded an average heritability estimate of 40% for SCA.g, compared to 53% for uncorrected SCA. Using genome-wide SNP genotypes, average SNP-based heritabilities were 26% for SCA.g and 35% for SCA. We then created multi-PGS from at least 50 PGS to predict each SCA and SCA.g using elastic net penalised regression models. Multi-PGS predicted 4.4% of the variance of SCA.g on average, compared to 11.1% for SCA uncorrected for g. The twin, SNP and PGS heritability estimates for SCA.g provide further evidence that the heritabilities of SCA are not merely a reflection of g. Although the relative reduction in heritability from SCA to SCA.g was greater for PGS heritability than for twin or SNP heritability, this decrease is likely due to the paucity of PGS for SCA. We hope that these results encourage researchers to conduct genome-wide association studies of SCA, and especially SCA.g, that can be used to predict PGS profiles of SCA strengths and weaknesses independent of g.

Regular Expressions Avoiding Absorbing Patterns and the Significance of Uniform Distribution

Although regular expressions do not correspond univocally to regular languages, it is still worthwhile to study their properties and algorithms. For the average case analysis one often relies on the uniform random generation using a specific grammar for regular expressions, that can represent regular languages with more or less redundancy. Generators that are uniform on the set of expressions are not necessarily uniform on the set of regular languages. Nevertheless, it is not straightforward that asymptotic estimates obtained by considering the whole set of regular expressions are different from those obtained using a more refined set that avoids some large class of equivalent expressions. In this paper we study a set of expressions that avoid a given absorbing pattern. It is shown that, although this set is significantly smaller than the standard one, the asymptotic average estimates for the size of the Glushkov automaton for these expressions does not differ from the standard case. Furthermore, for this set the asymptotic density of [Formula: see text]-accepting expressions is also the same as for the set of all standard regular expressions.

Basic aspects of meta-analysis. Part 2

Meta-analysis combines the results of several scientific studies to obtain a summary quantitative estimation of effect size in order to compare the results of several studies and to identify patterns among them and possible sources of biases. Since the research teams, patients, research protocol, clinical guidelines and time intervals are often different between original scientific studies all these sources of difference can influence the results of each study, causing statistical heterogeneity. Meta-analyses are the highest level of credibility within evidence-based medicine, as they allow us to take into account the influence of many confounding factors and publication biases on the true effect size. Understanding the possible sources of erroneous conclusions in papers will allow researchers to analyze the results of such studies correctly and properly plan their own experiments. In this article the reader will be introduced to methods for identifying and quantifying hidden heterogeneity, such as subgroup analysis and meta-regression. In addition, the reader will learn how to calculate effect size in studies, estimate publication bias mathematically and graphically, and incorporate this estimate into the overall average effect size estimate in meta-analyses.

Depth estimation of pipe wall thinning using multifrequency reflection coefficients of T(0,1) mode-guided waves with supervised multilayer perceptron

This study entailed the development of a novel method for estimating the depth of wall thinning of pipes using multifrequency (30–65 kHz) reflection coefficients (MRCs) of the T(0,1) mode-guided waves and a multilayer perceptron (MLP). First, this study established why MRCs are a critical feature of the input layer of the MLP for the defect depth estimation of wall thinning. Further, a mathematical model that can quickly collect large amounts of training data was used to calculate the reflection waveforms. The depths of artificial and actual wall thinning were estimated using the MLP based on the MRCs and the mathematical model. Experiments were conducted using the T(0,1) mode-guided waves to obtain the MRCs for 21 artificial and 6 actual wall thinnings to estimate the defect depths. A maximum of 8347 training data points were prepared using the mathematical model. Because the optimization of the MLP strongly depended on the initial weights and biases, 100 random initial values were prepared to evaluate the average estimations and their standard deviations. The classification scheme of the MLP was used, with classification step widths of 0.5 and 0.25 mm. The correct answer rates for the 21 artificial defects were 93%, with a tolerance of ±0.5 mm for the 0.5 mm classification scheme; those for the 0.25 mm classification scheme were 89%. For the six actual defects, the correct answer rates were 100% with a tolerance of ±0.5 mm for both the 0.5 and 0.25 mm classification schemes. Sufficiently high correct answer rates were obtained in all the cases.

Monocular Pose Estimation Method for Automatic Citrus Harvesting Using Semantic Segmentation and Rotating Target Detection.

The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation and rotating target detection to estimate the pose of a single culture. First, Faster R-CNN is used for grab detection to identify candidate grab frames. At the same time, the semantic segmentation network extracts the contour information of the citrus fruit to be harvested. Then, the capture frame with the highest confidence is selected for each target fruit using the semantic segmentation results, and the rough angle is estimated. The network uses image-processing technology and a camera-imaging model to further segment the mask image of the fruit and its epiphyllous branches and realize the fitting of contour, fruit centroid, and fruit minimum outer rectangular frame and three-dimensional boundary frame. The positional relationship of the citrus fruit to its epiphytic branches was used to estimate the three-dimensional pose of the citrus fruit. The effectiveness of the method was verified through citrus-planting experiments, and then field picking experiments were carried out in the natural environment of orchards. The results showed that the success rate of citrus fruit recognition and positioning was 93.6%, the average attitude estimation angle error was 7.9°, and the success rate of picking was 85.1%. The average picking time is 5.6 s, indicating that the robot can effectively perform intelligent picking operations.

The Level of Social Intelligence and its Relationship with professional Competencies of School Principals in Jordan

The current study aimed to reveal the level of social intelligence among school principals in Bani Kinana District in Jordan, and its relationship to the level of their professional competencies. The researcher used the descriptive analytical method, where the study sample consisted of (98) individuals. To achieve the objectives of the study a "questionnaire" was prepared and used to collect its data after verifying its validity and reliability, and using the SPSS program to analyze its data. The results of the study showed: The level of social intelligence among the principals of public schools in the Bani Kenana District, from their viewpoint was at medium level. And the level of professional competencies came at medium level. There is a positive, statistically significant correlation at the level (α ≤ 0.05) between the average estimates of the sample members on the social intelligence level scale and their estimates on the professional competency scale. There are no statistically significant differences at the level (α ≤ 0.05) between the average estimates of the sample members on all areas of the level of social intelligence & professional competencies among the principals of public schools in the Bani Kenana District according to the different study variables (gender, educational qualification, years of administrative experience). In light of the results, the study came out with several recommendations, including: - Number of training courses and programs for the development and activation of social intelligence among school principals. - Work to develop the professional competencies of school principals so that they can perform their duties and work tasks with distinction and mastery, which is positively reflected on the outputs that achieve the desired and desired goals of the educational process.

$L^{p}$ improving properties and maximal estimates for certain multilinear averaging operators

In this article we focus on L^{p} estimates for two types of multilinear lacunary maximal averages over hypersurfaces with curvature conditions. Moreover, we give a different proof for the bilinear lacunary spherical maximal functions. To obtain our results, we make use of the L^{1} -improving estimates of multilinear averaging operators. We also obtain L^{p} -improving estimates for certain multilinear averages by means of the nonlinear Brascamp–Lieb inequality.

Личностные предикторы педагогической оценки способностей у студентов-актеров

<p>One of the main way for the study of acting can be considered the process of training student-actors, in which the mechanisms and techniques of creating an image and assuming a role are unfolded and highlighted. Special attention in the study of this problematic should be paid to the factors contributing to the mastery of the profession. It is assumed that personal characteristics play a key role in the issue of successful training of a student-actor, because it is the actor's personality that can be considered as the main tool that the actor has in the art of acting transformation. The aim is to identify personal predictors of pedagogical assessments of acting abilities among student actors. Psychodiagnostic study using the following techniques: “16 personality factors” by R. Kettell and “Short portrait questionnaire of the Big Five” by M.S. Egorova and O.V. Parshikova, the scale of average assessments of acting abilities. It was revealed that two personality traits are associated with the pedagogical assessment of the acting skills of an actor student: “consciousness” (B5-10) and “sensitivity” (I, 16PF). They also contribute to this average estimate. In this regard important factors for the success of mastering the acting profession are consciousness, organization, focus on maintaining relationships in the learning process, as well as the sensitivity of the student actor to internal and external changes, the richness of his emotional experience.</p>

Indicator metrics and temporal aggregations introduce ambiguities in water scarcity estimates

Water scarcity is a global challenge affecting billions of people worldwide. This study systematically assesses differences in the estimation of the global population exposed to water scarcity based on 7 water scarcity indicators and 11 Environmental Flow Requirements (EFR) evaluated at various spatial and temporal resolutions. All indicators show an increase in water scarcity since 1901. However, considering monthly average water scarcity estimates spatially aggregated at the basin scale found 35% less population exposed than estimates based on a distributed grid over the landscape. Estimates temporally disaggregated to consider water scarcity for at least one month a year found 50% (tenfold) larger population exposed compared to average monthly (annual) estimates. The study illustrates that estimates of the impacts of water scarcity are an artifact of how water scarcity is defined and calculated. This suggests caution is needed when relying on a single method and emphasizes the importance of considering the diversity of factors that can influence estimates of impact when assessing water scarcity.

In-situ grouting rate prediction of semi-flexible pavement based on a novel composite dielectric constant model and ground-penetrating radar

Semi-flexible pavement (SFP) is characterized by injected cementitious grout within porous asphalt (PA) matrixes to improve anti-rutting performance. The grouting rate is a key factor to ensure longevity and performance of SFP that require precise and non-destructive in-situ measurements. No practical approach has been built in both literature and field applications. This research proposed an automatic method for determining the SFP grouting rate, namely the Leng-Wang-Sui (LWS) model, a novel SFP dielectric constant prediction model derived from electromagnetic (EM) mixing theory. The validity of the proposed method was substantiated through a sensitivity analysis, numerical simulations of meso-scale ground-penetrating radar (GPR)-SFP models, and laboratory tests. Results verify the method’s capability to accurately predict the in-situ grouting rate of SFP, demonstrating an average estimation error below 7 %. A practical workflow of using GPR in conjunction with the LWS model for on-site assessments of SFP grouting rate was recommended for real-world applications.

Bootstrapping cascaded random matrix models: Correlations in permutations of matrix products.

Random matrix theory is a useful tool in the study of the physics of multiple scattering systems, often striking a balance between computation speed and physical rigour. Propagation of waves through thick disordered media, as arises, for example, in optical scattering or electron transport, typically necessitates cascading of multiple random matrices drawn from an underlying ensemble for thin media, greatly increasing the computational burden. Here we propose a dual pool based bootstrapping approach to speed up statistical studies of scattering in thick random media. We examine how potential matrix reuse in a pool based approach can impact statistical estimates of population averages. Specifically, we discuss how both bias and additional variance in the sample mean estimator are introduced through bootstrapping. In the diffusive scattering regime, the extra estimator variance is shown to originate from samples in which cascaded transfer matrices are permuted matrix products. Through analysis of the combinatorics and cycle structure of permutations we quantify the resulting correlations. Proofs of several analytic formulas enumerating the frequency with which correlations of different strengths occur are derived. Extension to the ballistic regime is briefly considered.

Kriging Interpolation for Constructing Database of the Atmospheric Refractivity in Korea

This paper presents a Kriging interpolation method for constructing a database of atmospheric refractivity in Korea. To collect as much data as possible for the interpolation, meteorological data from 120 regions of Korea, including both land and sea areas, are examined. Then, the normalized atmospheric refractivity Nn is calculated for an altitude of 0 m, because the refractivity tends to vary depending on the altitude of the observation site. In addition, the optimal variogram model to obtain the spatial correlation required for the Kriging method is investigated. The estimation accuracy of the Kriging interpolation is compared with that of the inverse distance weighting (IDW) and the bi-linear methods. The average and maximum estimation errors when using the Kriging method are 0.24 and 1.32, respectively. The result demonstrates that the Kriging method is more suitable for the interpolation of atmospheric refractivity in Korea than the conventional methods.

Determination of Micro-Insurance Premium Price for Fisheries Using Poisson-Exponential Aggregate Distribution Approach

Engaging in pond aquaculture is currently an attractive option amid the high demand for fish in the market. Entrepreneurial opportunities in the pond fish farming sector are increasingly open, although the risk of crop failure remains, both due to weather factors and the livestock process. Crop failure can have a significant financial impact on pond aquaculture cultivators. Therefore, it is necessary to have special insurance to protect against financial losses due to risks that can occur, namely Fisheries Micro Insurance. Microinsurance is a type of insurance product that is specifically designed for low-income people, offering simple, accessible, economically priced features and administration, and a fast compensation settlement process. The focus of this research is to calculate premium prices by applying an aggregate risk model approach. The data used is the number of incidents and the amount of losses due to crop failure in shrimp pond cultivation in Pandeglang Regency in the period of January 1, 2019-January 1, 2021. Data on the number of events follows the Poisson distribution, while data on the magnitude of losses follows the Exponential distribution. Furthermore, the Maximum Likelihood Estimation (MLE) method is used to calculate the parameter estimation. The average and variance estimates of the aggregate risk are used to determine the amount of premiums. The result of the premium selection in this study is IDR 42,005,600. The amount of premium reflects the collective premium resulting from the calculation based on the standard deviation principle.

Estimation of Infrared Stellar Flux Based on Star Catalogs with I-GWO for Stellar Calibration

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands.

A Novel Three-Parameter Nadarajah Haghighi Model: Entropy Measures, Inference, and Applications

The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences: notably, lifetime, medical, and financial sciences. This paper introduces a heavy-tailed Nadarajah Haghighi model by compounding the heavy-tailed family and Nadarajah Haghighi distribution. The model obtained has three parameters that account for the scale and shape of the distribution. The proposed distribution’s fundamental characteristics, such as the probability density, cumulative distribution, hazard rate, and survival functions, are provided, several key statistical properties are established, and several entropy information measures are proposed. Estimation of model parameters is performed via a maximum likelihood estimator procedure. Further, different simulation experiments are conducted to demonstrate the proposed estimator’s performance using measures like the average estimate, the average bias, and the associated mean square error. Finally, we apply our proposed model to analyze three different real datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing models.

Analyzing possible seismic velocity changes in Mexico City using seismic noise interferometry

The seismic response of the Mexico City basin has received plenty of attention as one of the most relevant cases of ground motion amplification. A recent study showed possible velocity variations of the deposits in both the lake bed and firm ground. A clear drop in seismic velocity was observed during the 2017.09.19 earthquake (Mw 7.1) in addition to more subtle seismic velocity changes related to seasonal temperature and precipitation. We use continuous seismic noise data recorded in a 29 broadband seismograph network in and around Mexico City to investigate Rayleigh wave group velocity structure and velocity changes. We estimate Green's functions (GF) between station pairs using seismic noise cross-correlation. An average estimate is computed for each of three five-day periods of recording. GFs are dominated by a Rayleigh pulse at low frequencies (between 0.1 and 0.2 Hz). Velocity changes are investigated through the comparison between GF's for different five-day periods. No velocity variations are observed. Station pairs separated at a distance smaller than 20 km also show some Rayleigh wave energy at higher frequencies, between 0.3 and 1.2 Hz. We repeated the analysis in a higher frequency band (up to 5 Hz) and show that again no velocity variation can be observed. In addition, we used horizontal-to-vertical spectral ratios computed for both earthquake and seismic noise records to identify possible non-linear effects during the 2017 earthquake. The results showed that significant non-linear effects during that event are to be discarded. Finally, we present preliminary results of a tomography of Rayleigh wave group velocity at the center of the array. Lateral structure variations below the geotechnical zonation are significant. Our data will be useful to constrain the structure of the volcanic deposits of the Transmexican Volcanic Belt (TVB).

On propensity score matching with a diverging number of matches

Abstract This paper re-examines the work of Abadie & Imbens (2016) on propensity score matching for average treatment effect estimation. We explore the asymptotic behaviour of these estimators when the number of nearest neighbours, M, grows with the sample size. It is shown, while not surprising, but technically nontrivial, that the modified estimators can improve upon the original fixed M-estimators in terms of efficiency. Additionally, we demonstrate the potential to attain the semiparametric efficiency lower bound when the propensity score admits some special structures, echoing the insight of Hahn (1998).

An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography.

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.