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Among the directional angle calculation models of Bluetooth 5.1, the rectangular hollow array offers the advantage of shorter sampling time due to its fewer elements compared to traditional planar arrays. However, the antimultipath algorithms suitable for traditional planar arrays cannot be applied to rectangular hollow arrays. Therefore, this study proposes a virtual array filling algorithm, wherein four virtual matrices are inserted into the hollow matrix to transform the array into a uniform rectangular array. This algorithm ensures translation invariance of the rectangular array, enabling the application of the antimultipath coherent source algorithm to a rectangular hollow array. An algorithm for reconstructing Toeplitz matrices in two‐dimensional uniform planar arrays is also proposed. Through the analysis of the spatial spectrum and angle estimation results of various algorithms, the effectiveness of the signal angle of arrival estimation theory is verified. [ABSTRACT FROM AUTHOR]

Objectives. Aortic arch replacement is a major surgical intervention. This meta‐analysis aims to combine the international literature on the branch‐first technique to describe its outcomes and offer a comparison to the standard techniques. Methods. PubMed and Cochrane were searched for studies on the branch‐first technique in aortic arch surgery including those comparing this technique with the conventional ones. Mortality, neurological complications, bleeding, and renal replacement therapy were the major endpoints of interest. Subgroup analysis was performed for both single‐ and double‐armed studies. Odds ratios with 95% confidence interval were used as summary estimates. The random‐effects model was used for all outcomes. Results. Ten studies from three countries with a total of 498 patients were included. Proportional analysis of the branch‐first technique estimated mortality of (3%, 95% CI [0.02–0.05]), neurological complications of (8%, 95% CI [0.03–0.13]), re‐exploration for bleeding of (9%, 95% CI [0.05–0.13]), and renal replacement therapy of (9%, 95% CI [0.05–0.12]). This meta‐analysis revealed no significant differences between the branch‐first technique and the conventional technique in mortality (5% vs. 15%, OR = 0.32, 95% CI [0.08–1.18], p = 0.09, I2 = 40%), neurological complications (10% vs. 16%, OR = 0.53, 95% CI [0.24–1.21], p = 0.13, I2 = 0%), or renal replacement therapy (12% vs. 14%, OR = 0.72, 95% CI [0.33–1.59], p = 0.41, I2 = 0%). Conclusions. Branch‐first technique provides a promising alternative approach in aortic arch surgery with no different surgical risks and potentially reduced operative complexity. Randomised controlled trials are needed to further investigate the safety and advantages of this technique. [ABSTRACT FROM AUTHOR]

This paper proposes modified Karhunen–Loeve transform with total least square estimation of signal parameters using rotational in-variance technique (MKLT-TLS-ESPRIT) to approximate the low-frequency oscillatory modes. MKLT decreases the impact of highly correlated additive colored Gaussian noise (ACGN) from the signal by differentiating the correlation matrix w.r.t from the final time instance. A quantitative study of the suggested method with other estimation methods is used to evaluate the effectiveness of the proposed method. Monte Carlo simulations with 50,000 runs are conducted to test the robustness of the estimation scheme for MKLT-TLS-ESPRIT. The evaluation of the efficiency of the proposed method in real-time perspective, the two-area system, and New England sixty-eight bus test system has been considered. The analysis shows that the suggested methodology correctly measures the interarea modes and lowers their mean and standard deviation to a minimum value. [ABSTRACT FROM AUTHOR]

This research study proposes the inclusion of randomness or an error term in the modified Lee–Carter model, which improves the traditional Lee–Carter model for modeling and forecasting mortality risk for years in the actuarial science field. While the modified Lee–Carter model points out some of its common shortcomings , it has no distributional assumption that has been placed on the error/disturbance term. Incorporating a Gaussian distributional assumption on the error term is proposed, and then, the deep learning technique is used to obtain the parameter estimates. It is a departure from the traditional singular value decomposition estimation technique for estimating the parameters in the model. Finally, the Bühlmann credibility approach is incorporated into the model to determine its forecasting precision compared to the classical Lee–Carter model before applied in actuarial valuations. [ABSTRACT FROM AUTHOR]

Counting and detecting the pedestrians is an important and critical aspect for several applications such as estimation of crowd density, organization of events, individual's flow control, and surveillance systems to prevent the difficulties and overcrowding in a huge gathering of pedestrians such as the Hajj occasion, which is the annual event for Muslims with the growing number of pilgrims every year. This paper is based on applying some enhancements to two different techniques for automatically estimating the crowd density. These two approaches are based on individual motion and the body's thermal features. Theessential characteristic of crowd counting techniques is that they do not require a previously stored and trained data; instead they use a live video stream as input. Also, it does not require any intervention from individuals. So, this feature makes it easy to automatically estimate the crowd density. What makes this work special than other approaches in literature is the use of thermal videos, and not just relying on a way or combining several ways to get the crowd size but also analyzing the results to decide which approach is better considering different cases of scenes. This work aims at estimating the crowd density using two methods and decide which method is better and more accurate depending on the case of the scene; i.e., this work measures the crowd size from videos using the heat signature and motion analysis of the human body, plus using the results analysis of both approaches to decide which approach is better. The better approach can vary from video-to-video according to many factors such as the motion state of humans in this video, the occlusion amount, etc. Both approaches are discussed in this paper. The first one is based on capturing the thermal features of an individual and the second one is based on detecting the features of an individual motion. The result of these approaches has been discussed, and different experiments were conducted to prove and identify the most accurate approach. The experimental results prove the advancement of the approach proposed in this paper over the literature as indicated in the result section. [ABSTRACT FROM AUTHOR]

The purpose of this work is to identify a multiple-input single-output (MISO) continuous time (CT) system with various unknown time delays using sampled data. A novel technique for simultaneously estimating system parameters and various time delays may be developed using a gradient algorithm. Indeed, we propose a new formulation of the identification problem which permits defining the generalized vector containing the time delays and the dynamic parameters. The gradient optimization algorithm with exact derivatives of the objective function with respect to the adjustable parameters has been suggested as an alternative for solving this optimization problem. The identification method can be easily implemented by a filtering procedure to generate the input/output time derivatives. In this sense, the proposed method, which is based on a state variable filter, intrinsically allows data filtering and thus simplifies the use of the method in a practical case. An analysis is then presented which proves the convergence of the algorithm, and robustness issues are discussed as well. Finally, the suggested technique is evaluated on simulated instances to ensure its validity and performance. [ABSTRACT FROM AUTHOR]

The ensemble-based Kalman filter requires at least a considerable ensemble (e.g., 10,000 members) to identify relevant error covariance at great distances for multidimensional geophysical systems. However, increasing numerous ensemble sizes will enlarge sampling errors. This study proposes a modified Cholesky decomposition based on the covariance localization (CL) scheme, namely a covariance localization scheme with modified Cholesky decomposition (CL-MC). Our main idea utilizes a modified Cholesky (MC) decomposition technique for estimating the background error covariance matrix; meanwhile, we employ the tunable singular value decomposition method on the background error covariance to improve the ensemble increment and avoid the imbalance of the system. To verify if the proposed method can effectively mitigate the sampling errors, numerical experiments are conducted on the Lorenz-96 model and large-scale model (SPEEDY model). The results show that the CL-MC method outperforms the CL method for different data assimilation parameters (ensemble sizes and localizations). Furthermore, by performing one year of assimilation experiments on the SPEEDY model, it is found that the 1-day forecast RMSEs obtained by the CL are approximately equal to the 5-day forecast RMSEs of CL-MC. So, the CL-MC method has potential advantages for long-term forecasting. Maybe the proposed CL-MC method achieves good prospects for widespread application in atmospheric general circulation models. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new method of doing things that have turned traditional lives into high technology. The improvements of IoT include smart economies, smart homes, pollution control, energy efficiency, smart transportation, and intelligence in the capacity. This technology is especially crucial in smart logistics, which is a generator of country and business efficiency and is critical to economic progress. Unfortunately, the current logistics business continues to suffer from high prices and low effectiveness. The advancement of intelligent logistics provides the potential to address these issues. In addition, IoT can generate vast amounts of data and investigate the intricate links between the transactions reflected by these data with the use of a variety of mathematical analytic methods. These characteristics are beneficial to the advancement of intelligent logistics. In this paper, we present a complete overview of IoT technologies used in smart logistics. Initially, the relevant work and IoT-based level of the growth of the logistics sector from a low-carbon viewpoint are presented. Then, we outline the technological solutions for IoT in intelligent logistics. During the experimental work, it selects a technique for estimating carbon emissions to compute the total carbon emissions of the logistics industry in eight provinces in the north and south. The carbon productivity and carbon emission characteristics of China's logistics industry are then examined. In addition, it calculates the carbon emission efficiency of Fuzhou's regional logistics industry based on the BCC model. Secondly, the coordination degree model of the composite system is used to analyze the coordination degree between the low-carbon economic system and the logistics industry level in Fujian Province. Furthermore, it selects the index of the logistics subsystem and the index of the low-carbon economic development subsystem to measure the coupling and coordination effect between logistics manufacturing and the low-carbon economy in Fujian Province. [ABSTRACT FROM AUTHOR]

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets. [ABSTRACT FROM AUTHOR]

With rapid economic growth and urbanization, the accelerated increase in car ownership has brought massive pressure on urban traffic, and accurate traffic flow prediction information can provide an important basis for urban traffic dynamic planning. The existing methods have problems such as low efficiency, large error, and inability to adapt to short-term traffic changes. To solve the above problems, the CEEMDAN-SE-GWO-LSTM method was proposed in this paper. First, the traffic flow data is processed for outliers and missing values. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the traffic flow data, and sample entropy (SE) is used to reconstruct the subsequence, which is used to improve the quality of the input data. Then, the Grey Wolf Optimizer (GWO) is used to optimize the parameters of the long-short-term memory (LSTM) in order to improve the prediction accuracy and prevent the model from falling into a local optimum. Three models are used to compare with the ensemble model proposed in this paper, including back propagation neural network (BPNN), LSTM, and long-short-term memory optimized by Grey Wolf Optimizer (GWO-LSTM). Root mean square error (RMSE) is reduced by 40.9% to 66.7%; R 2 score is improved by 1.5% to 7.1%. The experimental results show that CEEMDAN-SE-GWO-LSTM has a higher prediction accuracy than the existing traffic flow prediction models. Finally, this paper uses the model prediction error to establish an interval prediction model based on the kernel density estimation theory, which enhances the generalization of the model and the practical application value. [ABSTRACT FROM AUTHOR]

Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator. However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data. Meanwhile, we are interested in forecasting performance and covariate selection in the presence of outliers. IIS method uses Autometrics techniques to estimate the covariates and outlier as the number of covariates P > n observations. Besides Autometrics, regularization techniques are a well-known method for covariate selection and forecasting in high-dimensional analysis. However, the efficiency of regularization techniques for the IIS method has remained unexplored. For this purpose, we explore the efficiency of regularization techniques for out-of-sample forecast in the presence of outliers with 6 and 4 standard deviations (SD) and orthogonal covariates. The simulation results indicate that SCAD and MCP outperform in forecasting and covariate selection with 4 SD (20% and 5% outliers) compared to Autometrics. However, LASSO and AdaLASSO select more covariates than SCAD and MCP and possess higher RMSE. Overall, regularization techniques possess the least RMSE than Autometrics, as Autometrics possesses the least average gauge at the cost of the least average potency. We use COVID-19 cross-sectional data collected from 1 July 2021 to 30 September 2021 for real data analysis. The SCAD and MCP select CRP level, gender, and other comorbidities as an important predictor of hospital stay with the least out-of-sample RMSE of 7.45 and 7.50, respectively. [ABSTRACT FROM AUTHOR]

In this research, the authors were interested in an efficiency comparison study of new adjusted nonparametric and parametric statistics interval estimation methods in the simple linear regression model. The independent variable and the error came from normal, scale-contaminated normal, and gamma distributions. Six point estimations were performed, for example, least squares, Bayesian, Jack knife, Theil, optimum-type Theil, and new adjusted Theil–Sen and Siegel methods in the simple linear regression model with 1,000 iterations. The criteria used to consider in this study were the coefficient of the confidence interval and the average width of the confidence interval used to compare and determine the optimal effectiveness for six interval estimations of the simple linear regression model. In the interval estimation for normal and scale-contaminated normal distributions of β 0 , the least squares method had the narrowest average width of confidence interval. For the interval estimation of β 1 , the Bayesian method had the narrowest average width of confidence interval in a small variance of 1, followed by the same of optimum-type Theil and new adjusted Theil–Sen and Siegel methods, and Theil method, respectively. In the interval estimation for gamma distribution of β 1 , the Bayesian method had the narrowest average width of confidence interval, followed by optimum-type Theil, new adjusted Theil–Sen and Siegel, and Theil methods, respectively. The optimum-type Theil method was good for medium sample size, while Theil and new adjusted Theil–Sen and Siegel methods were good for small and large sample sizes. Therefore, new adjusted Theil–Sen and Siegel method can be used in many situations and can be used in place of optimum-type Theil and Theil methods for nonparametric statistics interval estimation. [ABSTRACT FROM AUTHOR]

Summary: Applications such as structural health monitoring (SHM) often rely on the analysis of time‐series using methods such as state‐space models (SSM). In this paper, we propose an analytical method called the Gaussian multiplicative approximation (GMA) that is applicable to multiplicative state‐space models that are often encountered in practical SHM applications. The method enables the analytical inference of the mean vector and the covariance matrix for the product of two hidden states in the transition and/or observation models using linear estimation theory and the online estimation of model parameters as hidden states. The potential of combining the GMA and Bayesian dynamic linear models (BDLM) is illustrated through the development of (1) a generic component called online autoregressive that can estimate both the state variable and the parameter together; (2) a generic component called trend multiplicative for multiplicative seasonality model to identify non‐harmonic periodic pattern whose amplitude changes linearly with time; and (3) a generic component called double kernel regression to identify non‐harmonic periodic pattern that involves the product of two periodic kernel regression components. The SHM‐based case studies presented confirm that the GMA exceeds the performance of the existing nonlinear Kalman filter methods in terms of accuracy along with the computational cost. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a framework for fatigue monitoring on linear components of a structure subjected to unknown loads using a limited number of output‐only vibration measurements. It exploits virtual sensing techniques to estimate the dynamical responses, including virtual multiaxial strain/stress time histories. It applies joint input–state estimation algorithms to predict the dynamical response of the structural components. The resulting virtual multiaxial stress histories are used to compute the fatigue load cycles and corresponding fatigue damage. The proposed streamline approach allows the use of different available methodologies for virtual sensing and multiaxial fatigue analysis. A numerical validation example is provided using the tower of a reference wind turbine model subject to realistic wind loads. The proposed procedure can be adopted over a monitoring period to produce a global fatigue damage accumulation map from limited vibration data. It thus provides realistic fatigue damage accumulation information for a component based on the actual operational conditions of a structure. [ABSTRACT FROM AUTHOR]

In Statistics of Extremes, the estimation of the extreme value index is an essential requirement for further tail inference. In this work, we deal with the estimation of a strictly positive extreme value index from a model with a Pareto‐type right tail. Under this framework, we propose a new class of weighted Hill estimators, parameterized with a tuning parameter a. We derive their non‐degenerate asymptotic behavior and analyze the influence of the tuning parameter in such result. Their finite sample performance is analyzed through a Monte Carlo simulation study. A comparison with other important extreme value index estimators from the literature is also provided. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new general family of distributions. The submodels of the general family accommodate various shapes of pdf and hazard rate, involving decreasing, increasing, bathtub and J‐shapes. Hence, it can provide analysis for many practical datasets. Mathematical expressions for the family are obtained, including moments, moment generating and quantile functions, stochastic ordering, and entropy. Some submodels of the family are inserted based on the baseline distributions: Exponential, Gompertz, Lindley, and weight exponential distributions. Estimation of the model parameters is justified by the method of maximum likelihood. Capability of the family is shown by fitting three practical datasets to the mentioned submodels. [ABSTRACT FROM AUTHOR]

Summary: Coulomb Counting (CC) method plays an important role in the state of charge (SOC) estimation theory of lithium‐ion batteries, and a lot of improvement and optimization strategies are based on it. With the increasing demand for precise management of lithium‐ion battery systems, the performance of the traditional CC method is no longer suitable for more complex working conditions. First, the battery aging, extreme temperature, and high‐rate discharging were considered as the main influencing factors which limit the SOC estimation accuracy of the CC method, and the performance degradation mechanism of the traditional CC method under the influence of the above factors are experimentally analyzed, especially the change of battery total dischargeable capacity after aging, and the change rules of key parameters in the CC equation are analyzed. Then the initial SOC and the total dischargeable capacity of the CC method are modified and estimated respectively to realize the accurate estimation of SOC under complex working conditions, especially the accurate SOC estimation during the whole life cycle of lithium‐ion batteries. The experimental results show that the improved CC method can effectively deal with complex working conditions, and the comprehensive estimation accuracy of SOC is within 3.6%. [ABSTRACT FROM AUTHOR]

Surveillance data are key to informing decisions on the control and prevention of transboundary and emerging diseases. Here, we describe new methods for acquiring difficult to access, publicly available disease surveillance data. We use World Organisation for Animal Heath (OIE) data on African Swine Fever (ASF) outbreaks in European countries to showcase the importance of adequate disease surveillance data to inform decision‐making. The data acquired using these methods allow for large scale, geospatial outbreak mapping and estimation of summary statistics for any listed terrestrial disease in the OIE World Animal Health Information System (WAHIS) database. These techniques will make valuable epidemiological data more accessible to the scientific community, aiding further insight into the occurrence and spread of transboundary and emerging diseases in a timely manner, fulfilling an important function of disease surveillance. [ABSTRACT FROM AUTHOR]

In this article, a switched quantizer is proposed to solve the bandwidth limitation application problem for distributed wireless sensor networks (WSNs). The proposed estimator based on switched quantitative event-triggered Kalman consensus filtering (KCF) algorithm is used to monitor the aircraft cabin environmental parameters when suffering packet loss and path loss issues during the communication process for WSN. The quantization error of the novel switched quantizer structure is bounded, and the corresponding stability theory for the quantitative estimation approach is proved. Compared with other methods, the simulation results for the introduced method verify that the environmental parameters can be estimated accurately and timely and reduce the burden of network communication bandwidth. [ABSTRACT FROM AUTHOR]

Multiple input and multiple output (MIMO) radar systems have advantages over traditional phased-array radar in resolution, parameter identifiability, and target detection. However, the estimation performance of the direction of arrivals (DOAs) and the direction of departures (DODs) will be significantly degraded for a colocated MIMO radar system with unknown mutual coupling matrix (MCM). Although auxiliary sensors (AS) can be set to solve this problem, the computational cost of two-dimensional multiple signal classification (2D-MUSIC) is still large. In this paper, a new angle estimation method is proposed to reduce the computational complexity. First, a local-search range is defined for each initial angle estimation obtained by the MUSIC with AS method. Second, the new estimation of DOAs and DODs of the targets is estimated via the joint estimation theory of angle and mutual coupling coefficient in the local search area. Simulation results validate that the proposed method can obtain the same precision and have the advantage over the global searching in computational complexity. [ABSTRACT FROM AUTHOR]

Nowadays, a huge number of shield-driven tunnels with noncircular cross sections are constructed in urban areas all around the world. However, the ground displacements associated with tunneling still form a difficult issue, especially for noncircular tunnels. In this study, an analytical solution is derived to estimate the ground displacements induced by the deformations of shallow noncircular tunnels in soft ground. First, a solution for the stresses and displacements around a deep tunnel in a full plane is formulated by imposing a specified convergence pattern over the cavity boundary. Subsequently, this solution is validated using finite element simulations in a case study of an elliptical tunnel with four different convergence patterns. Afterward, the solution in the full plane is extended to a half plane using the virtual image technique to estimate the ground displacements around shallow tunnels. The solution is also validated using finite element simulations. [ABSTRACT FROM AUTHOR]

A clustering similarity particle filter based on state trajectory consistency is presented for the mathematical modeling, performance estimation, and smart sensing of nonlinear systems. Starting from an information fusion model based on the consistency principle of the spatial state trajectory, the predicted observation information of the current particle filter (original trajectory) and future multistage Gaussian particle filter (modified trajectory) are selected as the state trajectories of the sampling particles. Clustering similarity methods are used to measure the state trajectories of the sampling particles and the actual system (reference trajectory). The importance weight of a first-order Markov model is updated with the measurement results. By integrating the targeted compensation scheme of the latest measurement information into the sequential importance sampling process, the adverse effects of the particle degradation phenomenon are effectively reduced. The convergence theorems of the improved particle filter are proposed and proved. The improved filter is applied to practical cases of nonlinear process estimation, economic statistical prediction, and battery health assessment, and the simulation results show that the improved particle filter is superior to traditional filters in estimation accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

Aiming at the diversified requirements of network application QoS (Quality of Service) in the terminal equipment of Internet of Vehicles, this paper proposes a distributed congestion control strategy based on harmony search algorithm and the Throughput Evaluation Priority Adjustment Model (TEPAM) to ensure real-time transmission of high-priority data messages related to security applications. Firstly, the channel usage rate is periodically detected and the congestion is judged; then, in order to minimize delay and delay jitter as the goal, harmony search algorithm is utilized to perform global search to obtain a better solution for the transmission range and transmission rate. Secondly, packet priority and the TEPAM are applied to indicate the sending right of each packet. The data message priority and throughput percentage factor are used to express the transmission weight of each data message. Besides, the real-time evaluation of path state in MPTCP is carried out by the batch estimation theory model, which realizes the on-demand dynamic adjustment of the network congestion time window. Finally, SUMO, MOVE, and NS2 tools are used to create a VANET-like environment to evaluate the performance of the proposed congestion control strategy. Experimental results show that the proposed method is superior to other three methods in the four indicators of average delay time, average transmission rate, number of retransmissions, and packet loss rate compared with other advanced methods. [ABSTRACT FROM AUTHOR]

The new generation of smartphones, equipped with various sensors, such as a three-axis accelerometer, has shown potential as an intelligent, low-cost monitoring platform over the past few years. This paper reports the results of an analytical and experimental study on a proposed SDOF model-based noisy deconvolution (SMND) coupled with a deechoing technique to estimate pavement profiles and to modify their geometry using a smartphone inside a vehicle. In the analytical study, the acceleration response of the car was obtained, where the input was a road profile with an arbitrary pattern. Two different methods, classical band-pass filter and wavelet-denoising technique, were used for denoising the acceleration response. In a 2-step deconvolution process coupled with a deechoing technique, the pavement profile was extracted and compared with the original pavement profile, demonstrating good agreement. In the next step, a parametric study was performed to evaluate the effect of vehicle characteristics and speeds. Then, a case study was conducted in Blacksburg, VA, to evaluate the capability of the proposed method in identifying profile types such as potholes and speed bumps. The acceleration-versus-time responses in vertical direction were recorded using smartphone accelerometers located in a moving vehicle. Then, the proposed approach was applied to remove the echo and vehicle dynamics effects to obtain the pavement profiles and to modify their geometry. The results showed that the proposed approach can remove the echo and vehicle dynamics effect from the response to obtain the pavement profile even if the vehicle characteristics and speed are changed. [ABSTRACT FROM AUTHOR]

The study of water surface profiles is beneficial to various applications in water resources management. In this study, two artificial intelligence (AI) models named the artificial neural network (ANN) and genetic programming (GP) were employed to estimate the length of six steady GVF profiles for the first time. The AI models were trained using a database consisting of 5154 dimensionless cases. A comparison was carried out to assess the performances of the AI techniques for estimating lengths of 330 GVF profiles in both mild and steep slopes in trapezoidal channels. The corresponding GVF lengths were also calculated by 1-step, 3-step, and 5-step direct step methods for comparison purposes. Based on six metrics used for the comparative analysis, GP and the ANN improve five out of six metrics computed by the 1-step direct step method for both mild and steep slopes. Moreover, GP enhanced GVF lengths estimated by the 3-step direct step method based on three out of six accuracy indices when the channel slope is higher and lower than the critical slope. Additionally, the performances of the AI techniques were also investigated depending on comparing the water depth of each case and the corresponding normal and critical grade lines. Furthermore, the results show that the more the number of subreaches considered in the direct method, the better the results will be achieved with the compensation of much more computational efforts. The achieved improvements can be used in further studies to improve modeling water surface profiles in channel networks and hydraulic structure designs. [ABSTRACT FROM AUTHOR]

The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. In essence, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs as required so as to guarantee that the best one can be found within them. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance. [ABSTRACT FROM AUTHOR]

Cheng and Tang [Upper bounds on the number of columns in supersaturated designs. Biometrika, 88 (2001), 1169‐1174] provided upper bounds on the maximum number of columns, denoted by B(n,t), that can be accommodated in two‐symbol supersaturated designs for a given number, say n, of rows and a maximum correlation in absolute value, say t/n, between any two columns. Recently, Morales et al [On the maximum number of columns in supersaturated designs with smax=2, J. Combin. Des., 27 (2019), 448‐472] proved that B(n,2)=n+1 for n=14,18,22,30. However, from the 35 lower bounds for B(n,t) provided by Cheng and Tang, only 11 supersaturated designs are known to satisfy these bounds. In this article, by performing a computer search, we show that B(9,1)=7, B(13,1)=12, B(17,1)=15, B(21,1)=19, B(7,3)=15, B(9,3)=12, B(11,3)=B(13,3)=15, B(11,5)=66, and B(15,3)=17. Likewise, our search produces supersaturated designs that achieve these maximums. Each of these exact values was previously unknown. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the parameter estimation for the generalized gamma distribution based on left‐truncated and right‐censored data. A stochastic version of the expectation‐maximization (EM) algorithm is proposed as an alternative method to compute approximate maximum likelihood estimates. Two different methods to obtain reliable initial estimates of the parameters required for the iterative algorithms are also proposed. Interval estimation based on a parametric bootstrap method is discussed. The proposed methodologies are illustrated with a numerical example. Then, a Monte Carlo simulation study is used to evaluate the performance of the proposed estimation procedures and to compare with the direct optimization method and the conventional EM algorithm. Based on the simulation results, we show that the proposed stochastic EM algorithm is a useful alternative estimation method for the model fitting of the generalized gamma distribution. [ABSTRACT FROM AUTHOR]

In recent years singular spectrum analysis (SSA) has been used as a powerful technique to analyze time series, including theoretical developments and application to many practical problems. However, no inclusive theoretical approach has been discussed regarding the construction of confidence intervals for forecasts. Due to the prominent role of prediction intervals in evaluating the accuracy of forecasts in time series analysis, in this paper, we consider the topic of constructing prediction intervals for SSA. Namely, we revise the existing approaches for the vector SSA forecasting method and propose a new median‐based alternative to this algorithm where the mean in the diagonal averaging step of the SSA algorithm is replaced by the median. The results from the existing and proposed approaches are compared by considering Monte Carlo simulations and real data applications. [ABSTRACT FROM AUTHOR]

This paper originates from the interest in the distribution of a statistic defined as the ratio of independent generalized gamma random variables. It is shown that it can be represented as the product of independent generalized gamma random variables with some reparametrization. By decomposing the characteristic function of the negative natural logarithm of the statistic and by using the distribution of the difference of two independent generalized integer gamma random variables as a basis, accurate and computationally appealing near‐exact distributions are derived for this statistic. In the process, a new parameter is introduced in the near‐exact distributions, which allows to control the degree of precision of these approximations. Furthermore, the performance of the near‐exact distributions is assessed using a measure of proximity between cumulative distribution functions and by comparison with the exact and empirical distributions. We illustrate the use of the proposed approximations on the distribution of the ratio of generalized variances in a multivariate multiple regression setting and with an example of application related with single‐input single‐output networks. The proposed results ensure less computing time and stability in results as well. [ABSTRACT FROM AUTHOR]

Recently there has been a growing interest to study various estimation procedures to estimate the model parameters of the Weibull Pareto distribution (WPD). It is observed that the proposed distribution can be used quite effectively to model skewed data. They also proposed a modification of the maximum likelihood method (MML) to estimate the parameters of the WPD. The proposed methods showed a better performance from the standard maximum likelihood (ML) method. However, when the shape parameter c>>1, the modified ML method showed unforgettable large bias and standard deviation. In this article, we address some new properties and propose different methods of estimation of the unknown parameters of a WPD from the frequentist point of view. We briefly describe different frequentist approaches, namely, moments estimators, L‐moments estimators, percentile‐based estimators, least squares estimators, method of maximum product of spacings estimators, method of Cramér‐von‐Mises estimators, method of Anderson‐Darling estimators. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and compare them with modified maximum likelihood estimation from the work of Alzaatreh et al (2013a). A real data set have been analyzed for illustrative purposes. Finally, bootstrap confidence intervals are obtained for the parameters of the model based on a real data set. [ABSTRACT FROM AUTHOR]

Global solar radiation (GSR) is a critical variable for designing photovoltaic cells, solar furnaces, solar collectors, and other passive solar applications. In Nepal, the high initial cost and subsequent maintenance cost required for the instrument to measure GSR have restricted its applicability all over the country. The current study compares six different temperature-based empirical models, artificial neural network (ANN), and other five different machine learning (ML) models for estimating daily GSR utilizing readily available meteorological data at Biratnagar Airport. Amongst the temperature-based models, the model developed by Fan et al. performs better than the rest with an R 2 of 0.7498 and RMSE of 2.016 2 MJm − 2 d − 1 . Feed-forward multilayer perceptron (MLP) is utilized to model daily GSR utilizing extraterrestrial solar radiation, sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity as inputs. ANN3 performs better than other ANN models with an R 2 of 0.8446 and RMSE of 1.4595 MJm − 2 d − 1 . Likewise, stepwise linear regression performs better than other ML models with an R 2 of 0.8870 and RMSE of 1.5143 MJm − 2 d − 1 . Thus, the model developed by Fan et al. is recommended to estimate daily GSR in the region where only ambient temperature data are available. Similarly, a more robust ANN3 and stepwise linear regression models are recommended to estimate daily GSR in the region where data about sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity are available. [ABSTRACT FROM AUTHOR]

The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two-parameter ridge-type modified M-estimator (RTMME) based on the M-estimator to deal with the combined problem resulting from multicollinearity and outliers. Through theoretical proofs, Monte Carlo simulation, and a numerical example, the proposed estimator outperforms the modified ridge-type estimator and some other considered existing estimators. [ABSTRACT FROM AUTHOR]

This study proposes an efficient and fast method of scanning (e.g., television (TV) scan) coupled with digital image processing technology to replace the conventional slow-scan mode as a standard model of acquisition for general-purpose scanning electron microscopy (SEM). SEM images obtained using the proposed method had the same quality in terms of sharpness and noise as slow-scan images, and it was able to suppress the adverse effects of charging in a full-vacuum condition, which is a challenging problem in this area. Two problems needed to be solved in designing the proposed method. One was suitable compensation in image quality using the inverse filter based on characteristics of the frequency of a TV-scan image, and the other to devise an accurate technique of image integration (noise suppression), the position alignment of which is robust against noise. This involved using the image montage technique and estimating the number of images needed for the integration. The final result of our TV-scan mode was compared with the slow-scan image as well as the conventional TV-scan image. [ABSTRACT FROM AUTHOR]

The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Different factors make the image dehazing process cumbersome like unbalanced airlight, contrast, and darkness in hazy images. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. The proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. PWM is very effective as it suppresses low variations present in images that are affected by the haze. We have proposed a method in this article that performs well as compared to state-of-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image. The qualitative and quantitative analysis carried on standard image databases proves its robustness in terms of the standard performance evaluation metrics. [ABSTRACT FROM AUTHOR]

The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy. [ABSTRACT FROM AUTHOR]

The paper investigates the influence of structural components of a composite material on the strain values measured by using an embedded optical fiber with Bragg gratings. The effect of composite plies and intermediate epoxy layers on the transfer of deformations from the measured object to the optical fiber was studied taking into account various methods of the fiber attachment and surrounding media configurations. A numerical estimation of the effect of the longitudinal and transverse components of the strain tensor on the wavelength of the reflected spectrum is performed. [ABSTRACT FROM AUTHOR]

Background. Evaluation of diagnostic assays and predictive performance of biomarkers based on the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are vital in diagnostic and targeted medicine. The partial area under the curve (pAUC) is an alternative metric focusing on a range of practical and clinical relevance of the diagnostic assay. In this article, we adopt and extend the min-max method to the estimation of the pAUC when multiple continuous scaled biomarkers are available and compare the performances of our proposed approach with existing approaches via simulations. Methods. We conducted extensive simulation studies to investigate the performance of different methods for the combination of biomarkers based on their abilities to produce the largest pAUC estimates. Data were generated from different multivariate distributions with equal and unequal variance-covariance matrices. Different shapes of the ROC curves, false positive fraction ranges, and sample size configurations were considered. We obtained the mean and standard deviation of the pAUC estimates through re-substitution and leave-one-pair-out cross-validation. Results. Our results demonstrate that the proposed method provides the largest pAUC estimates under the following three important practical scenarios: (1) multivariate normally distributed data for nondiseased and diseased participants have unequal variance-covariance matrices; or (2) the ROC curves generated from individual biomarker are relative close regardless of the latent normality distributional assumption; or (3) the ROC curves generated from individual biomarker have straight-line shapes. Conclusions. The proposed method is robust and investigators are encouraged to use this approach in the estimation of the pAUC for many practical scenarios. [ABSTRACT FROM AUTHOR]

An overview of current debates and contemporary research devoted to modeling decision making processes and their facilitation directs attention to techniques based on pairwise judgments. At the core of these techniques are various judgment consistency measures which, in a sense, control the prioritization process which leads to the establishment of decision makers' unknown preferences. If judgments expressed by decision makers were perfectly consistent (cardinally transitive), all available prioritization techniques would deliver the same solution. However, human judgments are consistently inconsistent, as it were; thus the preference estimation quality significantly varies. The scale of these variations depends, among others, on the chosen consistency measure of pairwise judgments. That is why it seems important to examine relations among various consistency measures and the preferences estimation quality. This research reveals that there are consistency measures whose performance may confuse decision makers with the quality of their ranking outcome. Thus, it introduces a measure which is directly related to the quality of the preferences estimation process. The main problem of the research is studied via Monte Carlo simulations executed in Wolfram Mathematica Software. The research results argue that although the performance of examined consistency measures deviates from the exemplary ones in relation to the estimation quality of decision makers preferences, solutions proposed in this paper can significantly improve that quality. [ABSTRACT FROM AUTHOR]

Cement asphalt mortar (CA) disengagement of a ballastless track will induce changes of dynamic response of a passing vehicle, which can accordingly be used to estimate the disengagement degree. In this paper, a novel method called CA mortar disengagement degree estimation algorithm (CMDEA) is proposed through an analysis of wheel acceleration of a passing vehicle. The disengagement degree estimation is transformed into an optimization problem by regarding the CA mortar disengagement degree as a parameter of a vehicle-track coupling model. An improved genetic algorithm with a shifting window is employed for the parameter optimization, which is split into a number of phases and whose initial values are given in terms of a priori probabilities. The accuracy and robustness of the estimation are discussed, and the results are compared with regular genetic algorithm. The simulation results show that CMDEA can estimate CA mortar degrees with an acceptable accuracy. Meanwhile, the proposed algorithm has the advantages of a lower error value and much shorter computation time. Moreover, the robustness of the algorithm under different vehicle speeds, track irregularities, and signal noise levels is also verified. [ABSTRACT FROM AUTHOR]

Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant. [ABSTRACT FROM AUTHOR]

The objective of this study is to monitor the water content of soil quickly and accurately using a UAV. Because UAVs have higher spatial and temporal resolution than satellites, they are currently becoming more useful in remote sensing areas. We developed a water content estimation equation using the color of the soil and suggested a calibration method for field application. Since the resolution of the images taken by the UAV is different according to the altitude, the water content estimation formula is developed by using the images taken at each altitude. In order to calibrate the color difference according to lighting conditions, the calibration method using field data were proposed. The results of the study showed an altitude-specific estimation equation using RGB values of the UAV image through linear regression. The appropriate number of field data needed for calibration for site application of the estimation equation was found between 4 and 10. On-site application results of the proposed calibration method showed RMSE accuracy of 1.8 to 2.9%. Thus, the water content estimation and calibration method proposed in this study can be used in effectively monitoring the water content of soil using UAVs. [ABSTRACT FROM AUTHOR]

The neural autoregressive distribution estimator(NADE) is a competitive model for the task of density estimation in the field of machine learning. While NADE mainly focuses on the problem of estimating density, the ability for dealing with other tasks remains to be improved. In this paper, we introduce a simple and efficient reweighted scheme to modify the parameters of the learned NADE. We make use of the structure of NADE, and the weights are derived from the activations in the corresponding hidden layers. The experiments show that the features from unsupervised learning with our reweighted scheme would be more meaningful, and the performance of the initialization for neural networks has a significant improvement as well. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) is one of the indispensable elements of physiological health characteristics and a significant indicator for predicting and diagnosing hypertension and cardiovascular diseases. This paper proposes a two-domain fusion model to estimate BP continuously from pulse wave acquired with a pressure sensor. Method. The optimal external pressure applied on the pressure sensor is first determined in order to capture pulse wave in the radial artery. The captured pulse wave is then processed in both the time and frequency domains via filtering and fast Fourier transform. Finally, a set of features are extracted from these two domains and input into a neural network along with blood pressure values measured by a commercial sphygmomanometer for training. The model is then tested on new data for accuracy evaluation. Results. The proposed two-domain fusion method achieved a high degree of accuracy in measuring blood pressure. [ABSTRACT FROM AUTHOR]

Cylindrical fitting is an essential step in Large Process Pipeline's measurement process, and precision of initial values of cylindrical fitting is a key element in getting a correct fitting result. In order to get well initial values, covariance matrixes of all points in cylinder's three-dimensional laser scanning point cloud should be firstly established to estimate normals of all points, and then cylinder's axis vector can be calculated by using least squares method. Secondly, remaining parameters' initial values of the cylinder can be got by coordinate transformation. Finally, Levenberg-Marquardt algorithm is used in iterative optimization process to get fitting result by using the above values as initial values. Experiments demonstrate that this method can get precise initial values of cylindrical fitting and improve the accuracy and speed of cylindrical fitting. [ABSTRACT FROM AUTHOR]

The existing positioning methods that use received signal strength indication (RSSI) and channel state information (CSI) may suffer from multipath and shadowing in a complex wireless environment, which can result in more positioning errors. This paper proposes a method for accurate multilabel positioning in the non-line-of-sight (NLOS) environment. First, the position is roughly estimated using the orthogonal variable spreading factor (OVSF-TH) algorithm, which can automatically match the signal interference. The ultra-wideband (UWB) spectral density and pulse amplitude in the time domain are used to determine the direction of the label and enhance estimation of the mobile label direction. Then, the location of the tag is obtained by triangulation, and a coordinate-based coordinate estimation method is proposed to calculate the relative displacement of multiple tags to determine the label position. Finally, by setting up a real experimental environment, the influence of the number of base stations on the accuracy and the performance of the localization method under different circumstances are analyzed. The theoretical analysis and experimental results show that the method is simple to deploy, inexpensive, and very accurate in terms of positioning, having a clearly effective indoor positioning accuracy. Compared with other existing positioning methods, this method can achieve more accurate positioning. Moreover, it has important theoretical and practical applicability because of the reliability and accuracy of indoor positioning in an NLOS environment. [ABSTRACT FROM AUTHOR]

As a new type of secondary battery, lithium-ion battery is widely used in the aerospace industry with the advantages of long lifetime, high energy density and low pollution, etc. In this paper, we focus on the problem of offline and online life prediction for satellite lithium-ion batteries. Firstly, based on the NASA laboratory battery dataset, a Wiener process with time-scale transformation is used to capture battery capacity fading, and then the battery reliability point and interval estimation equation are derived, respectively. Secondly, by analyzing the charge and discharge profiles of the batteries in orbit environments, an accurate capacity prediction model is proposed based on the partial charging curves. Finally, the Bayesian framework is used to perform capacity degradation model online updating, and the analytical expression of residual life distribution is derived to achieve RL prediction for in-orbit satellite lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Summary The available power of vehicle‐mounted batteries needs to be real‐time predicted to accommodate prospective driving demands of overtaking, gradient climbing, constant‐speed cruising, and regenerative braking. Generally, battery power capabilities are limited by multiple constraints, for example, terminal voltage, current, state‐of‐charge (SoC), and State‐of‐Energy (SoE). This paper constructs SoC and SoE estimators resorting to the square‐root central difference Kalman filter (SR‐CDKF) and an equivalent circuit model with online updated parameters. In addition, a battery thermal evolution model is formulated, whereby the ordinarily ignored temperature constraint is taken into account. Based on above achievements, a battery power prediction scheme conforming to multiple constraints is realized. Finally, experimental verifications are conducted on a LiFePO4 battery pack subject to consecutive Federal Urban Driving Schedule profiles. The SR‐CDKF‐based SoC and SoE estimators give accurate results under different conditions. In contrast to the conventional PNGV‐HPPC method, the proposed method behaves with more reliability and robustness at different time horizons, temperatures, and aging levels. The assessment results justify the effectiveness of the battery modeling and algorithm utilization efforts. An online lightweight identifier is developed to update battery model parameters, and a battery thermal evolution model is formulated to forecast battery temperature; square‐root central difference Kalman filter is exploited for battery SoC and SoE estimation; battery state‐of‐available‐power is predicted limited by multiple constraints including temperature. [ABSTRACT FROM AUTHOR]

The classification of a new item may be viewed as a matter of associating it with the class where it is the least at odds w.r.t. the elements already in the class. An oddness measure of an item with respect to a multiset, applicable to Boolean features as well as to numerical ones, has been recently proposed. It has been shown that cumulating this measure over pairs or triples (rather than larger subsets) of elements in a class could provide an accurate estimate of the global oddness of an item with respect to a class. This idea is confirmed and refined in the present paper. Rather than considering all the pairs in a class, one can only deal with the pairs whose an element is one of the nearest neighbors of the item, in the target class. The oddness evaluation computed on this basis still leads to good results in terms of accuracy. One can take a step further and choose the second element in the pair also as another nearest neighbor in the class. Although the method relies on the notion of neighbors, the resulting algorithm is far from being a variant of the classical k‐nearest neighbors approach. The oddness with respect to a class computed only on the basis of pairs made of two nearest neighbors leads to a low complexity algorithm. Experiments on a set of UCI benchmarks show that the classifier obtained can compete with other well‐known approaches. [ABSTRACT FROM AUTHOR]