Error Distribution Function Research Articles

Statistical inference for innovation distribution in ARMA and multi-step-ahead prediction via empirical process

The kernel distribution estimator (KDE) is proposed based on residuals of the innovation distribution in the autoregressive moving-average (ARMA) time series. The deviation between KDE and the innovation distribution function is shown to converge to the Brownian bridge, leading to the construction of a Kolmogorov–Smirnov smooth simultaneous confidence band for the innovation distribution function. Additionally, an empirical cumulative distribution function (CDF) based on prediction residuals is introduced for the multi-step-ahead prediction error distribution function. This empirical process weakly converges to a Gaussian process with a specific covariance function. Furthermore, a quantile estimator is derived from the empirical CDF of prediction residuals, and multi-step-ahead prediction intervals (PIs) for future observations are established using these estimated quantiles. The PIs achieve the nominal prediction level asymptotically for the finite variance ARMA model. Simulation studies and analysis of crude oil price data confirm the validity of the asymptotic theory.

High Accuracy Localization Scheme Using 1-Bit Side Information: Achievability from a GDoP Perspective

In this paper, we provide a novel methodology for high-precision positioning that utilizes 1-bit additional information, which applies to various positioning techniques. The proposed approach leverages binary information to indicate if a user is within a specified space of interest and refines the estimated location information outside this area. By matching the estimated locations outside the area of interest with the valid location information within, this methodology corrects the positional data obtained through any arbitrary positioning technique, aligning the estimated positions with the intended spatial boundaries. Performance analysis metrics, such as Average Positioning Error (APE) and Cumulative Distribution Function for positioning coverage, were employed to assess the effectiveness of the proposed methods. Numerical simulations demonstrate how the proposed method enhances the averaged positioning accuracy, significantly outperforming the conventional time of arrival method. Furthermore, the proposed positioning correction methodology demonstrates validated feasibility applicable to an arbitrary existing positioning method.

Distributed optimal operation of PV-storage-load micro-grid considering renewable and load uncertainties

Micro-grid is one of the important carriers for renewable energy integration. Optimizing the operation mode of micro-grid can improve economic benefits and reduce risks brought by uncertainties. To maximize the economic benefits of photovoltaic-storage-load micro-grid, a chance-constrained optimal operation model considering renewable and load uncertainties is proposed in this paper. Firstly, the photovoltaic (PV) power and load forecast error distribution function based on fitting method is obtained. Then, a probabilistic power balance constraint based on chance constrained theory is established to reflect risks brought by uncertainties. To protect the information privacy of different entities, the alternating direction multiplier method (ADMM) with self-adaptive parameters is used to the proposed optimal model in a distributed way. Case study demonstrates that the proposed model can achieve a profit and risk balance of micro-grid scheduling by setting different confidence levels, and the information privacy of micro-grid operator and users is well protected.

A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system.

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture.

To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method.

Routing Optimization of Unmanned AVS for Localization of Wireless Emitters in Outside Locations

To control illegal radio waves, methods of locating unknown emitters are used. Location methods using ground sensors suffer from location accuracy degradation in environments where the distance between the emitter and the sensor is not line of sight (NLoS). Therefore, research is being done to improve location accuracy by using Unmanned Aerial Vehicles (UAVs) as sensors to ensure a line-of-sight (LoS) condition. However, UAVs can fly freely in the sky, making it difficult to optimize flight paths based on particle swarm optimization (PSO) for efficient and accurate localization. This article examines the optimization of UAV flight paths to achieve highly efficient and accurate external localization of unknown emitters through two approaches, a circular orbit and a free path, respectively. Our numerical results reveal the improved location estimation error performance of our proposed approach. In particular, when evaluating at the 90th percentile of the cumulative error distribution function (CDF), the proposed approach can achieve an error of 28.59 m with circular orbit and 12.91 m with free path orbit, compared to the case of the conventional fixed sensor. whose location estimation error is 55.02 m.

Удовлетворенность медицинской помощью в Российской Федерации: факторы, влияющие на оценку.

One of the primary indicators of the efficiency of the healthcare system is the patient’s experience and his individual evaluation of the medical care given. With the development and launch of the Federal project “Modernization of primary health care”, a methodology for assessing the satisfaction of the population with medical care has been formed. The survey evaluates not only the general indicator of public satisfaction with medical care, but also a number of individual domains and parameters of respondents. The aim of the study: was to determine the relationship between the level of satisfaction with medical care and its domains with the gender and age characteristics of respondents, the timing of receiving medical care, the place of residence of the study participants and preferences in choosing medical organizations. Materials and methods. The data were obtained by conducting a survey by medical insurance organizations operating in 85 constituent entities of the Russian Federation. The satisfaction level was calculated as the ratio of the number of respondents who chose the answers “Mostly satisfied” and “Absolutely satisfied” to the total number of respondents who answered the corresponding question, translated into a percentage. To test the hypothesis about the impact of factors on the level of satisfaction, a generalized linear mixed model with a binomial error distribution function was used. Results. The total number of respondents is 254488. The maximum level of satisfaction is 45,8% (April 2023), the minimum is 40,0% (September 2022). Living in rural areas and the private type of medical organization were factors that increase the level of both overall satisfaction (OSH=1,12 and OSH=2,81) and all its domains. The age of the respondents was inversely proportional to the specified parameters. In men, compared with women, the chance of getting into the group satisfied with the general expectation and the solution of the problem increased by 1,04 and 1,09 times, respectively (p<0,001), and in the group satisfied with the comfort in a medical organization – decreased (OR=0,97, p<0,001). Conclusion. The data obtained during the study allowed us to determine the relationship between the level of satisfaction with medical care and its domains with the gender and age characteristics of respondents, the timing of receiving medical care, the place of residence of study participants and preferences in choosing medical organizations, which can become the basis for the development of measures aimed at achieving and maintaining a high level of the parameter under study.

Model updating of rotor system based on the adaptive Gaussian process model using unbalance response

For predicting the dynamic responses of rotor system with high accuracy, an adaptive Gaussian process model-based model updating method for rotor system using the unbalance responses is proposed. To utilize the data from multiple sensors, multi-channel data fusion is adopted to construct the objective function of the model updating. The fusion feature is the mean of the cumulative distribution function of the response errors of each channel. The Gaussian process model is used to describe the mapping relationship between the selected parameters and fusion features. An adaptive sampling strategy is developed to reduce the computation cost of repeated finite element calculation. Based on Sobol sequence sampling, new sampling parameters are added near the sample parameter with a larger error in the predicted value. The performance of the proposed method is validated by a gas-generator rotor and a dual-disks rotor. Results show that the unbalance responses of the updated model show good agreement with the exact responses and measured responses. The maximum error of the support parameters for the gas-generator rotor is 3.56%. For the dual-disks rotor, the discrepancies of the first three modal frequencies between the updated model and the measured model are below 1%, 2.5% and 5%. The proposed method is effective for model updating of the rotor system.

Pragmatic multicentre stepped-wedge cluster randomised trial to investigate the effectiveness of community-based falls prevention programme for older adults with falls risk in Singapore: a protocol paper

IntroductionFalls are an important public health issue with consequences that include injuries, quality of life reduction and high healthcare costs. Studies show that falls prevention strategies are effective in reducing...

Development of real-time monitoring BLE-LoRa positioning system based on RSSI for non-line-of-sight condition

Indoor positioning has become popular in this decade and is used to locate users or objects in indoor environments. This is because global positioning system (GPS) is not efficient for indoor use due to the multipath fading effect. This research is about development bluetooth low energy (BLE) indoor positioning system with the aid of long range (LoRa) network and guideline on selection of the BLE beacons. Next, positioning systems are developed consisting of BLE beacons, a transceiver of hybrid BLE-LoRa module, a LoRa receiver and Raspberry Pi as real-time monitoring. The received signal strength indicator (RSSI) and BLE Mac address from BLE beacons received via LoRa network are analyzed using the positioning algorithm designed in MATLAB. The positioning algorithm incorporates distance estimation, filter implementation and trilateration technique. The estimated location is analyzed with the root mean square error (RMSE) and cumulative distribution function (CDF). According to the results, implementing the filter reduces the positioning accuracy error, achieving 90% accuracy of positioning error less than 1.20 meters for the whole testbed. Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.

Research on Fingerprint and Hyperbolic Fusion Positioning Algorithm Based on 5G Technology

With the development of the Internet of Things technology, higher requirements are put forward for the positioning accuracy of objects. This paper presents an indoor fusion positioning algorithm based on the 5th Generation Mobile Communication Technology (5G), which effectively solves two problems. The first is that fingerprint positioning is susceptible to environmental changes and results in inaccurate fingerprint matching. The second is the problem of the hyperbolic positioning algorithm based on the line-of-sight fluctuating too much in complex indoor environments. This paper uses a 5G flexible subcarrier interval of Orthogonal Frequency Division Multiplexing (OFDM) to significantly reduce the time delay error of Time Of Arrival (TOA), and an improved genetic algorithm and the weighted hyperbolic algorithm are used to estimate the optimal position coordinates. In the offline database establishment stage of fingerprint positioning, the Channel State Information-reference signal (CSI-RS) of multiple-beam sets provides high-dimensional information for subsequent training and prediction. The online stage cooperates with the improved residual network model to make predictions. Finally, the positioning information and the error distribution function generated by the two positioning processes are simultaneously used as the input of the Kalman filter to obtain the precise position coordinates. The simulation results show that in complex indoor scenes where line-of-sight propagation and non-line-of-sight propagation paths are mixed, the accuracy of this method can reach below 0.82 m. Thus, the positioning accuracy is significantly improved compared with other methods, which can meet most indoor scene positioning needs.

Reduced-Complexity Decimeter-Level Bluetooth Ranging in Multipath Environments

In this paper, we investigate enhanced super-resolution range estimators with decimeter-level accuracy for multi-antenna and multipath Bluetooth systems. To enhance the traditional MUSIC range estimator for a two-way frequency-hopping Bluetooth channel model, we apply forward-backward averaging and bandwidth extrapolation using Burg’s algorithm to improve ranging accuracy, which is limited by the used Bluetooth bandwidth and the quality of the estimated sample covariance matrix. For the multi-antenna case, we compare the Summed Antenna Processing and Individual Antenna Processing methods to process multiple-antenna Bluetooth channel measurements and enhance the range estimation accuracy compared to the single-antenna case. In addition, we investigate a sparsity-aware range estimator which exploits the sparsity of Bluetooth channel impulse response and achieves comparable ranging accuracy to the enhanced MUSIC estimator but at a much lower computational complexity. We apply the greedy Orthogonal Matching Pursuit algorithm to heuristically solve the sparsity-constrained optimization problem for Bluetooth ranging. Furthermore, we evaluate the computational complexity of our investigated Bluetooth range estimators with two complexity-reduction techniques to further reduce the complexity of MUSIC range estimator. Moreover, we analyze the Cramer-Rao Lower Bound (CRLB) on unbiased range estimation using the frequency-hopping Bluetooth channel model and derive a new insightful CRLB expression for a two-path channel model. Finally, we evaluate the Root-Mean-Square Error and Empirical Cumulative Distribution Function performance of our investigated range estimators both on simulated and real-world Bluetooth data that we collected in line-of-sight (LOS) and non-line-of-sight (NLOS) multipath scenarios. Our proposed enhancements on the range estimators improved the ranging accuracy by 58% for our collected Bluetooth data.

Associations of Adverse Childhood Experiences with Frailty in Older Adults: A Cross-Sectional Analysis of Data from the Canadian Longitudinal Study on Aging

Introduction: Frailty in older adults, characterized by a decline in multiple physiological systems and increasing vulnerability to loss of independence, disability, and death, is a public health priority in developed countries. Etiology of frailty extends across the lifespan and may begin in early life, but empirical evidence for this association is scarce. In this study, we examined whether adverse childhood experiences (ACEs) are associated with frailty in later life. Methods: We conducted a cross-sectional analysis of data for a population-based sample of 27,748 adults aged 45–85 years from the Canadian Longitudinal Study on Aging. The frailty index (FI) was computed with 76 health-related characteristics of physical and cognitive performance, self-rated health, chronic conditions, visual and hearing ability, activities of daily living, and well-being. Self-reported exposure to ACEs included physical, emotional, and sexual abuse, neglect, and witnessing intimate partner violence prior age of 16 and parental death, divorce, and living with a family member with mental illness prior age of 18. Generalized linear regression models with gamma error distribution and identity link function, adjusted for age and sex, were used to examine associations of each ACE type and the number of ACE types (0, 1, 2, or 3+) reported by an individual with FI. All models were adjusted for income, education, smoking, and alcohol consumption in sensitivity analysis. Results: Individuals exposed to ACEs had elevated levels of FI (mean = 0.13, SD = 0.09) than those unexposed, with the largest difference observed for neglect (B [95% CI]: 0.05 [0.04, 0.06]) and the smallest for parental death and divorce (0.015 [0.01,0.02]). The ACE count was associated with frailty in a graded manner, with the FI difference reaching 0.04 [0.037, 0.044] for participants exposed to 3+ ACE types. The association between ACEs and frailty tended to be stronger for women than men and for men aged 45–64 years than older men. Conclusions: Our study supports previous studies showing that exposure to ACEs is associated with frailty in adults. Our findings suggest that screening for ACEs involving childhood maltreatment may be useful for identifying individuals at risk of frailty and prevention of ACEs may have long-term benefits for healthy aging.

Oracle-efficient estimation for functional data error distribution with simultaneous confidence band

Kolmogorov-Smirnov (K-S) simultaneous confidence band (SCB) is constructed for the error distribution of dense functional data based on kernel distribution estimator (KDE). The KDE is computed from residuals of B spline trajectories over a smaller number of measurements, whereas the B spline trajectories are computed from the remaining larger set of measurements. Under mild and simple assumptions, it is shown that the KDE is a uniformly oracle-efficient estimator of the error distribution, and the SCB has the same asymptotic properties as the classic K-S SCB based on the infeasible empirical cumulative distribution function (EDF) of unobserved errors. Simulation examples corroborate with the theoretical findings. The proposed method is illustrated by examples of an EEG (Electroencephalogram) data and a stock data.

Clinical Profile of Low Vision in Children

Introduction: Visual impairment in children is more common in developing countries like Nepal. A low vision service has been found to be effective in significantly improving their overall development and quality of life. The main aim of this study was to determine the causes of low vision in pediatric population along with their refractive error distribution and visual functions. Methods: A descriptive cross sectional study was carried out in Tribhuvan University, BP Koirala Lions Center for Ophthalmic Studies. A total of 50 low vision children were under went detail low vision examination. They were selected through purposive sampling. Data was analyzed by using the descriptive and inferential statistics with SPSS version 19.0. Results: The study findings showed, most common cause of visual impairment in low vision children was refractive error (20%), followed by congenital cataract (18%) and macular dystrophy (16%). The most commonly prescribed low vision device for distance was telescope and for near was spectacle magnifier. There was average acuity improvement of five lines in distance visual acuity with low vision devices. Conclusion: The study concluded that refractive error and congenital cataract being the commonest cause of low vision in children, which reflect poor accessibility of care service among Nepalese children. Refractive error, the major cause of visual impairment could have been managed even in primary eye care center in Nepal. Children with impairment have potential visual acuity that can be improved with low vision services.

Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

Indoor positioning systems has become popular in this era where it is a network of devices used to locate people or object especially in indoor environment instead of satellite-based positioning. The satellite-based positioning global positioning system (GPS) signal is affected and loss incurred by the wall of the building causes the GPS lack of precision which leads to large positioning error. As a solution to the indoor area coverage problem, an indoor positioning based on bluetooth low energy (BLE) and long range (LoRa) system utilising the receive signal strength indicator (RSSI) is proposed, designed and tested. In this project, the prototype of indoor positioning system is built using node MCU ESP 32, LoRa nodes and BLE beacons. The node MCU ESP 32 will collect RSSI data from each BLE beacons that deployed at decided position around the area. Then, linear regression algorithm will be used in distance estimation. Next, particle filteris implemented to overcome the multipath fading effect and the trilateration technique is applied to estimate the user’s location. The estimated location is compared to the actual position to analyze the root mean square error (RMSE) and cumulative distribution function (CDF). Based on the experiment result, implementing the particle filter reduces the error of location accuracy. The particle filter achieves accuracy with 90% of the time the location error is lower than 2.6 meters.

The strong uniform consistency of kernel estimator of a smooth distribution function in censored linear regression

ABSTRACT This paper considers the kernel estimation of a smooth error distribution function in linear regression with right-censored data. Based on the Kaplan–Meier estimator of the residual distribution, we define the kernel-smoothed estimator of the error distribution function. Under the appropriate conditions, uniformly strong consistency of the proposed estimator is obtained.

Density deconvolution with non–standard error distributions: Rates of convergence and adaptive estimation

It is a typical standard assumption in the density deconvolution problem that the characteristic function of the measurement error distribution is non-zero on the real line. While this condition is assumed in the majority of existing works on the topic, there are many problem instances of interest where it is violated. In this paper we focus on non--standard settings where the characteristic function of the measurement errors has zeros, and study how zeros multiplicity affects the estimation accuracy. For a prototypical problem of this type we demonstrate that the best achievable estimation accuracy is determined by the multiplicity of zeros, the rate of decay of the error characteristic function, as well as by the smoothness and the tail behavior of the estimated density. We derive lower bounds on the minimax risk and develop optimal in the minimax sense estimators. In addition, we consider the problem of adaptive estimation and propose a data-driven estimator that automatically adapts to unknown smoothness and tail behavior of the density to be estimated.

Estimating buildup factor of alloys based on combination of Monte Carlo method and multilayer feed-forward neural network

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society (ANS). Afterwards, the appropriate architecture of FFNN (i.e. the appropriate number of hidden neurons and hidden layers) and the appropriate input patterns features are investigated. The resulted FFNN is trained using the modeled BFs and the selected category of features. In the test process, the BFs of the master alloys (i.e. Fe-Al%50, Cu-Fe50%, Al-Cu50%) are estimated. To evaluate the performance of the proposed FFNN for training/estimation of the new elements/alloys, Si is added to the training process and the BFs of the Al-Si35% is estimated. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the errors show the acceptable accuracy of the estimating the BFs of the alloys. The noticeable advantages of the proposed technique are: 1- The BFs of the different alloys are estimated only by using the BFs of the constituent elements of the alloys. 2- The time needed to estimate the new BFs by the proposed technique can be neglected versus the time needed to model the new BFs by Monte Carlo. 3- The proposed technique can generalize its ability for estimating the BFs of the new alloys. 4- Monte Carlo codes need the trained person to model the BFs of the alloys while the FFNN generates the new BFs easily.

SAED: Self-adaptive Error Detection Automation for Leveraging Computational Efficiency of HCI Systems

The human–computer interaction (HCI) system is one of the state-of-art consolidations of intellectual psychology and computer automation technology in the recent era. An organized HCI focuses on designing intelligent and competent processing systems for interaction through human–reciprocation. Processing efficiency and computational precision of intelligent systems unambiguously rely on user interfaces. The improper access to user interfaces results in error-prone responses and hence the computation increases in account of better accuracy. In this manuscript, the self-adaptive error detection (SAED) automation method is introduced for leveraging the computation precision of HCI systems. SAED harmonizes a bi-linear learning process based on Bayes classification subsided by a cumulative distribution function (CDF). CDF defines the maximum error unleashing probability of the inputs to recognize certain computations. Bayes classification regresses the decisive computation as a reference to classify inputs by detaining errors to provide more relevant suggestions as a part of automation. SAED results in non-trivial input processing to enhance the choice of HCI integrated into intelligent computing systems. This method focuses on improving the prediction of inputs by reducing error, computation time, and memory overloading. The errors and memory overloading due to improper and partially classified inputs and convergence are thwarted using this method. The specular conduct of SAED is gauged using experimental modeling and error detection rate, predicted inputs, and distribution function metrics are analyzed. The proposed method is found to achieve 13.61% less completion time, 5.63% less error, and 19.29% less memory consumption, respectively.