Weighted Estimation Algorithm Research Articles

Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture

With the rise of artificial intelligence, the automotive industry searched for novel ways to improve future product design. We focus on designing automatic MacPherson suspension architecture for the automotive sector. It takes time for an automotive engineer to design vehicle parts and thus slows the pace of innovation in this field. Given the car's particular kinematic characteristics, we propose to predict an architecture by positioning the hardpoints. This work deals with the biased data generated using the discipline models using the dataset shift learning paradigm. The optimized data are created with random and uniform sampling, with more samples with random sampling. We resolve the bias in the data, using a novel criterion for tuning the kernel mean matching and a weight estimation algorithm and designing the required target characteristics.

Research on the Mechanism of Dual Integration and Promotion of Confucian Moral Education Thought and College Students’ Civic Education Based on Data Fusion Model

Abstract This paper uses Data fusion technology to integrate Confucian moral education with Civic Education. Different information sources are detected, and their parameter states are estimated using a weighted estimation algorithm. Principal component analysis is used to linearly transform the original information and calculate the weight values of the main feature vectors. The trust function is established using the D-S combination theory to achieve the fusion of knowledge structures. The results show that the average access rate of the teaching platform constructed based on data fusion reached 89.2% per week, and the average number of platform hits was around 716 per week. It shows that the integration of Confucian moral education ideas has enriched the content of Civic Education for college students and realized the teaching mechanism of double integration and double promotion.

Graph-Based Dynamic Modeling and Traffic Prediction of Urban Road Network

Based on various on-road sensor observations, dynamic modeling and analysis of urban road networks becomes an important task of an intelligent transportation system. The major difficulty of this task is that traffic states vary dynamically in both spatial and temporal domains. In this paper, we propose a novel urban road network modeling approach. A road network is described by a weighted undirected graph composed of vertices and edges denoting, respectively, traffic intersections and their pairwise connections. Given the topology of the network, an effective weight estimation algorithm is proposed to extract spatial correlations among adjacent traffic intersections from physical sensor observations. Graph weights can be regularly updated to capture the dynamic essence of traffic states over time. On the basis of weight estimation, we further develop a dynamic spatio-temporal traffic prediction model by using the spatio-temporal autoregressive integrated moving average (STARIMA) model. The effectiveness of the proposed graph-based STARIMA is validated by a series of numerical experiments.

Active vibration control method of double crystal monochromator of P2 protein crystallography beam line at SSRF

The angular stability of the double-crystal monochromator (DCM) directly affects the positional stability of the focused spot. This paper describes the active vibration control (AVC) method of the DCM of P2 Experimental Station at SSRF based on the adaptive filtering algorithm and the AVC system improves the performance of by optimizing the filter weight estimation algorithm. The simulation results show that the amplitude of the crystal in the pitch direction is greatly reduced by the AVC method, the RMS value of the angular displacement in the pitch direction of the crystal is 1.23 nrad, which is 91.93% lower than the passive vibration control (the RMS value of the angular displacement in the pitch direction is 15.25 nrad); the RMS value of the angular displacement in the pitch direction of the optimized AVC system is 0.233 nrad, which is 98.47% lower than that of the passive vibration control method. The AVC method can effectively ensure the angular stability of the DCM in the pitch direction, which is of great significance for the key equipment of the new generation of synchrotron radiation light source to achieve nano-radian stability.

Multi-structure non-linear semi-parametric optimal weighting parameters estimation algorithm based on adaptive sparse representation deconstruction model and integrative COD comprehensive model

For the combined orbit determination (COD) satellite-network system which is made of GEO-IGSO-MEO satellite-constellation and LEO satellites, the satellites-trajectory are not precise enough, because the meticulous satellite dynamics model, credible observation data model and optimal parameters estimation method are not taking into consideration. The high-level adaptive sparse-deconstruction dynamics model, non-linear semi-parametric combined orbit determination comprehensive model is put forward. By using the above models, adaptive weighting iterative wavelets decomposition algorithm of sparse-representation deconstruction model and multi-structure non-linear semi-parametric optimization weighted estimation algorithm of integrative combined orbit determination comprehensive models are designed. Simulated calculation results show that results showed that the high-precision sparse representation method and the optimization model estimation method can improve COD precision, and when comprehensive model systematic & random error parameters and adaptive sparse-representation deconstruction model parameters are estimated by integrative nonlinear semi-parametric comprehensive model synchronously, COD ultimate trajectory calculation estimation precision can get significant improvement.

A vision-based robust grape berry counting algorithm for fast calibration-free bunch weight estimation in the field

Counting the number of berries per bunch is a key component of many yield estimation processes but is exceptionally tedious for farmers to complete. Recent work into image processing in viticulture has produced methods for berry counting, however these require some degree of manual intervention or need calibration to manual counts for different bunch architectures.Therefore, this paper introduces a fast and robust calibration-free algorithm for berry counting for winegrapes to aid yield estimation. The algorithm was tested on 529 images collected in the field at multiple vineyards at different maturity stages and achieved an accuracy of approximately 89% per bunch. As it would mostly likely be used to obtain an average value across a block, the low bias of this method resulted in an average accuracy of 99% and was shown to be robust from pea-sized to harvest and between both red and green bunches.Taking only 0.1 to 1 s per image to process and requiring only a smartphone and small backing board to capture, the algorithm can readily be applied to images which are captured in the field by farmers. This allowed bunch weights to be estimated to within 92% accuracy and assisted larger scale yield estimation processes to achieve accuracies of between 3% and 16%. The robustness of the method lays the foundation for fast fruit-set ratio determination and more detailed bunch architecture studies in vivo on a large scale.

The electronic Pediatric Emergency Ruler - A digital alternative to the BroselowTape

Study objective: The Broselow® tape (BT) is a pediatric emergency tape (PET) supporting medical teams during pediatric emergencies in estimating body weight, recommending drug dosage and medical equipment. Publications have reported the risk of incorrect use and low accuracy. A recently published digital algorithm for length-based body weight estimation showed higher accuracy for weight estimation. A prototype for an electronic Pediatric Emergency Ruler (ePER) utilizing this algorithm was developed for further testing. The aim of this study was to compare the BT with the ePER in terms of time and correctness of identifying medical information required during pediatric emergency treatment. Methods: Voluntary participants were randomly assigned to use the BT or the ePER in a simulated low-fidelity pediatric emergency manikin scenario and instructed to identify four parameters. Outcomes were time required for identification of all parameters, correct determination of length-based weight and erroneous reading of parameters for the selected weight category. Data are mean or percent. T-test for statistical significance (p < 0.05) and standardized mean difference (SMD > 0.8) were calculated. Results: Identifying medical information was significantly faster with the ePER than with the BT (24.5 vs 36.7 sec, p<0.001; SMD 1.53). Both devices were used correctly in 77.8% of the cases. Overall erroneous readings occurred in 1.9%. Conclusion: The ePER represents a modern and comprehensive solution to support medical staff during pediatric emergencies. This digital solution could be considered as an alternative to the BT.

A Weighted Estimation Algorithm for Enhancing Pulsed Eddy Current Infrared Image in Ecpt Non-Destructive Testing

Non-destructive testing (NDT) plays a crucial role in large scale industrial production such as in the nuclear industry and bridge structures where even a small crack can lead to severe accidents. The pulsed eddy current infrared thermography testing method, as a classic non-destructive testing technology, is proposed to detect cracks in the presence of excitation sources that cause temperature changes in the vicinity of defects, which is higher than normal area. However, in the vicinity of the excitation sources, the temperature is higher than normal even if there is no defect. Traditional infrared image enhancing algorithms do not work efficiently when processing infrared images because the colors in the images represent the temperature. To address this, a novel algorithm is proposed in this paper. A weighted estimation algorithm is proposed because each pixel value has a strong relationship with its neighboring pixels. The value of each pixel is determined by calculating the values of its neighboring pixels with a specific step-size and the correlation coefficients between them. These coefficients are obtained by calculating the differences between the pixels. The experimental results indicated that the outline of the welding defect became significantly clearer after being processed using the proposed algorithm, which can eliminate the errors caused by the excitation source.

A low-cost digital-domain foreground calibration for high resolution SAR ADCs

A digital-domain foreground calibration method simultaneously correcting mismatch errors in main capacitive digital to analog converter (CDAC) and “segment error” between main CDAC and sub-CDAC of split-CDAC array in high resolution successive approximation register (SAR) analog-to-digital converter (ADC) is proposed. The actual weight is obtained with the digital-domain weight estimation algorithm. This proposed digital-domain calibration saves silicon area by eliminating the need for a dedicated calibration DAC employed in conventional CDAC mismatch calibration schemes. Behavioral simulation shows the proposed calibration is capable of correcting mismatch errors and “segment error” of split-CDAC array, and improving SNDR from 67.3 dB to 92.8 dB in a prototype 16 bit split-CDAC SAR ADC.

Comparison of a paediatric emergency ruler with a digital algorithm for weight and age estimation

Emergency tapes like the "Paediatric-Emergency-Ruler" (PaedER) provide drug dosing and recommend medical airway equipment based on estimated patient bodyweight and age. Previous studies have revealed unsatisfactory accuracy in bodyweight estimation, due to the tapes' length-based weight categories. Therefore, we developed a digital algorithm allowing continuous length-based estimation of weight and age. The aim of this study was to compare the new algorithm with the PaedER regarding accuracy in estimating bodyweight and recommendation of medical airway equipment. Patients with a body length suitable for the PaedER were included in this single centre, prospective clinical observation trial after obtaining informed written parental consent. Bodyweight estimations by the algorithm and PaedER within ±10% and ±20% of the actual bodyweight were compared. Furthermore, medical airway equipment suggested by the PaedER and algorithm were compared with the equipment actually used for anaesthesia. Wilcoxon- and McNemar-Tests were used for statistical analysis. Results are median (interquartiles), P<0.05 was considered significant. In total, 489 patients aged 2.0years (0.4-5.9), with a body length of 89.0cm (63.5-114.5), weighing 12.8kg (6.3-19.6), were included. The algorithm's precision of bodyweight estimation within ±10%/±20% was significantly higher at 64.0%/91.6% than the PaedER at 55.4%/81.8% (P<0.001). Compared to PaedER the algorithm showed a higher incidence of correctly recommended medical airway equipment based on its accuracy for age and bodyweight estimation. The new digital algorithm is an alternative to conventional emergency tapes, showing improved accuracy regarding length-based estimation of bodyweight and recommendation of medical airway equipment.

An optimized approach for massive web page classification using entity similarity based on semantic network

With the development of mobile technology, the users browsing habits are gradually shifted from only information retrieval to active recommendation. The classification mapping algorithm between users interests and web contents has been become more and more difficult with the volume and variety of web pages. Some big news portal sites and social media companies hire more editors to label these new concepts and words, and use the computing servers with larger memory to deal with the massive document classification, based on traditional supervised or semi-supervised machine learning methods. This paper provides an optimized classification algorithm for massive web page classification using semantic networks, such as Wikipedia, WordNet. In this paper, we used Wikipedia data set and initialized a few category entity words as class words. A weight estimation algorithm based on the depth and breadth of Wikipedia network is used to calculate the class weight of all Wikipedia Entity Words. A kinship-relation association based on content similarity of entity was therefore suggested optimizing the unbalance problem when a category node inherited the probability from multiple fathers. The keywords in the web page are extracted from the title and the main text using N-gram with Wikipedia Entity Words, and Bayesian classifier is used to estimate the page class probability. Experimental results showed that the proposed method obtained good scalability, robustness and reliability for massive web pages.

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Growth analysis and selection of sea cucumbers (Stichopus japonicas) is typically performed through length or weight measurements. However, because sea cucumbers continuously change shape depending on the external environment, weight measurement has been the preferred approach. Weight measurements require extensive time and labor, moreover it is often difficult to accurately weigh sea cucumbers because of their wet surface. The present study measured sea cucumber features, including the body length, width, and thickness, by using a vision system and regression analysis to generate <TEX>$R^2$</TEX> values that were used to develop a weight estimation algorithm. The <TEX>$R^2$</TEX> value between the actual volume and weight of the sea cucumbers was 0.999, which was relatively high. Evaluation of the performance of this algorithm using cross-validation showed that the root mean square error and worst-case prediction error were 1.434 g and <TEX>${\pm}5.879g$</TEX>, respectively. In addition, the present study confirmed that the proposed weight estimation algorithm and single slide rail device for weight measurement can measure weights at approximately 4,500 sea cucumbers per hour.

Bi-linear adaptive estimation of Fuzzy Cognitive Networks

Fuzzy Cognitive Networks (FCNs) have been introduced by the authors as an operational extension of Fuzzy Cognitive Maps (FCMs), initially introduced by Kosko to model complex behavioral systems in various scientific areas. FCNs rely on the admission that the underlying cognitive graph reaches a certain equilibrium point after an initial perturbation. Weight conditions for reaching equilibrium points have been recently derived in [54] along with an algorithm for weight estimation. In this paper, the conditions are extended to take into account not only the weights of the map but also the inclination parameters of the involved sigmoid functions, increasing the structural flexibility of the network. This in turn gives rise to the development of a new adaptive bilinear weight and sigmoid parameter estimation algorithm, which employs appropriate weight projection criteria to assure that the equilibrium is always achieved.

The possibility of wireless sensor networks for commercial vehicle load monitoring

It is of great importance to be able to monitor and enforce vehicle weight limits for road authorities involved in almost all aspects of transportation and pavement engineering. For the active control of additional weight carried by overloaded vehicles, essential IT technologies such as sensors, measurement, and data processing have been applied. The integration of vehicle load monitoring systems with Wireless Sensor Networks (WSN) technology has a possibility of reducing installation efforts and costs, and enabling the quick and easy configuration of data acquisition and control systems. In this paper, we try to verify that WSN technology can replace the wiring sensor applications for vehicle monitoring issues. The WSN-based system includes: inclinometer sensors which measure variation of inclination values with load changes;, an Access Point (AP) that logs the data collected from all these sensors;, and a weight estimation algorithm. To reach the goal, we performed an experiment with real deployment and estimated the weight of trucks with an error of less than 3%. The result shows that it is possible to adopt WSN for commercial vehicle load monitoring.

Direct adaptive regulation of unknownnonlinear systems with analysis of themodel order problem

A new method for the direct adaptive regulation of unknown nonlinear dynamical systems is proposed in this paper, paying special attention to the analysis of the model order problem. The method uses a neurofuzzy (NF) modeling of the unknown system, which combines fuzzy systems (FSs) with high order neural networks (HONNs). We propose the approximation of the unknown system by a special form of an NF-dynamical system (NFDS), which, however, may assume a smaller number of states than the original unknown model. The omission of states, referred to as a model order problem, is modeled by introducing a disturbance term in the approximating equations. The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability properties. An adaptive modification method, termed ‘parameter hopping’, is incorporated into the weight estimation algorithm so that the existence and boundedness of the control signal are always assured. The applicability and potency of the method are tested by simulations on well known benchmarks such as ‘DC motor’ and ‘Lorenz system’, where it is shown that it performs quite well under a reduced model order assumption. Moreover, the proposed NF approach is shown to outperform simple recurrent high order neural networks (RHONNs).

Adaptive Estimation of Fuzzy Cognitive Maps With Proven Stability and Parameter Convergence

Fuzzy cognitive maps (FCMs) have been introduced by Kosko to model complex behavioral systems in various scientific areas. One issue that has not been adequately studied so far is the conditions under which they reach a certain equilibrium point after an initial perturbation. This is equivalent to studying the existence and uniqueness of solutions for their concept values. In this paper, we study the existence of solutions of FCMs equipped with continuous differentiable sigmoid functions having contractive or, at least, non-expansive properties. This is done by using an appropriately defined contraction mapping theorem and the non-expansive mapping theorem. It is proved that when the weight interconnections fulfill certain conditions, the concept values will converge to a unique solution, regardless of the exact values of the initial concept values perturbations, or in some cases, a solution exists that may not necessarily be unique; otherwise, the existence or the uniqueness of equilibrium cannot be assured. Based on these results, an adaptive weight-estimation algorithm is proposed that employs appropriate weight projection criteria to assure that the uniqueness of FCM solution is not compromised. In view of these results, recently proposed extensions of FCM, which are the fuzzy cognitive networks (FCN), are invoked.

Weighted estimation methods for multistage sampling survey data

Multistage sampling techniques are widely applied in the cross-sectional study of epidemiology, while methods based on independent assumption are still used to analyze such complex survey data. This paper aims to introduce the application of weighted estimation methods for the complex survey data. A brief overview of basic theory is described, and then a practical analysis is illustrated to apply to the weighted estimation algorithm in a stratified two-stage clustered sampling data. For multistage sampling survey data, weighted estimation method can be used to obtain unbiased point estimation and more reasonable variance estimation, and so make proper statistical inference by correcting the clustering, stratification and unequal probability effects.

In-Flight Estimation of Helicopter Gross Weight and Mass Center Location

The ability to compute the weight and balance of a helicopter in flight under general conditions is an enabling technology for future condition based maintenance systems as well as advanced automatic flight control systems. This paper creates a real-time weight and balance estimation algorithm using an extended Kalman filter framework. To highlight estimation characteristics, the algorithm is exercised on the OH-6A helicopter in a variety of flight regimes. The algorithm is examined in hover and forward flight as well as situations where loads are dropped and picked up in flight. For sample scenarios considered, the algorithm quickly estimates station line and butt line mass center position (1 s) and more slowly converges on helicopter weight (10 s). To estimate the helicopter waterline, the algorithm requires modest flight maneuvering where a nonzero roll rate is present. The algorithm is also shown to be reasonably robust relative to sensor and model errors with increasing estimation error with increasing levels of model mismatch and sensor error.

Unsupervised Stream-Weights Computation in Classification and Recognition Tasks

In this paper, we provide theoretical results on the problem of optimal stream weight selection for the two stream classification problem. It is shown that in the presence of estimation or modeling errors using stream weights can decrease the total classification error. Specifically, we show that stream weights should be selected to be proportional to the feature stream reliability and informativeness. Next, we turn our attention to the problem of unsupervised stream weights computation in real tasks. Based on the theoretical results we propose to use models and ldquoanti-modelsrdquo (class-specific background models) to estimate stream weights. A nonlinear function of the ratio of the inter- to intra-class distance is proposed for stream weight estimation. The resulting unsupervised stream weight estimation algorithm is evaluated on both artificial data and on the problem of audiovisual speech classification. Finally, the proposed algorithm is extended to the problem of audiovisual speech recognition. It is shown that the proposed algorithms achieve results comparable to the supervised minimum-error training approach for classification tasks under most testing conditions.

Efficient Intelligent Localization Scheme for Distributed Wireless Sensor Networks

To estimate location accurately in distributed wireless sensor networks (WSNs), an efficient intelligent position estimation scheme is proposed which needs fewer known nodes without calculating relative positions. The proposed intelligent localization scheme includes two phases. In the first phase, an initialization location algorithm is adopted, where known nodes broadcast their positions and one-hop distances, and then unknown nodes use the received information and operate weighted estimation algorithm to calculate their initial positions. In the second phase, an optimized location algorithm is employed. Unknown nodes exchange information with their neighbors and operate a modified weighted estimation algorithm to refine their positions repeatedly. Additionally, a location scheme also is proposed to solve the problem that a node only has two neighbors. Detailed simulation results show that, the proposed scheme can decrease the average position error down to 9% radio range, and increase the average number of located nodes up to 78% when connectivity is greater than 12 in 20% known node ratio.