Accuracy Of Different Types Research Articles

Calibration Procedure to Test the Effects of Multiple Influence Quantities on Low-Power Voltage Transformers.

The article presents a study on low-power voltage transformers (LPVTs). Considering their increasing spread among Smart Grids, it is fundamental to assess their accuracy behavior in as realistic conditions as possible. Therefore, this article presents a detailed calibration procedure to test LPVTs’ accuracy when various external influence quantities are simultaneously acting on them. In the calibration procedure, the considered quantities are frequency, air temperature, and external electric field. Afterwards, the designed procedure is applied on three different off-the-shelf LPVTs using a measurement setup developed in a laboratory environment. The presented results (i) confirm the easy applicability of the designed calibration procedure; (ii) highlight the various effects of the influence quantities on the accuracy of different types of LPVTs; (iii) confirm the need to include more realistic tests, like the type-tests presented, into the standards to appreciate a wider set of possible in-field behaviors.

Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

The fruit categorization according to their visual quality has recently experienced tremendous growth in the field of agriculture and food products. Due to post-harvest loses during handling and processing, there is an increasing demand for quality products in agro industry which requires accuracy to predict the fruit. Various techniques of machine learning have been successfully applied for classifying the fruit built on binary class. In this paper, machine leaning technique is used to automate the process of categorization and to improve the accuracy of different types of fruits by feature selection. To categorized images domain specific features such as color, shape and textual features are considered. Statistical color features are extracted from the image, bounding box feature for shape features and gray-level co-occurrence matrix (GLCM) is used to extract the textual feature of an image. These features are combined in a single feature fusion. A support vector machine (SVM) classification model is trained using training set features on fruit360 dataset which includes six fruit categories (classes) with two sub category (sub-classes) which builds multiclass classification task. We present one-vs-one coding design of Error correcting output codes (ECOC) and apply to SVM classifier; validation followed a fivefold cross validation strategy. The result shows that the textual features combined with color and shape feature improved fruit classification accuracy.

Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model

As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the training set, exploring new features related to explicit mediating water molecules as well as ligand conformation stability, and applying extreme gradient boosting (XGBoost) with Δ-Vina parametrization, we have improved robustness and applicability of machine-learning scoring functions. The new scoring function ΔvinaXGB can not only perform consistently among the top compared to classical scoring functions for the CASF-2016 benchmark but also achieves significantly better prediction accuracy in different types of structures that mimic real docking applications.

Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer

Due to the use of multiple observers and controllers in multi-sensor fault-tolerant control of PMSM drive systems, the algorithm is complex and the system control performance is affected. In view of this, the paper studies multi-sensor fault diagnosis and active fault-tolerant control strategies based on a composite sliding mode observer. With the mathematical model of PMSM built, a design method of the composite sliding mode observer is proposed. A single observer is used to observe and estimate various state variables in the system in real time, which simplifies the implementation of observer-related algorithms. In order to improve the diagnostic accuracy of different types of sensors under different faults, a method for determining fault thresholds is proposed through global search for the maximum residual value. Based on this, a fault diagnosis and active fault-tolerant control strategy is proposed to realize fast switching and reconstruction of feedback signals of closed-loop control systems under different faults of multiple sensors, thus restoring the system performance. Finally, the effectiveness of the proposed algorithm and control strategy is verified by simulation experiments

Dirty-data-based alarm prediction in self-optimizing large-scale optical networks.

Machine-learning-based solutions are showing promising results for several critical issues in large-scale optical networks. Alarm (caused by failure, disaster, etc.) prediction is an important use-case, where machine learning can assist in predicting events, ahead of time. Accurate prediction enables network administrators to undertake preventive measures. For such alarm prediction applications, high-quality data sets for training and testing are crucial. However, the collected performance and alarm data from large-scale optical networks are often dirty, i.e., these data are incomplete, inconsistent, and lack certain behaviors or trends. Such data are likely to contain several errors, when collected from old-fashioned optical equipment, in particular. Even after appropriate data preprocessing, feature distribution can be extremely unbalanced, limiting the performance of machine learning algorithms. This paper demonstrates a Dirty-data-based Alarm Prediction (DAP) method for Self-Optimizing Optical Networks (SOONs). Experimental results on a commercial large-scale field topology with 274 nodes and 487 links demonstrate that the proposed DAP method can achieve high accuracy for different types of alarms.

AB221. 154. Accuracy of positioning of scaling ball in pre-operative imaging for total hip arthroplasty

Background: Accurate preoperative planning improves surgical outcomes. Positioning of an external calibrator marker (ECM) used in digital templating can be complicated by many factors. Previous papers have evaluated the accuracy of different types of markers and X-ray quality, this audit aims to evaluate how the variability of the SB position effects templating.

Detecting Malicious Social Bots Based on Clickstream Sequences

With the significant increase in the volume, velocity, and variety of user data (e.g., user-generated data) in online social networks, there have been attempted to design new ways of collecting and analyzing such big data. For example, social bots have been used to perform automated analytical services and provide users with improved quality of service. However, malicious social bots have also been used to disseminate false information (e.g., fake news), and this can result in real-world consequences. Therefore, detecting and removing malicious social bots in online social networks is crucial. The most existing detection methods of malicious social bots analyze the quantitative features of their behavior. These features are easily imitated by social bots; thereby resulting in low accuracy of the analysis. A novel method of detecting malicious social bots, including both features selection based on the transition probability of clickstream sequences and semi-supervised clustering, is presented in this paper. This method not only analyzes transition probability of user behavior clickstreams but also considers the time feature of behavior. Findings from our experiments on real online social network platforms demonstrate that the detection accuracy for different types of malicious social bots by the detection method of malicious social bots based on transition probability of user behavior clickstreams increases by an average of 12.8%, in comparison to the detection method based on quantitative analysis of user behavior.

An investigation into the accuracy of different types of medical devices used in measurement of blood pressure, temperature and blood glucose

An investigation into the accuracy of different types of medical devices used in measurement of blood pressure, temperature and blood glucose

Clinical Pearls

Body temperature readings provide vital information for accurate diagnoses and required therapies. However, standard practices for measuring body temperature in intensive care units (ICUs) vary and often require invasive methods with limited accuracy in different types of patients.Schell-Chaple and colleagues evaluated a new, noninvasive monitoring system that is applied to the lateral forehead, called SpotOn. They compared temperatures taken with the SpotOn system with rectal and bladder temperatures in febrile adult patients. They found the following:The SpotOn method was accurate. Although the authors recommend further research to test for longer periods, they suggest this noninvasive system be considered for continuous monitoring of core temperature in ICU patients.See Article, pp 43–50Family-centered care (FCC) enables and empowers families through improved communication and involvement in their child’s care. However, recent changes in FCC have incorporated a customer service emphasis that can be challenging for staff.The research team interviewed nurses to explore their perceptions of the benefits and challenges in providing FCC. They found thatWhile acknowledging the benefits of FCC, Coats and colleagues recommend that (1) support systems be available for nurses dealing alone with families, (2) new nurses have a longer mentorship to become comfortable with providing care independently in private rooms, and (3) nurses be involved in the implementation of FCC practices.See Article, pp 52–58Patients who require mechanical ventilation often receive sedative therapy to reduce anxiety and optimize comfort and safety. Although various sedation protocols have been successfully used to facilitate quick weaning from mechanical ventilation, there is no standardized practice despite guidelines from organizations such as the Society of Critical Care Medicine.Borkowska and colleagues surveyed more than 300 nurses from an adult intensive care unit and asked them to evaluate their daily sedation practices. They found the following:Nurses’ perceptions, attitudes, and knowledge are vital to the success of any sedation practice. Organizational and multidisciplinary collaboration is needed to improve sedation practices at regional, national, and international levels.See Article, pp 32–42The influenza A(H1N1) pandemic of 2009 to 2010 caused thousands of deaths across the world and left survivors with significant functional limitations and morbidities. Many patients had acute renal failure requiring continuous renal replacement therapy (CRRT); however, the long-term consequences are unknown.Napolitano and colleagues examined the long-term recovery of adult patients in a surgical intensive care unit (ICU) with acute respiratory distress syndrome (ARDS) who tested positive for H1N1. They found the following:This study is the largest to date to report renal outcomes in ARDS-H1N1 patients. The findings provide clinicians with valuable information needed to optimize future resource use.See Article, pp 67–73

Comprehensive evaluation of the influence of meta-models on Bayesian calibration

Many efforts are used to eliminate the gaps between physical measurements and simulation results. The Bayesian calibration technique is one of the popular automatic calibration methods Allowing for all sources of uncertainty, the method attempts to correct inadequate models and parameters that produce discrepancies between prediction and observed data. Bayesian calibration requires numerous iterations of simulation. To reduce the computational cost, a meta-model is often used to replace the original detailed model. This paper evaluates and analyzes the influence of meta-model accuracy on the outcomes of the Bayesian calibration for building energy simulation model. The following five meta-models: multiple linear regression model, neural network, support vector machine, multivariate adaptive regression splines, and Gaussian process emulator are investigated and compared. The study varies the ranges of input parameters to explore the associated accuracy of the built meta-models. The calibration results are evaluated and compared using three criteria: simulation time, the coefficient of variation with root mean square error (CVRMSE) to true input parameter values, and CVRMSE to observed outputs (monthly and annual energy use intensity). The paper confirms that there are significant differences in simulation time and accuracy for different types of meta-models. The most accurate meta-model tested is the Gaussian process emulator (GPE), while it requires the longest computing time. The multiple linear regression model is the fastest but shows the worst performance in the calibration for the true input parameters. All the calibrated models, however, can predict well the outputs against the observations.

Design and Assessment of Sound Source Localization System with a UAV-Embedded Microphone Array

[abstFig src='/00290001/15.jpg' width='300' text='Visualization of localization result' ] We have studied on robot-audition-based sound source localization using a microphone array embedded on a UAV (unmanned aerial vehicle) to locate people who need assistance in a disaster-stricken area. A localization method with high robustness against noise and a small calculation cost have been proposed to solve a problem specific to the outdoor sound environment. In this paper, the proposed method is extended for practical use, a system based on the method is designed and implemented, and results of sound source localization conducted in the actual outdoor environment are shown. First, a 2.5-dimensional sound source localization method, which is a two-dimensional sound source localization plus distance estimation, is proposed. Then, the offline sound source localization system is structured using the proposed method, and the accuracy of the localization results is evaluated and discussed. As a result, the usability of the proposed extended method and newly developed three-dimensional visualization tool is confirmed, and a change in the detection accuracy for different types or distances of the sound source is found. Next, the sound source localization is conducted in real-time by extending the offline system to online to ensure that the detection performance of the offline system is kept in the online system. Moreover, the relationship between the parameters and detection accuracy is evaluated to localize only a target sound source. As a result, indices to determine an appropriate threshold are obtained and localization of a target sound source is realized at a designated accuracy.

Make a drawing. Effects of strategic knowledge, drawing accuracy, and type of drawing on students’ mathematical modelling performance

Drawing strategies are widely used as a powerful tool for promoting students’ learning and problem solving. In this article, we report the results of an inferential mediation analysis that was applied to investigate the roles that strategic knowledge about drawing and the accuracy of different types of drawings play in mathematical modelling performance. Sixty-one students were asked to create a drawing of the situation described in a task (situational drawing) and a drawing of the mathematical model described in the task (mathematical drawing) before solving modelling problems. A path analysis showed that strategic knowledge about drawing was positively related to students’ modelling performance. This relation was mediated by the type and accuracy of the drawings that were generated. The accuracy of situational drawing was related only indirectly to performance. The accuracy of mathematical drawings, however, was strongly related to students’ performance. We complemented the quantitative approach with a qualitative in-depth analysis of students’ drawings in order to explain the relations found in our study. Implications for teaching practices and future research are discussed.

Comparative Study of Different Numerical Models for Vehicle–Bridge Interaction Analysis

In this paper, a comprehensive comparative study on several existing vehicle–bridge interaction (VBI) models is presented with the aim to provide a useful reference to the selection of vehicle and bridge models when conducting the VBI simulation. A simply-supported slab-on-girder highway bridge and the AASHTO HS20-44 vehicle are adopted in the numerical analysis. The bridge is modeled as an Euler–Bernoulli beam, grillage, plate-and-beam system and solid-element system, respectively, while the vehicle is modeled as a moving-force, moving-mass and spring-damper-mass (SDM) system, respectively. Other factors, including the road roughness and the contact condition between the vehicle tire and bridge, are also considered. The effects of different VBI models on the bridge responses are studied and the results from different models are compared in terms of their accuracy, efficiency and suitability. The results show that the accuracy of different types of bridge responses calculated varies with the number of bridge vibration modes used in the simulation. It is also found that the type of element used in the bridge model and the vehicle tire model both have a larger impact on the bridge acceleration than bridge deflection.

Accuracy of torque-limiting devices: A comparative evaluation

Statement of problemTo prevent the loosening of implant screws, clinicians should be aware of the output torque values needed to achieve the desired preload. Accurate torque-control devices are crucial in this regard; however, little information is currently available comparing the accuracy of mechanical with that of electronic torque-control devices. PurposeThe purpose of this in vitro study was to identify and compare the accuracy of different types of torque-control devices. Material and methodsDevices from 5 different dental implant manufacturers were evaluated, including 2 spring-type (Straumann, Implance) mechanical devices (MTLD), 2 friction-type (Biohorizons, Dyna) MTLDs, and 1 (Megagen) electronic torque-control device (ETLD). For each manufacturer, 5 devices were tested 5 times with a digital torque tester, and the average for each device was calculated and recorded. The percentage of absolute deviations from the target torque values (PERDEV) were calculated and compared by using 1-way ANOVA. A 1-sample t test was used to evaluate the ability of each device to achieve its target torque value within a 95% confidence interval for the true population mean of measured values (α=.05 for all statistical analyses). ResultsOne-way ANOVAs revealed statistically significant differences among torque-control devices (P<.001). ETLD showed higher PERDEVs (28.33 ±9.53) than MTLDs (P<.05), whereas PERDEVS of friction-type (7.56 ±3.64) and spring-type (10.85 ±4.11) MTLDs did not differ significantly. In addition, devices produced by Megagen had a significantly higher (P<.05) PERDEV (28.33 ±9.53) other devices, whereas no differences were found in devices manufactured by Biohorizons (7.31 ±5.34), Dyna (7.82 ±1.08), Implance (8.43 ±4.77), and Straumann (13.26 ±0.79). However, 1-sample t tests showed none of the torque-control devices evaluated in this study were capable of achieving their target torque values (P<.05). ConclusionsWithin the limitations of this in vitro study, MTLDs were shown to be significantly more accurate than ETLDs. However, none of the torque-control devices evaluated were able to meet their target torque values successfully.

Analysis of Boundary Conditions for Crystal Defect Atomistic Simulations

Numerical simulations of crystal defects are necessarily restricted to finite computational domains, supplying artificial boundary conditions that emulate the effect of embedding the defect in an effectively infinite crystalline environment. This work develops a rigorous framework within which the accuracy of different types of boundary conditions can be precisely assessed. We formulate the equilibration of crystal defects as variational problems in a discrete energy space and establish qualitatively sharp regularity estimates for minimisers. Using this foundation we then present rigorous error estimates for (i) a truncation method (Dirichlet boundary conditions), (ii) periodic boundary conditions, (iii) boundary conditions from linear elasticity, and (iv) boundary conditions from nonlinear elasticity. Numerical results confirm the sharpness of the analysis.

Assumptions Inherent in Assessing Traffic Forecast Accuracy

A dozen approaches such as extrapolation of previous traffic counts, trip rates based on expected future land development, and application of regional travel demand models can be used to forecast traffic volumes. Because of Virginia’s interest in knowing the accuracy of forecasting approaches, the researchers compared previous Virginia forecasts with observed volumes. These comparisons revealed five observations affecting how accuracy is evaluated. First, the statistic used to aggregate individual link forecast errors may influence the perception of accuracy; for one study, two percentage-based statistics—the median percentage error and the mean absolute percentage error—yielded errors of 2% and 43%, respectively. (An implication is that the appropriate error statistic depends on the forecast’s purpose.) Second, the accuracy of different types of traffic forecasts varies by magnitude; the median absolute percentage error for Virginia studies ranged from 12% (for a site-specific land development study) to 72% (for statewide forecasts based on historic traffic volumes). Third, because of differences in geometry and the frequency of counts, there may not be a direct relationship between a link’s forecast and its observed volume; in one study, the error ranged from almost 0% to 36%, depending on how the forecast and observed volumes were compared. Fourth, some methods require input data that must be forecast, and different indications of accuracy result (e.g., mean absolute percentage errors of 13% versus 34% for the Fratar method) depending on whether the input data are forecast with or without error. Fifth, depending on the chosen decision criterion, large errors may not necessarily affect the actions taken.

Effect of long-term steam autoclaving on changes in torque delivery of spring- and friction-type torque wrenches

Statement of problemTwo types of mechanical torque-limiting devices (MTLD) are available: friction-style and spring-style. Limited information is available regarding the accuracy of different types of MTLDs after sterilization. PurposeThe purpose of this in vitro study was to determine the accuracy of MTLDs after sterilization. Material and methodsThree new friction-style and 3 new spring-style MTLDs (Astra Tech/Dentsply, Zimmer Dental, Biohorizons, Biomet 3i, Straumann [ITI] and Nobel Biocare; n=5 per manufacturer; 30 total) were selected to evaluate their accuracy in delivering the target torque values preset by their manufacturers before and after sterilization. Target torque measurements were made with a digital torque gauge (model DFS2-R-ND; Chatillon) 10 times for each device. All MTLDs were sterilized following the manufacturers’ recommendations. The sterilization procedure was repeated 100 times, and the accuracy of all MTLDs was retested. Absolute torque differences were analyzed using a repeated measures analysis of variance with instrument as the repeated factor. MTLD type (friction or spring) and MTLD status (new or autoclaved) were the independent variables. Post hoc testing was done using the Tukey method for balanced data. ResultsNo significant difference was found between the spring-style and friction-style MTLDs or within the spring-style and friction-style MTLDs before and after sterilization (P>.05). ConclusionsAfter 100 cycles of steam autoclaving, the accuracy of spring-style and friction-style MTLDs was similar. All MTLDs fell within ±10% of the target torque value before and after sterilization.

Segmentation of Images Containing Multiple Intensity Levels using Genetic Algorithms

Image segmentation technique is the process of separating the foreground objects of different intensities from the background. Several authors have proposed different methods for segmentation of images into two classes, each one having different quality of segmentation. Yu et al [1]. used a GA approach to segmentation of 2-D images into two classes. We have extended this method to segment the images into multiple classes or multiple intensity levels (Four, Eight, Twelve and Sixteen greay Levels). The proposed GA-based approach gives us good results for original synthetic images and noisy images containing rectangular, elliptical and irregular-objects using morphological operations upto sixteen classes. The results obtained give 81% to 100% pixel classification accuracy for different types of noise (Gaussian, Speckle, salt and pepper and Poisson) and high noise levels (SNR ranging between 2.18 dB to 6.97 dB). The segmentation results obtained by using proposed GA-based method are good as compared to standard image segmentation FCM method with the increasing noise density of salt and pepper and Gaussian noises. General Terms Image Processing, Pattern Recognition.

Meta Expert Learning and Efficient Pruning for Evolving Data Streams

Researchers have proposed several ensemble methods for the data stream environments including online bagging and boosting. These studies show that bagging methods perform better than boosting methods although the opposite is known to be true in the batch setting environments. The reason behind the weaker performance of boosting methods in the streaming environments is not clear. We have taken advantage of the algorithmic procedure of meta expert learnings for the sake of our study. The meta expert learning differs from the classic expert learning methods in that each expert starts to predict from a different point in the history. Moreover, maintaining a collection of base learners follows an algorithmic procedure. The focus of this paper is on studying the pruning function for maintaining the appropriate set of experts rather than proposing a competitive algorithm for selecting the experts. It shows how a well-structured pruning method leads to a better prediction accuracy without necessary higher memory consumption. Next, it is shown how pruning the set of base learners in the meta expert learning (in order to avoid memory exhaustion) affects the prediction accuracy for different types of drifts. In the case of time-locality drifts, the prediction accuracy is highly tied to the mathematical structure of the pruning algorithms. This observation may explain the main reason behind the weak performance of previously studied boosting methods in the streaming environments. It shows that the boosting algorithms should be designed with respect to the suitable notion of the regret metrics.