scholarly journals Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6283
Author(s):  
Mingzhu Tang ◽  
Zixin Liang ◽  
Huawei Wu ◽  
Zimin Wang
Keyword(s):  
Random Forest ◽  
Wind Turbine ◽  
False Positive ◽  
Fitness Function ◽  
False Negative ◽  
Step Size ◽  
Size Change ◽  
Change Rate ◽  
Whale Optimization ◽  
Diagnosis Method

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods.

scholarly journals A New Improved Variable Step Size MPPT Method for Photovoltaic Systems Using Grey Wolf and Whale Optimization Technique Based PID Controller

2021 ◽  
Vol 54 (1) ◽  
pp. 175-185
Author(s):  
Zemmit Abderrahim ◽  
Herraguemi Kamel Eddine ◽  
Messalti Sabir
Keyword(s):  
Pid Controller ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Boost Converter ◽  
Variable Step Size ◽  
Fast Variable ◽  
Grey Wolf ◽  
Step Size ◽  
Whale Optimization ◽  
Variable Step

In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.

Critical Evaluation of Impedance Phlebography for the Diagnosis of Deep Vein Thrombosis

1974 ◽  
Vol 31 (02) ◽  
pp. 273-278
Author(s):  
Kenneth K Wu ◽  
John C Hoak ◽  
Robert W Barnes ◽  
Stuart L Frankel
Keyword(s):  
Deep Vein Thrombosis ◽  
False Positive ◽  
False Negative ◽  
Critical Evaluation ◽  
Original Method ◽  
Vein Thrombosis ◽  
Negative Results ◽  
False Negative Results ◽  
Deep Vein ◽  
Positive Results

SummaryIn order to evaluate its daily variability and reliability, impedance phlebography was performed daily or on alternate days on 61 patients with deep vein thrombosis, of whom 47 also had 125I-fibrinogen uptake tests and 22 had radiographic venography. The results showed that impedance phlebography was highly variable and poorly reliable. False positive results were noted in 8 limbs (18%) and false negative results in 3 limbs (7%). Despite its being simple, rapid and noninvasive, its clinical usefulness is doubtful when performed according to the original method.

scholarly journals Preliminary Results of MyCog, a Brief Assessment for the Detection of Cognitive Impairment in Primary Care

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 259-260
Author(s):  
Laura Curtis ◽  
Lauren Opsasnick ◽  
Julia Yoshino Benavente ◽  
Cindy Nowinski ◽  
Rachel O’Conor ◽  
...  
Keyword(s):  
Primary Care ◽  
Cognitive Impairment ◽  
Cognitive Aging ◽  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Self Report ◽  
Card Sorting ◽  
Primary Care Settings

Abstract Early detection of Cognitive impairment (CI) is imperative to identify potentially treatable underlying conditions or provide supportive services when due to progressive conditions such as Alzheimer’s Disease. While primary care settings are ideal for identifying CI, it frequently goes undetected. We developed ‘MyCog’, a brief technology-enabled, 2-step assessment to detect CI and dementia in primary care settings. We piloted MyCog in 80 participants 65 and older recruited from an ongoing cognitive aging study. Cases were identified either by a documented diagnosis of dementia or mild cognitive impairment (MCI) or based on a comprehensive cognitive battery. Administered via an iPad, Step 1 consists of a single self-report item indicating concern about memory or other thinking problems and Step 2 includes two cognitive assessments from the NIH Toolbox: Picture Sequence Memory (PSM) and Dimensional Change Card Sorting (DCCS). 39%(31/80) participants were considered cognitively impaired. Those who expressed concern in Step 1 (n=52, 66%) resulted in a 37% false positive and 3% false negative rate. With the addition of the PSM and DCCS assessments in Step 2, the paradigm demonstrated 91% sensitivity, 75% specificity and an area under the ROC curve (AUC)=0.82. Steps 1 and 2 had an average administration time of <7 minutes. We continue to optimize MyCog by 1) examining additional items for Step 1 to reduce the false positive rate and 2) creating a self-administered version to optimize use in clinical settings. With further validation, MyCog offers a practical, scalable paradigm for the routine detection of cognitive impairment and dementia.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

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