Loss Of Predictive Power Research Articles

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials

AbstractThis paper outlines the methodology for predicting power loss in magnetic materials. It starts by introducing the concept of core loss and the complexity of modelling it. Steinmetz's equation is presented to calculate power loss based on frequency and magnetic flux density, but its limitations are highlighted. As an alternative, a neural network‐based method is introduced. The proposed methodology adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density, frequency, temperature, and wave classification. Fast Fourier transform was implemented to reduce the data points of the sampled flux density waveform while preserving its characteristics. Analyzing in the frequency domain enabled streamlining the training of the model. The input features were arranged as required, and the network architecture was designed with appropriate layers and optimal activation functions. Through extensive training using the datasets, the model assimilated intricate relationships between input variables and known power loss. Evaluation and validation metrics were subsequently employed to gauge the performance of the trained network. This innovative methodology aims to significantly augment the precision of power loss predictions, providing valuable insights into the nuanced behaviour of magnetic materials.

Measurement of proton induced absolute production cross-section of 6.13, 6.92 and 7.12 MeV γ-rays from 16O(p,p ′γ )16O reaction

In this article, we report the absolute production cross-section of 6.13, 6.92 and 7.12 MeV γ-rays from the 16O(p,p ′γ )16O reaction for incident proton energy range of 8-16 MeV. Angular distributions of the three γ-rays have been measured for seven angles at 9 MeV proton energy. A detailed phenomenological optical model potential (OMP) was set up to analyse the cross-section data. The OMP parameters were optimised using elastic, polarization and total reaction data available in the literature for protons and neutrons. Low-lying states of 16O were coupled, the optical potential was deformed using deformation parameters, and several resonances were included in the calculations to account for the nuclear structure effects. The potentials so generated have been used to calculate the differential and total cross sections for both 16O(p,p ′ )16O and 16O(p,p ′γ )16O reactions. Our calculated cross-sections are in fairly good agreement with our measured data for 6.13, 6.92 and 7.12 MeV γ-rays. However, there still exist discrepancies in reproduction of the finer details of the cross sections. The comparisons of the calculations with the data bring forth the rather complex roles of channel couplings, resonances in the p+16O system and target deformation in the variation of the cross sections with projectile energy. The increased contribution of nuclear structure effects in light mass nuclei, leads to an apparent loss of predictive power of the theoretical calculation, as we approach the low-energy region of less than 10 MeV projectile energy.

Power Loss Evaluation of an E-Axle Gearbox Considering the Influence of Gear Oil Factors

An accurate power loss prediction in the gearbox is desirable for improving vehicle efficiency. To achieve this objective, evaluating the power loss is necessary. However, power loss is influenced by factors such as the gearbox structure, operating conditions, and gear oil formulation, making power loss evaluation a bottleneck in practice. Therefore, a systematic modeling methodology was developed to evaluate the gearbox power loss in an E-Axle that focuses on the influence of the gear oil factors in the load and no-load cases. The gearbox used in a light-duty truck E-Axle was tested to verify the proposed model. The test was performed under various operating speeds, input loads, and oil temperatures, and four types of gear oil with different formulations were also included to quantify their influence on the power loss. The results showed that the gearbox power loss was significantly influenced by the E-Axle operating conditions, oil temperatures, and different gear oil formulations, promoting different power losses. The comparison results showed good consistency between the predicted power loss and the measured data. The proposed methodology can be utilized to effectively predict the power loss of the E-Axle gearbox and further improve the E-Axle efficiency by selecting suitable oil formulations and adjusting oil temperatures.

Analysis of the Strengths of Various Intermediate Layer Neurons of Radial Basis Function and Vanilla Neural Networks in the Prediction of Signal Power Loss Using Measurement Data from Micro-Cell Environment

Analysis of the Strengths of Various Intermediate Layer Neurons of Radial Basis Function and Vanilla Neural Networks in the Prediction of Signal Power Loss Using Measurement Data from Micro-Cell Environment

Analysis of power loss in heat pipes for integrated thermal management of next generation electrical machines

This paper presents analysis of eddy-current power loss in heat pipes (HPs) for integrated thermal management of electrical machines. Here, a close integration of HPs with winding body is considered. Such an arrangement is particularly attractive, as it targets the main heat source within the machine assembly. However, there are several challenges associated with the subsystem compatibility, which include electromagnetic, thermal, and mechanical design aspects. The HP’s power loss, which is generated as a results of the time varying stator and/or rotor slot magnetic flux leakage, requires careful considerations. Although, the HP-enabled thermal management of electrical machines (electrical windings) has been previously investigated, the additional HP generated power loss has had a very little attention. In this work, the author proposes alternative techniques for accurate predictions of HP generated power loss accounting for the HP’s wick structure. Three alternative custom-built HP constructions Copper-Water and Titanium-Water with sintered and mesh wicks have been investigated in this analysis. Both theoretical finite element (FE) electromagnetic and experimental methods are discussed in detail. The results show that the proposed experiment informed FE model of HP with an equivalent electrical resistivity wick region provides an accurate HP representation useful in design of electrical machines. Further to these, the proposed approach is demonstrated on an example custom vapor chamber (VC) highlighting the importance of accurate power loss predictions in HPs and VCs.

A transdiagnostic, dimensional classification of anxiety shows improved parsimony and predictive noninferiority to DSM.

The current conceptualization of anxiety in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-which includes 11 anxiety disorders plus additional anxiety-related conditions-does not align with accumulating evidence that anxiety is transdiagnostic and dimensional in nature. Transdiagnostic dimensional anxiety models have been proposed, yet they measure anxiety at either a very broad (e.g., "anxiety") or very narrow (e.g., "performance anxiety") level, overlooking intermediate properties of anxiety that cut across DSM disorders. Using indicators from a well-validated semistructured interview of anxiety-related disorders, we constructed intermediate-level transdiagnostic dimensions representing the intensity, avoidance, pervasiveness, and onset of anxiety. We captured these content-agnostic dimensions in a sample representing varying levels and forms of anxiety (N = 268), including individuals with generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, specific phobia, separation anxiety disorder, posttraumatic stress disorder, and obsessive-compulsive disorder (n = 205) and individuals with no psychopathology (n = 63). In preregistered analyses, our dimensional anxiety model showed noninferiority to DSM-5 diagnoses in predicting concurrent and prospective measures of anxiety-related impairment, anxiety vulnerabilities, comorbid depression, and suicidal ideation. These results held regardless of whether the dimensions were combined into a single composite or retained as separate components. Our transdiagnostic dimensional model offers meaningful gains in parsimony over DSM, with no loss of predictive power. This project provides a methodological framework for the empirical evaluation of other transdiagnostic dimensional models of psychopathology that have been proposed as alternatives to the DSM. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Research on internal flow field analysis and power loss modeling of the expansion seal ring

Expansion seal rings are widely used in automobile transmission systems, and their sealing performance plays a vital role in the pressure stability and transmission efficiency of the transmission system. However, investigations into revealing the internal flow mechanism of the expansion seal ring and predicting the power loss of the expansion seal ring are still insufficient. In this study, a two-way fluid–structure interaction model consisting of the shaft, oil sleeve, and expansion seal ring was established. The flow and structural physical fields under a series of operating conditions were simulated, and the effects of oil temperature, rotational speed, and seal pressure differential on leakage rate, deformation, power loss, and contact pressure were analyzed. Further, a power loss prediction model was developed by multiple linear regression method. The results demonstrate that in the internal flow field, the oil flow into and out of the groove is characterized by backward step flow and jet flow, respectively. In the structural field, the maximum deformation and the maximum contact pressure of the main sealing surface appear at the notch of the expansion seal ring, and the seal pressure differential has the most significant influence on it. The relative error between the predicted value of the developed model and experimental values is within 9%, which meets the industrial requirements. This work facilitates scholars to understand the internal flow mechanism and deformation process of expanding seal rings and provides a method to predict sealing ring power loss.

Influence of Learning Rate Hyper-Parameter and Early Stopping Training Technique in Training Vanilla Neural Network Model for Effective Signal Power Loss Prediction

Influence of Learning Rate Hyper-Parameter and Early Stopping Training Technique in Training Vanilla Neural Network Model for Effective Signal Power Loss Prediction

Neural Network Optimization and Implications of High and Low Gradient Results in Training Vanilla and Hybrid Adaptive Neural Network Models for Effective Signal Power Loss Prediction

Neural Network Optimization and Implications of High and Low Gradient Results in Training Vanilla and Hybrid Adaptive Neural Network Models for Effective Signal Power Loss Prediction

A novel risk score for venous thromboembolism in lung cancer patients: a retrospective cohort study

Background: Venous thromboembolism (VTE) is a common and potentially fatal complication in patients with lung cancer. This study aimed to develop and validate a risk score for early prediction of VTE in these patients. Methods: Four hundred and one patients with lung cancer from three pulmonology departments hospitalized between January 2011 and December 2021 were retrospectively assessed. The population was divided into two groups: a Development Group (182 patients) and a validation group (199 patients). In the development group, the risk score system was developed, via univariate and multivariate analyses, based on demographic and clinicopathological variables; it was then validated in the validation group. Results: The incidence of VTE was 26.8% in the development group. It was 25.8%, and 27.6% in the internal and external validation groups, respectively. Hemoglobin level <10g/l, metastasis, histological type poorly or undifferentiated non-small cell carcinoma, and active smoking were the items of the risk score system. This score allowed proper stratification of patients with either high or low risk of VTE in the development group (c statistic =0.703). The patients in the development group were classified into 3 risk groups: low risk (scores 0-1), moderate risk (scores 2-3), and high risk (scores 4-5). When validated in the validation group, there was a moderate loss of predictive power of the score (c statistic=0.641), but the categorization of the patients by the score remained clinically useful. Conclusions: This risk score requires prospective validation studies on a nationwide scale in order to use it as a valid tool for the prevention of VTE in lung cancer.

Investigating unsteady airflow characteristics in the human upper airway based on the clinical inspiration data

To enhance understanding of the airflow characteristics in the human respiratory system during realistic inspiration, we investigated the airflow field in a human upper airway model using large eddy simulation and the dynamic grid method, taking into account clinically measured inspiratory characteristics. The results reveal the following novel findings: (1) The laryngeal jet and recirculation zone exhibit significant unsteadiness, with their dynamic characteristics primarily influenced by the transient inspiration flow rate and glottis motion. This pattern holds true for other airflow characteristics as well. (2) Glottis expansion reduces the energy consumed during inhalation for both steady and unsteady inspiratory flow rates, with the degree of expansion being directly related to the reduction in energy. We can accurately predict power loss by considering the glottis area and inspiratory flow rate. (3) Analysis of spectral entropy clearly demonstrates that the flow transitions from the laminar to turbulence earlier when using clinical inspiration data. Turbulence intensity in the trachea increases when either glottis motion or the transient inspiratory is ignored. In conclusion, the airflow dynamics are significantly more unsteady compared to cases where we ignore either glottis motion or the transient inspiratory flow rate. A precise understanding of realistic respiratory airflow cannot be achieved by assuming either a rigid glottis or a steady inspiration pattern. Therefore, it is crucial to use accurate inspiratory data when studying the properties of airflow structures in the human respiratory system. Moreover, incorporating more physiological data is also essential to obtain realistic respiratory airflow characteristics.

Supporting the monitoring of the digital capacity of schools through optimal shortening of the SELFIE tool

Data-based decision-making could be vital to improving learning and teaching. Digital education has led to an upsurge in the amount of data that researchers and key stakeholders can utilise to enhance learning. In this article, we present an optimal shortening of the European Commission's SELFIE tool, an instrument used to measure the digital capacity of schools. SELFIE is an established and scientifically validated tool that has been used by over 5.5 million users (September 2023) in 80 different countries. In this paper, we propose two shorter measurement instruments based on the original SELFIE tool that could be used when the original scale needs to be shortened due to time and organisational limitations, without considerable loss in predictive power in relation to the digital capacity measurement construct: (1) a midi-SELFIE consisting of 16 items and (2) a mini-SELFIE consisting of 8 items. Using existing data, we shorten the instruments through psychometric analysis using Item Response Theory models. We use three cases to show the uses of the shortened versions and explore their validity compared to the complete instrument. For the first case, we offer a longitudinal analysis of the digital capacity dynamic for selected schools. In the second case, we look at regional differences in Portugal's digital capacities based on an almost full sample of the country. Finally, in the third case, we use a representative sample from Spain to investigate the relationship between digital capacity and time dedicated by teachers to use digital technology during lessons. The three instruments (full, midi and mini) provide similar results, suggesting that the shortened versions of SELFIE would be a reliable alternative to the complete tool for specific purposes, such as monitoring the development of the digital capacity of the school and policy monitoring.

Generalizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics

Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms are believed to result from disrupted neurocognitive development. However, evidence for the clinical and predictive value of neurocognitive assessments in this context has been mixed, and there have been no large-scale efforts to quantify their potential for use in generalizable models that predict individuals’ ADHD symptoms in new data. Using data drawn from the Adolescent Brain Cognitive Development Study (ABCD), a consortium that recruited a diverse sample of over 10,000 youth (ages 9–10 at baseline) across 21 U.S. sites, we develop and test cross-validated machine learning models for predicting youths’ ADHD symptoms using neurocognitive abilities, demographics, and child and family characteristics. Models used baseline demographic and biometric measures, geocoded neighborhood data, youth reports of child and family characteristics, and neurocognitive tests to predict parent- and teacher-reported ADHD symptoms at the 1-year and 2-year follow-up time points. Predictive models explained 15–20% of the variance in 1-year ADHD symptoms for ABCD Study sites that were left out of the model-fitting process and 12–13% of the variance in 2-year ADHD symptoms. Models displayed high generalizability across study sites and trivial loss of predictive power when transferred from training data to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, parental monitoring, and screen time. This work quantifies the information value of neurocognitive abilities and other child characteristics for predicting ADHD symptoms and provides a foundational method for predicting individual youths’ symptoms in new data across contexts.

Theoretical Calculations and Experimental Studies of Power Loss in Dual-Clutch Transmission of Agricultural Tractors

Recent carbon neutrality policies have led to active research in the agricultural tractor sector to replace internal combustion engines, making it imperative to minimize power losses to improve efficiency. Dual-clutch transmissions (DCTs) have been employed in agricultural tractors primarily due to their short shift time and smooth shift feel. However, DCTs have a relatively large number of components and complex structures owing to spatial constraints, making it challenging to predict power losses. Therefore, to predict DCT power losses, this study defined oil churning by considering the structural characteristics and oil circulation and comparing and analyzing the theoretical calculation and test results of power losses at different oil levels. Power loss was calculated based on ISO standards and fluid viscosity theory, and tests were performed to verify. We calculated power losses based on the defined oil churning of a DCT in agricultural tractors and confirmed that their consistency in test results improved when reflecting the lubrication state, considering the structural features and oil circulation. In addition, the factors contributing to power loss under low- and high-speed conditions were analyzed by calculating the power loss for each component.

Modeling and tuning of glucose oxidase‐catalase system incorporating dissolved oxygen concentration

AbstractBACKGROUNDGluconic acid production by a glucose oxidase (GOx) and catalase (CAT) system has been proposed. However, the bioprocess optimal design and operation are limited by the lack of kinetic models of GOx involving oxygen as substrate. Herein, the GOx‐CAT system is modeled considering a continuous oxygen supply at a bioreactor lab scale. Initially, experiments and modeling for parameter estimations of and the individual enzymes were conducted. Then, experiments and modeling for the GOx‐CAT system were performed for final model tuning. Additionally, the model quality was evaluated, allowing for a deeper understanding of the system phenomenology.RESULTSFrom the oxygen transport model tuning, a highly accurate estimation was obtained (>0.98 and confidence interval <2%). The highest oxygen transport rate was obtained for the combined system Buffer‐antifoam‐GOx‐CAT that was 2.5 times larger than that obtained for DI water. Kinetic models for the individual enzymes were very accurate and the parameters were fully identifiable. For the integrated system, the gluconic acid evolution was properly predicted and there was a loss of predictive power for the oxygen model. Parameter identifiability analysis showed that and interpretability was compromised. However, the sensitivity analysis indicated that the model must not be simplified.CONCLUSIONHerein its has been demonstrated that is a complex parameter to be estimated for multi‐enzymatic systems due to its dependency on medium composition, particularly with GOx. Nonetheless, based on the acceptable model predictive power, the calculated parameters could be used for studies on the process design phase. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

Stochastic microcontact model for the prediction of gear mechanical power loss

Gears serve as the primary power transmission element in many industrial and automotive applications. Power loss in the form of heat generation contributes both to reduced fuel economy and elevated transmission operating temperatures which can lead to premature failure of components. The power loss is made up of both load independent losses and load dependent losses. Gear designers need engineering tools to estimate both components to determine overall system efficiency. This research seeks to utilize stochastic descriptions of surface asperity profiles with simplified Elastohydrodynamic lubrication (EHL) metrics to estimate the effective friction coefficient in mixed lubrication gear contacts. Power loss experiments are performed to measure load dependent gear losses and compare them to the model results.

Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied

The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation to systolic BP in PPG-BP all displayed muted correlation levels in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population.

Prediction and analysis of power consumption and power loss at industrial facilities

THE PURPOSE. Conduct a study to improve the reliability of forecasting the magnitude of power consumption and power losses at an industrial enterprise.METHODS. Methods are used to determine and predict the parameters of consumption and losses of electricity at industrial facilities.RESULTS. To clarify the magnitude of electricity losses, it is proposed to use coefficients that take into account the type of load curves and show the ratio of the values of the sum of the squares of currents (powers) of the variable load curve and the values of the sum of average currents (powers), that is. the ratio of power losses during load operation according to variable and uniform schedules (Kgraph), as well as a coefficient that takes into account the topology of the circuit (Ktop). The study of radial and main circuits of networks was carried out and the losses of electricity were determined using the proposed coefficients. The values of equivalent resistances of shop circuits of networks of various topologies are calculated. The operational data of the section of the workshop network are given. It was revealed that with a constant technological process, an increase in the equivalent resistance of the network circuit is due to an increase in the resistance of the contacts of switching devices installed on the lines. The value of the estimated supply of electricity was determined using the parameter of the average value of the equivalent resistance. At the same time, the error in calculating the estimated supply in relation to the actual annual supply of electricity amounted to 2,63%. According to the retrospective values of the average equivalent resistance of the circuit, it is possible to determine the predicted value of this parameter using the average value of the coefficient of change in the equivalent resistance. These characteristics of the scheme are recommended to be used in the assessment and forecasting of losses and the estimated supply of electricity, which will increase the reliability of the predicted parameters for industrial facilities.

BAT inspired regression model for prediction of power loss in solar panel

Solar energy is an increasingly popular and environmentally friendly source of renewable energy. The performance of solar panels can be significantly affected by various factors, including shading or shadowing. The shadowing effect on solar panels has been a topic of significant interest and research in the field of solar energy. Shadows cast on solar panels can have a detrimental impact on their performance, affecting their efficiency and power output. Shadows can cause hotspots, voltage drops, and current imbalances, negatively impacting the overall efficiency and output of the solar panel system. In this paper, we have used a bat inspired model to find optimum parameters which are further used in a regression model to predict the amount of power loss that can happen with the area of cells exposed to shadow in a solar panel. This model can help further researchers to know the exact amount of power loss from the shadow effect and accordingly, they can plan to mitigate the issue.

Prediction and Mitigation of Wind Farm Blockage Losses Considering Mesoscale Atmospheric Response

The engineering wind farm models currently used in industry can assess power losses due to turbine wake effects, but the prediction of power losses due to farm blockage is still a challenge. In this study we demonstrate a new prediction method of farm blockage losses and a possible strategy to mitigate them for a large offshore wind farm in the North Sea, by combining a common engineering wind farm model ’FLORIS’ with the ’two-scale momentum theory’ of Nishino and Dunstan (2020). Results show that the farm blockage losses depend significantly on the ’wind extractability’ factor, which reflects the strength of mesoscale atmospheric response. For a typical range of the extractability factor (assessed using a numerical weather prediction model) the farm blockage losses are shown to vary between about 5% and 15% of the annual energy production (AEP). However, these losses may be mitigated by adjusting turbine operating points taking into account the wind extractability. It is shown that a simple adjustment of the blade pitch angle and tip-speed ratio used below the rated wind speed may increase the AEP by up to about 2%.