Component Selection Method Research Articles

Baselining Unitized Reversible Fuel Cells with Low Catalyst Loadings: Performance and Durability Insights

Unitized Reversible Fuel Cells (URFCs) combines the functionality of hydrogen fuel cells and water electrolyzers into a single device, offering high energy density for energy conversion and storage. However, their commercial viability is deterred by the high precious group metal (PGM) loadings. Conventional URFCs utilize 1 mg/cm2 of Pt and 1 mg/cm2 of IrO2 in the oxygen electrode, posing economic and sustainability challenges.This study investigates the performance and durability implications of minimizing PGM loadings in oxygen electrode which utilizes a mixture of IrO2 and Pt black to facilitate oxygen evolution and oxygen reduction reaction respectively. We also examine the effect of the porous transport layer (PTL) morphology and wettability on the URFC performance and durability. Accelerated stress test (AST) protocol was developed to evaluate the performance loss across both fuel cell and water electrolyzer mode of URFC operation. Additionally, this research provides a detailed characterization of loss in active area, change in catalyst layer structural, and PTL wettability offering insights into the degradation mechanism in URFCs.This study not only establishes a foundational benchmark for understanding the URFC performance and durability at low catalyst loadings but also highlights the critical need for strategic component selection and activation methods to enhance the system's overall efficiency and durability, marking a significant step towards the realization of more sustainable energy solutions.

Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat.

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized t-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.

Fault diagnosis method of rolling bearing based on MSSA-VMD algorithm

Abstract Early fault signals of rolling bearings are affected by environmental noise, which makes it challenging to extract fault characteristics. In this paper, a bearing fault diagnosis method based on Sparrow Search Algorithm (SSA) and Variable Modal Decomposition (VMD) is proposed to achieve adaptive signal decomposition and feature extraction. First, the fusion shock index is constructed based on the envelope spectrum pulse factor and correlation coefficient to measure the shock fault components. Then, the VMD parameters are adaptively optimized by the improved sparrow search algorithm MSSA, and the fusion shock index is established as the optimal modal component selection method of the fitness function. Finally, the envelope spectrum of the signal after noise reduction is analyzed to extract the fault characteristic frequency. By analyzing the bearing outer ring fault signal and inner ring fault signal under strong noise, the results show that this method can adaptively determine the mode number and penalty factor of VMD, effectively extract the fault signal characteristics, and realize rolling bearing fault diagnosis.

Software component selection methods and techniques: a systematic review

<p>Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.</p>

Research on early fault feature extraction technology of aviation bearing based on noise estimation ITD

Early indications of faults in aircraft bearings are frequently accompanied by excessive noise. To enhance the accuracy of signal decomposition, this study presents the ensemble noise-reconstructed intrinsic time-scale decomposition (ENITD) technique. In addition, a highly sensitive mode component selection method is suggested to attain the goal of improving the precision of fault feature extraction. The findings demonstrate that the ENITD approach is successful in addressing the mode mixing issue and enhancing the precision of fault feature extraction. Unlike established decomposition methods, the estimated noise is applied for denoising instead of incorporating white noise. Furthermore, the estimated noise can introduce diverse frequency signals to their corresponding proper rotation component (PRCs), aiding in resolving the mode mixing problem. This paper examines the efficacy of the ENITD approach for extracting early fault features in aircraft bearings using both simulated and experimental signals.

Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study

This study compared the performance of near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) for the prediction of moisture, protein, total phenolic content (TPC), ferric reducing antioxidant potential (FRAP) and total monomeric anthocyanin (TMA) content in 65 samples of Australian wheat flour. Models were constructed on 50 of the wheat samples, with the 15 remaining samples used as a dependent test set. NIRS showed excellent results for the prediction of protein content (R2test = 0.991; RMSEP = 0.22% w/v) and acceptable to good results for TPC (R2test = 0.83; RMSEP = 3.9 mg GAE/100 g), FRAP (R2test = 0.92; RMSEP = 5.4 mg TE/100 g) and moisture content (R2test = 0.76, RMSEP = 0.62% w/v). Similarly, MIRS showed the best results for protein prediction (R2test = 0.93, RMSEP = 0.62% w/v) and acceptable results for moisture content (R2test = 0.83, RMSEP = 0.65% w/v), FRAP (R2test = 0.83, RMSEP = 7.0 mg TE/100 g) and TPC (R2test = 0.73, RMSEP = 5.6 mg GAE/100 g). However, the TMA content could not be predicted. Finally, moving window analysis was conducted to determine the optimum wavelength ranges for predicting selected analytes. On average, this improved RMSECV values by an average of 18–20% compared to the corresponding full wavelength models, when using the same component selection method. The results confirm that infrared spectroscopy may be useful for the real-time quantitation and/or screening of key quality parameters in wheat, such as protein, TPC and antioxidant capacity.

Efficiency optimization method and accurate power loss evaluation method of 48 V/12 V resonant DC–DC converter

With the rapid development of artificial intelligence, the rack load power is constantly increasing. The 48 V/12 V converter which is suitable for the two-stage step-down structure of 48 V distribution system has received much attention. In this paper, the efficiency optimization scheme for 48 V/12 V three-phase resonant switched-capacitor-converter is proposed. The component selection method is considered under light load and heavy load conditions. An accurate loss model is also proposed for the converter, and the model is accurate. Parameter design process is given and three experiments are designed to verify the correctness of the optimization method. The highest power convert efficiency (PCE) achieved by the 48 V/12 V experimental prototype with the maximum load power of 144 W is 98.97%.

Optimized Design of Floor Plan and Components of Prefabricated Building with Energy-Cost Effect

Optimizing building performance and economic benefits through feedback in building design is a hot topic in current academic research. However, few studies on prefabricated buildings have been undertaken in this field. Meanwhile, the methodology used for achieving optimized solutions is still poor. In this paper, genetic algorithms and correlation analysis are employed and two parametric design methods—i.e., the floor plan generation method and the component selection method—are proposed for the modularity of the prefabricated buildings. Taking a typical high-rise building in Tianjin as an example, correlation analyses are performed on the basis of the two proposed methods to enhance the depth of the optimized finding approach. The outcome of this research demonstrates the feasibility of the proposed numerical approach, which can produce the optimized floor plan and construction set under the local conditions. This also reveals that the shape coefficient and window-to-wall ratio are strongly correlated with the energy performance of a building, which can help architects to pursue optimized design solutions in the schematic design process.

An Improved Voltage Clamp Circuit Suitable for Accurate Measurement of the Conduction Loss of Power Electronic Devices.

Power electronic devices are essential components of high-capacity industrial converters. Accurate assessment of their power loss, including switching loss and conduction loss, is essential to improving electrothermal stability. To accurately calculate the conduction loss, a drain–source voltage clamp circuit is required to measure the on-state voltage. In this paper, the conventional drain–source voltage clamp circuit based on a transistor is comprehensively investigated by theoretical analysis, simulations, and experiments. It is demonstrated that the anti-parallel diodes and the gate-shunt capacitance of the conventional drain–source voltage clamp circuit have adverse impacts on the accuracy and security of the conduction loss measurement. Based on the above analysis, an improved drain–source voltage clamp circuit, derived from the conventional drain–source voltage clamp circuit, is proposed to solve the above problems. The operational advantages, physical structure, and design guidelines of the improved circuit are fully presented. In addition, to evaluate the influence of component parameters on circuit performance, this article comprehensively extracts three electrical quantities as judgment indicators. Based on the working mechanism of the improved circuit and the indicators mentioned above, general mathematical analysis and derivation are carried out to give guidelines for component selection. Finally, extensive experiments and detailed analyses are presented to validate the effectiveness of the proposed drain–source voltage clamp circuit. Compared with the conventional drain–source voltage clamp circuit, the improved drain–source voltage clamp circuit has higher measurement accuracy and working security when measuring conduction loss, and the proposed component selection method is verified to be reasonable and effective for better utilizing the clamp circuit.

A Synchrophasor Data Compression Technique With Iteration-Enhanced Phasor Principal Component Analysis

The phasor data were compressed as separated amplitudes and phases in previous synchrophasor data compression techniques. To utilize the spatial correlation and temporal continuity of synchrophasors for data compression, a phasor principal component analysis (PPCA) in the field of complex numbers is proposed to compress synchrophasors as a whole in this article. Then, an iterative phasor principal components selection method is proposed to achieve PPCA and ensure the accuracy of reconstructed data since the existing eigenvalue-based criteria are not suitable for data compressions. Moreover, the proposed PPCA is enhanced by an iteration-based process to reduce the computation of PPCA. Actual PMU data measured under both a low-frequency oscillation incident and a two-phase short circuit incident conditions are used to verify the performance of PPCA compared with a recent PCA-based compression method. The results demonstrate that PPCA achieves higher compression ratios with better accuracy of reconstructed data, significantly reduced computation, and better real-time performance under both conditions.

Comparative Evaluation of Dataflow Component Selection Methods in Distributed MQTT Broker Environment

Comparative Evaluation of Dataflow Component Selection Methods in Distributed MQTT Broker Environment

Visualization Technology and Tool Selection Methods for Solving Adaptive Training Complex Structural-Parametric Synthesis Problems

AbstractOur previous work in the field of formalization, modeling, and design of adaptive training complexes (ATCs) revealed the low accuracy of the traditional expert approach to selecting ATC components. The study addresses the practical scientific problem of classifying and selecting visualization tools and technologies for designing ATC. Currently, this is done using highly subjective expert evaluation. Therefore, we develop a methodology for the selection of visualization tools and technologies to reduce the influence of the human factor by applying a set of main criteria for visualization component evaluation. Classification is based on the facet approach. We create an original technique that allows users to formalize the main objects of a technical system needing a training complex. It allows users to correlate these aspects to the visualization components and to group, evaluate, and rank them. Integration time, use-cost and component visualization quality, training quality, and time are important variables in the methodology. The use of lexicographic optimization methods or linear convolution of criteria is proposed to obtain an optimal solution from the final Pareto-set. The proposed component selection method has several advantages, compared with the classical approach: greater objectivity of the obtained estimates, better development, and further software implementation automation. Thus, this method addressed the problem of choosing the components of ATC visualization. The significance of the study is in the development of the original algorithmic and mathematical software for the method of selecting ATC visualization components.

Determining the number of components in PLS regression on incomplete data set.

Partial least squares regression - or PLS regression - is a multivariate method in which the model parameters are estimated using either the SIMPLS or NIPALS algorithm. PLS regression has been extensively used in applied research because of its effectiveness in analyzing relationships between an outcome and one or several components. Note that the NIPALS algorithm can provide estimates parameters on incomplete data. The selection of the number of components used to build a representative model in PLS regression is a central issue. However, how to deal with missing data when using PLS regression remains a matter of debate. Several approaches have been proposed in the literature, including the Q2 criterion, and the AIC and BIC criteria. Here we study the behavior of the NIPALS algorithm when used to fit a PLS regression for various proportions of missing data and different types of missingness. We compare criteria to select the number of components for a PLS regression on incomplete data set and on imputed data set using three imputation methods: multiple imputation by chained equations, k-nearest neighbour imputation, and singular value decomposition imputation. We tested various criteria with different proportions of missing data (ranging from 5% to 50%) under different missingness assumptions. Q2-leave-one-out component selection methods gave more reliable results than AIC and BIC-based ones.

Independent Component-Based Spatiotemporal Clutter Filtering for Slow Flow Ultrasound.

Effective tissue clutter filtering is critical for non-contrast ultrasound imaging of slow blood flow in small vessels. Independent component analysis (ICA) has been considered by other groups for ultrasound clutter filtering in the past and was shown to be superior to principal component analysis (PCA)-based methods. However, it has not been considered specifically for slow flow applications or revisited since the onset of other slow flow-focused advancements in beamforming and tissue filtering, namely angled plane wave beamforming and full spatiotemporal singular value decomposition (SVD) (i.e., PCA-based) tissue filtering. In this work, we aim to develop a full spatiotemporal ICA-based tissue filtering technique facilitated by plane wave applications and compare it to SVD filtering. We compare ICA and SVD filtering in terms of optimal image quality in simulations and phantoms as well as in terms of optimal correlation to ground truth blood signal in simulations. Additionally, we propose an adaptive blood independent component sorting and selection method. We show that optimal and adaptive ICA can consistently separate blood from tissue better than principal component analysis (PCA)-based methods using simulations and phantoms. Additionally we demonstrate initial in vivo feasibility in ultrasound data of a liver tumor.

An effective method for service components selection based on micro-canonical annealing considering dependability assurance

Distributed virtualization changes the pattern of building software systems. However, it brings some problems on dependability assurance owing to the complex social relationships and interactions between service components. The best way to solve the problems in a distributed virtualized environment is dependable service components selection. Dependable service components selection can be modeled as finding a dependable service path, which is a multiconstrained optimal path problem. In this paper, a service components selection method that searches for the dependable service path in a distributed virtualized environment is proposed from the perspective of dependability assurance. The concept of Quality of Dependability is introduced to describe and constrain software system dependability during dynamic composition. Then, we model the dependable service components selection as a multiconstrained optimal path problem, and apply the Adaptive Bonus-Penalty Microcanonical Annealing algorithm to find the optimal dependable service path. The experimental results show that the proposed algorithm has high search success rate and quick converges.

Quality-Related Fault Detection Based on Improved Independent Component Regression for Non-Gaussian Processes

Partial least squares (PLS) and linear regression methods have been widely utilized for quality-related fault detection in industrial processes recently. Since these traditional approaches assume that process variables follow Gaussian distribution approximately, their effectiveness will be challenged when facing non-Gaussian processes. To deal with this difficulty, a new quality relevant process monitoring approach based on improved independent component regression (IICR) is presented in this article. Taking high-order statistical information into account, ICA is performed onto process data to produce independent components (ICs). In order to remove irrelevant variation orthogonal to quality variable and keep as much quality-related fault information as possible, a new quality-related independent components selection method is applied to these ICs. Then the regression relationship between filtered ICs and the product quality is built. QR decomposition for regression coefficient matrix is able to give out quality-related and quality-unrelated projectors. After the measured variable matrix is divided into quality relevant and quality irrelevant parts, novel monitoring indices are designed for fault detection. finally, applications to two simulation cases testify the effectiveness of our proposed quality-related fault detection method for non-Gaussian processes.

Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD

Incipient Fault Detection of Rolling Bearing with heavy background noise and interference harmonics is a hot topic. In this paper, a new method based on parameter optimized fast EEMD (FEEMD) and Maximum Autocorrelation Impulse Harmonic to Noise Deconvolution (MAIHND) method is proposed for detecting the incipient fault of rolling bearing. Firstly, the FEEMD method with parameters optimization is used to reduce the noise and eliminate the interference harmonics of the fault signal. As a noise assistant improved method, the FEEMD can reduce the mode mixing and enhance the calculation efficiency significantly. Secondly, a new indicator is developed to select the sensitive IMF. Finally, a novel MAIHND method is employed to extract impulse fault feature from the sensitive IMF. Simulation and experiments results indicated that the proposed parameter optimized FEEMD–MAIHND method can effectively identify the weak impulse fault feature of rolling bearing. Moreover, the excellent performance of the proposed indicator for sensitive IMF component selection and MAIHND method is verified.

Software component identification and selection: A research review

SummaryNowadays, with the development of software reuse, software developers are paying more attention to component‐related technologies, which have been mostly applied in the development of large‐scale complex applications to enhance the productivity of software development and accelerate time to market. Component‐based software development is well acknowledged as a methodology, which establishes the reusability of software and reduces the development cost effectively. Two crucial problems in component‐based software development are component identification and component selection. The main purpose of this paper is to provide a reference point for future research by categorizing and classifying different component identification and component selection methods and emphasizing their respective strengths and weaknesses. We hope that it can help researchers find the current status of this issue and serve as a basis for future activities.

Information Theoretic Approaches to Principal Component Selection

SYNOPTIC ABSTRACTComponent selection in principal component analysis is the main step for dimension reduction and working further with extracted factors. A range of component selection methods include hypothesis testing and subjective judgement of graphical representation of eigenvalues, and most of them are not suitable for automatic selection of number of principal components. Though several versions of description length criteria have been developed for automatic selection of principal components, each of the criteria has its own methodological advantages and disadvantages, and no single method always performs the best in all datasets. With the lack of general guidance and criteria for using a particular method, component selection procedures largely depend on personal preferences rather than real statistics. In this article, we survey theoretical grounds of three commonly used minimum description length criteria based on the inherent Karhunen-Loève expansion of the observed process, and examine their performances by employing a series of simulation experiments. Finally, we present some empirical results to demonstrate that the theoretical properties of these criteria are reflected in simulation experiments, and results obtained in simulation experiments are also reflected in real data analysis.

Investigation of hot pressing parameters for manufacture of catalyst-coated membrane electrode (CCME) for polymer electrolyte membrane fuel cells by response surface method

This paper presents the results of investigations to develop an optimized in-house catalyst- coated membrane electrode (CCME) assembling technique which is the fast and most cost-effective method for quick selection of electrode materials and components. Due to the absence of hydrogen, this method is safer than single cell. The hot-pressing conditions of the CCME of a proton exchange membrane fuel cell in this preparation technique were investigated by using a central composite design. The influence of CCME fabrication parameters like hot pressing parameters on performance of hydrogen fuel cells was studied by cathode half cell measurements. Compression pressure, temperature and time duration were key parameters varied from 35 to 105 kgf/cm2, 80 to 140 °C and 1 to 5 min, respectively. The CCME was prepared with a Nafion 117 membrane and the gas diffusion layer (GDL) has an active area of 0.785 cm2 with Pt/MWCNT catalysts of 0.1 mg cm−2 loaded at the cathode side. The design of experiment (DOE) work was performed with the response surface method using the central composite design. The results show that the proposed mathematical model in the response surface methodology (RSM) can be used adequately for prediction and optimization within the factor levels investigated. As it was predicted in present study, The combined optimum hot pressing parameters, gave the highest performance of 22.9 mW cm−2 predicted in this study are 35 kgf/cm2, 93 °C and 5 min.