Machine learning-based defect identification has emerged as a promising solution to improving the defect accuracy of the aero-engine blade. This solution adopts machine learning classifiers to classify the types of defects. These classifiers are trained to use features collected in ultrasonic echo signals. However, the current studies show the potential number of features, such as statistic values, for identifying defect reaches a number more than that offered by an ultrasonic echo signal. This necessitates multiple acquisitions of echo signal and increases manual effort, and the feature obtained from feature selection is sensitive to the characteristic of the classifier, which further increases the uncertainty of the classifier result. This paper proposes an ensemble learning technique that is only based on few features obtained from an echo signal and still achieves a high accuracy of defect identification as that in traditional machine learning, eliminating the need for multiple acq... [more]

is a relatively widely studied, yet non-model, bacterium. While 246 genome assemblies of its various strains are available currently, the diversity of the whole species has not been studied, and it has only been analyzed in part for a missing genome of the type strain. Here, we sequenced and assembled the complete genome of the type strain Clostridium beijerinckii DSM 791T, composed of a circular chromosome and a circular megaplasmid, and used it for a comparison with other genomes to evaluate diversity and capture the evolution of the whole species. We found that strains WB53 and HUN142 were misidentified and did not belong to the Clostridium beijerinckii species. Additionally, we filtered possibly misassembled genomes, and we used the remaining 237 high-quality genomes to define the pangenome of the whole species. By its functional annotation, we showed that the core genome contains genes responsible for basic metabolism, while the accessory genome has genes affecting final phenotype... [more]

The contamination of soil with high levels of cadmium (Cd) is of increasing concern, as Cd is a heavy metal element that seriously limits crop productivity and quality, thus affecting human health. (1) Background: Some miRNAs play key regulatory roles in response to Cd stress, but few have been explored in the highly Cd-enriched coefficient oilseed crop, Brassica juncea. (2) Methods: The genome-wide identification and characterization of miRNAs and their targets in leaves and roots of Brassica juncea exposed to Cd stress was undertaken using strand specific transcript sequencing and miRNA sequencing. (3) Results: In total, 11 known and novel miRNAs, as well as 56 target transcripts, were identified as Cd-responsive miRNAs and transcripts. Additionally, four corresponding target transcripts of six miRNAs, including FLA9 (Fasciclin-Like Arabinogalactan-protein 9), ATCAT3 (catalase 3), DOX1 (dioxygenases) and ATCCS (copper chaperone for superoxide dismutase), were found to be involved in... [more]

In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cu... [more]

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, a dual Kalman filter method based on the online identification of model parameters is proposed to estimate the state of charge in lithium-ion batteries. Here, we build the first-order equivalent circuit model of lithium-ion batteries and derive its online identification model based on extended Kalman (EKF). Considering that the noise value in the EKF algorithm is difficult to select through experiments to achieve the best filtering effect, this paper combines an improved particle swarm optimization algorithm (IPSO) with EKF to realize online model parameter identification. At the same time, the EKF filtering method derived from the state space equation is also used in SOC estimation. It constitutes a dual Kalman filter method for online identification of model parameters and SOC estimation. The experimental and simulation results show that the IPSO−EKF algorithm can adaptively... [more]

Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Cholesky factorization, the online sequential multiple kernel extreme learning machine algorithm (COS-MKELM) is proposed. The LDLT form Cholesky factorization of the matrix is introduced to avoid the large operation amount of inverse matrix calculation. In addition, the stored initial information is utilized to realize online model identification. Then, three regression datasets are used to test the performance of the COS-MKELM algorithm. Finally, an online prediction model for fCaO content is built based on COS-MKELM. Experimental results demonstrate that the fCaO content model improves the performance in terms of learning efficiency, regression accuracy, and generalization ability. In addition, the online prediction model can be corrected in re... [more]

A field survey was conducted to determine the pollution grade, sources, potential ecological risk, and health risk of soil potentially toxic elements (PTEs) in Xikuangshan Mine (XKS), the largest antimony (Sb) deposit in the world. A total of 106 topsoil samples were collected from 6 sites in XKS to measure the concentrations of PTEs Cr, Zn, Cd, Pb, As, Hg, and Sb. The results show that the average concentrations of these elements at all six sites were generally greater than their corresponding background values in Hunan province, especially Sb, Hg, and As. Correlation and principal component analyses suggested that Cd, Zn, Pb, Hg, and Sb were primarily released from mining and other industrial and human activities, while Cr and As were mainly impacted by the parent material from pedogenesis. A risk index analysis showed that, overall, sites were at very high ecological risk, and Sb is the highest ecological risk factor, followed by Cd and Hg. According to health risk assessment, oral... [more]

Kinetic models used for the kinetic analysis of solid-state reactions assume ideal conditions that are very rarely fulfilled by real processes. One of the assumptions of these ideal models is that all sample particles have an identical size, while most real samples have an inherent particle size distribution (PSD). In this study, the influence of particle size distribution, including bimodal PSD, in kinetic analysis is investigated. Thus, it is observed that PSD can mislead the identification of the kinetic model followed by the reaction and even induce complex thermoanalytical curves that could be misinterpreted in terms of complex kinetics or intermediate species. For instance, in the case of a bimodal PSD, kinetics is affected up to the point that the process resembles a reaction driven by a multi-step mechanism. A procedure for considering the PSD in the kinetic analysis is presented and evaluated experimentally by studying the thermal dehydroxylation of kaolinite. This process, wh... [more]

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.

Polymer electrolyte membrane fuel cells (PEMFCs) supplied with green hydrogen from renewable sources are a promising technology for carbon dioxide-free energy conversion. Many mathematical models to describe and understand the internal processes have been developed to design more powerful and efficient PEMFCs. Parameterizing such models is challenging, but indispensable to predict the species transport and electrochemical conversion accurately. Many material parameters are unknown, or the measurement methods required to determine their values are expensive, time-consuming, and destructive. This work shows the parameterization of a quasi-3D PEMFC model using measurements from a stack test stand and numerical optimization algorithms. Differential evolution and the Nelder−Mead simplex algorithm were used to optimize eight material parameters of the membrane, cathode catalyst layer (CCL), and gas diffusion layer (GDL). Measurements with different operating temperatures and gas inlet pressu... [more]

This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input−output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general.

The derivation of minimal bioreaction models is of primary importance to develop monitoring and control strategies of cell/microorganism culture production. These minimal bioreaction models can be obtained based on the selection of a basis of elementary flux modes (EFMs) using an algorithm starting from a relatively large set of EFMs and progressively reducing their numbers based on geometric and least-squares residual criteria. The reaction rates associated with the selected EFMs usually have complex features resulting from the combination of different activation, inhibition and saturation effects from several culture species. Multilayer perceptrons (MLPs) are used in order to undertake the representation of these rates, resulting in a hybrid dynamic model combining the mass-balance equations provided by the EFMs to the rate equations described by the MLPs. To further reduce the number of kinetic parameters of the model, pruning algorithms for the MLPs are also considered. The whole p... [more]

In the analysis of finished pharmaceutical products, numerous innovative analytical techniques are often used, i.e., Raman spectroscopy, scanning electron microscopy, computer microtomography, directional hemispherical reflectance, and hyperspectral analyses. These techniques allow for the identification of changes in solid phases. Many advantages over other techniques can be attributed to these techniques, e.g., they are rapid, non-destructive, and comprehensive. They allow for the identification of changes occurring in solid phases. However, the above-mentioned methods are still not standard procedures in pharmaceutical research. The present study aimed to assess the possible usefulness of total directional hemispherical reflectance (THR), hyperspectral imaging, and computer microtomography to evaluate the stability of tablets containing nifuroxazide during storage. In the study, expired and unexpired coating tablets containing nifuroxazide (n = 10 each) were analyzed. In addition, f... [more]

In this paper, a data-driven approach that is based on the k-mean clustering and local outlier factor (LOF) algorithm has been proposed and deployed for the management of non-destructive evaluation (NDE) in a welded joint. The k-mean clustering and LOF model algorithm, which was implemented for the classification, identification, and determination of data clusters and defect location in the welded joint datasets, were trained and validated such that three (3) different clusters and noise points were obtained. The noise points, which are regarded as the welded joint defects/flaws, allow for the determination of the cluster size, heterogeneity, and silhouette score of the welded joint data. Similarly, the LOF model algorithm was implemented for the detection, visualization, and management of flaws due to internal cracks, porosity, fusion, and penetration in the welded joint. It is believed that the management of welded joint flaws would aid the actualization of the Industry 4.0 concept i... [more]

Nanoplastics (NPs) are a rapidly developing subject that is relevant in environmental and food research, as well as in human toxicity, among other fields. NPs have recently been recognized as one of the least studied types of marine litter, but potentially one of the most hazardous. Several studies are now being reported on NPs in the environment including surface water and coast, snow, soil and in personal care products. However, the extent of contamination remains largely unknown due to fundamental challenges associated with isolation and analysis, and therefore, a methodological gap exists. This article summarizes the progress in environmental NPs analysis and makes a critical assessment of whether methods from nanoparticles analysis could be adopted to bridge the methodological gap. This review discussed the sample preparation and preconcentration protocol for NPs analysis and also examines the most appropriate approaches available at the moment, ranging from physical to chemical.... [more]

There are many unknown abnormal working conditions in industrial production. It is difficult to identify unknown abnormal working conditions because there are few relative sample and experience in this field. To solve this problem, a new identification method combining two-step clustering analysis and signed directed graph (TSCA-SDG) is proposed. Firstly, through correlation analysis and R-type clustering analysis, the variables are effectively selected and extracted. Then, a two-step clustering analysis was carried out on the selected variables to obtain the cluster results. Through the establishment of the signed directed graph (SDG) model, the causes of abnormal working conditions and their mutual influence are deduced from the mechanism. The application of the TSCA-SDG method in the catalytic cracking process shows that this method has good performance for abnormal condition identification.

Dry aging creates a unique taste and flavor in beef; however, the process also causes rancidity, which is harmful to humans. During dry aging, the microbial flora in beef changes continuously; thus, this change can be used as an indicator of rancidity. The objective of this study was to analyze the correlation between microbial flora in beef and rancidity during dry aging. The round of beef (2.5−3 kg) was dry aged under 1.5 ± 1 °C and 82 ± 5% moisture for 17 weeks. The microflora in the dry aged beef was analyzed by pyrosequencing. The volatile basic nitrogen (VBN) and thiobarbituric acid reactive substance (TBARS) values were also measured. Primers were designed to detect and quantify bacteria using real-time polymerase chain reaction (RT-PCR). The VBN and TBARS values in the dry aged beef depreciated from week 11 of aging. The levels of Streptococcus spp., Pantoea spp., and Pseudomonas spp. significantly changed at around week 11. Quantitative RT-PCR showed that the levels of Pantoea... [more]

Coffee is one of the main economic crops in the world and is now widely grown throughout Taiwan. The process of roasting coffee begins with the heating and smooth expansion of raw beans, which leads to changes in appearance and color while affecting the flavor and taste of coffee. So far, most coffee manufacturers have used visual inspection or colorimeter methods to identify differences in coffee quality. Moreover, there is no literature discussing the correlation of roasted bean color with caffeine and chlorogenic acid content. Therefore, the purpose of this experiment was to analyze the chlorogenic acid and caffeine content and their correlation with bean color under different roasting degrees and from different sources to establish basic data for the rapid identification of coffee quality in the future. In this experiment, the coffee Coffea arabica typica from Dongshan, Gukeng, and Sumatra’s Indonesian rainforest was used, and the beans were roasted into four degrees: raw bean, lig... [more]

For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently develo... [more]

This paper presents the development of a new Aerodynamic Ball Levitation Laboratory Plant at the Center of Modern Control Techniques and Industrial Informatics (CMCT&II). The entire design process of the plant is described, including the component selection process, the physical construction of the plant, the design of a printed circuit board (PCB) powered by a microcontroller, and the implementation of its firmware. A parametric mathematical model of the laboratory plant is created, whose parameters are then estimated using a nonlinear least-squares method based on acquired experimental data. The Kalman filter and the optimal state-space feedback control are designed based on the obtained mathematical model. The designed controller is then validated using the physical plant.

In this work, a classical linear control approach for the peroxide (H2O2) dosage in a photo-Fenton process is presented as a suitable solution for improving the efficiency in the treatment of recalcitrant organic compounds that cannot be degraded by classical wastewater treatment processes like anaerobic digestion. Experiments were carried out to degrade Lignin, Melanoidin, and Gallic acid, which are typical recalcitrant organic compounds present in some kinds of effluents such as vinasses from the Tequila and Cachaça industries. Experiments were carried in Open-Loop mode for obtaining the degradation model for the three compounds in the form of a Transfer Function, and in Closed-Loop mode for controlling the concentration of each compound. First-order Transfer Functions were obtained using the reaction curve method, and then, based on these models, the parameters of Proportional Integral controllers were calculated using the direct synthesis method. In the Closed-Loop experiments, the... [more]

In this paper, the Nonlinear Auto-Regressive with exogenous inputs (NARX) model with parameters of interest for design (NARX-M-for-D), where the design parameter of the system is connected to the coefficients of the NARX model by a predefined polynomial function is studied. For the NARX-M-for-D of nonlinear systems, in practice, to predict the output by design parameter values are often difficult due to the uncertain relationship between the design parameter and the coefficients of the NARX model. To solve this issue and conduct the analysis and design, an improved algorithm, defined as the Weighted Extended Forward Orthogonal Regression (WEFOR), is proposed. Firstly, the initial NARX-M-for-D is obtained through the traditional Extended Forward Orthogonal Regression (EFOR) algorithm. Then a weight matrix is introduced to modify the polynomial functions with respect to the design parameter, and then an improved model, which is referred to as the final NARX-M-for-D is established. The ge... [more]

The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate th... [more]

Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by using a root cause identification-new word sentence (RCI-NWS) keyword extraction method. The experimental results demonstrate that the extraction of railway fault text data can be performed using the keyword extraction method and the highest values are obtained using RCI-NWS.

In simulation studies, the precision of fuel cell models has a vital role in the quality of results. Unfortunately, due to the shortage of manufacturer data given in the datasheets, several unknown parameters should be defined to establish the fuel cell model for further precise analysis. This research addresses a novel application of the atom search optimization (ASO) algorithm to generate these unknown parameters of the fuel cell model and in particular of the polymer exchange membrane (PEM) type. The objective of this study is to establish an accurate model of the PEM fuel cells, which will provide accurate results of modeling and simulation in a steady-state condition. Simulations and further demonstrations were performed under MATLAB/SIMULINK. The viability of the proposed models was appraised by comparing its simulation results with the experimental results of number of commercial PEM fuel cells. In the same context, the obtained numerical results by the proposed ASO-based method... [more]