The latest advances made at NASA for simultaneous excitation in multiple axes and the identification of frequency responses for aircraft flight systems are discussed in this paper. These techniques are extended in the prospect of identifying multiple dynamics and control loops for multiple axes. Recent applications with flight test data and simulation data are also presented, along with a discussion of the practical aspects of the approach. A demonstration is also provided, using a simulation model for the X-59 airplane in which frequency responses for the bare-airframe, closed-loop, and broken-loop (both for the actuator and the sensor) dynamics were identified from a single 60 s maneuver. The results indicate that this approach can significantly shorten the duration of flight tests and Monte Carlo simulations to save time and costs, and can produce results in real time.

The new RP-HPLC-DAD method for the determination of ciprofloxacin and pefloxacin, next to their degradation products after the oxidation reaction with 4,4′-azobis(4-cyanopentanoic acid) (ACVA) was developed. The method was validated according to the guidelines of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) and meets the acceptance criteria. The experimental data indicate that the course of the oxidation process depends on the type of fluoroquinolone (FQ), the incubation time and temperature. The performed kinetic evaluation allowed us to state that the oxidation of FQs proceeds according to the second-order kinetics. The degradation products of the FQs were identified using the UHPLC-MS/MS method and their structures were proposed. The results obtained by the TLC-direct bioautography technique allowed us to state that the main ciprofloxacin and pefloxacin oxidation products probably retained antibacterial activity agains... [more]

Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identification based on partial-least-squares-based Granger causality analysis (PLS-GC) and Bayesian networks (BNs). The proposed method has three main contributions. First, a multiblock operating performance assessment model is established to correspondingly extract economic-related information and dynamic information. Then, a Bayesian network structure is established by PLS-GC that excludes the strong coupling of variables and simplifies the network structure. Lastly, nonoptimal root cause and and nonoptimal transmission path are identified by Bayesian inference. The effectiveness of the proposed method was verified on Benchmark Simulation Model 1... [more]

Lead in buildings can be found in certain materials such as paints or can be a result of contamination during the use stage. In situ methods for lead identification can be vital for the proper treatment of hazardous CDW (from repair works or selective demolition). A conventional handheld XRF (HHXRF) spectrometer can be used for this purpose, and this study analysed its reliability. A laboratory experiment was conducted to test different calibrations, and to establish a procedure for the conversion of the HHXRF lead concentrations into lead loadings. Model latex paint with a constant lead content was used on two types of surfaces (plasterboard and concrete). A field study was performed to identify and quantify the lead in paint on masonry walls in a public building. ICP-MS analysis was performed in order to verify the lead content. The coefficients of proportionality in the proposed model depend on various parameters: the contamination type, the layer thickness, the substrate, and the b... [more]

Almost all systems generate acoustic signals when operating or when a process is being performed. These signals contain certain data related to the operating performance of systems. In this study, acoustic data were used to study the performance and to identify the optimum operating points of natural gas burners that are used in steam boilers. The sound recordings of burners obtained under different operating conditions were examined with acoustic analysis methods. The impact of various operating parameters on acoustic values was determined using time series analysis, frequency spectrum data and then power spectral density values. When the excess air coefficient and emission and efficiency values of boilers were compared with the acoustic data, it was determined that the Yule−Walker algorithm contained distinct and explanatory values. The steam boiler and the natural gas burner within were considered a system for the analysis. Measurement results showed that operating parameters and ac... [more]

Vibration signals from centrifugal pumps are nonlinear, non-smooth, and possess implied trend terms, which makes it difficult for traditional signal processing methods to accurately extract their fault characteristics and details. With a view to rectifying this, we introduced empirical mode decomposition (EMD) to extract the trend term signals. These were then refit using the least squares (LS) method. The result (EMD-LS) was then combined with multi-fractal theory to form a new signal identification method (EMD-LS-MFDFA), whose accuracy was verified with a binomial multi-fractal sequence (BMS). Then, based on the centrifugal pump test platform, the vibration signals of shell failures under different degrees of cavitation and separate states of loosened foot bolts were collected. The signals’ multi-fractal spectra parameters were analyzed using the EMD-LS-MFDFA method, from which five spectral parameters (Δα, Δf, α0, αmax, and αmin) were extracted for comparison and analysis. The resul... [more]

The environmental and technical benefits of renewable energy sources make expanding their use essential in our lives. The main source of renewable energy used in this work is photovoltaic energy. Photovoltaic cells are a clean energy source dependent on solar irradiance to generate electricity from sunlight. The identification of solar cell variables is one of the main items in the simulation and modeling of photovoltaic models. The models used in this work are triple-diode, double-diode, and single-diode solar cells. A novel optimization method called weighted mean of vectors (INFO) is applied for estimating the solar cell variables in the three models. The fitness function of identification is to minimize the root-mean-square error (RMSE) between the measured data of current and the data of simulated current based on the parameters identified from the algorithms. The INFO technique is compared with another seven methods: Harris hawk optimization (HHO), tunicate swarm algorithm (TSA),... [more]

In the world of chaos, the pandemic has driven individuals around the globe to wear face masks for preventing the virus’s transmission, however, this has made it difficult to determine the gender of the person wearing a mask. Gender information is part of soft biometrics, which provides extra information about a person’s identification, thus, identifying a gender based on a veiled face is among the urgent challenges that must be advocated for in the next decade. Therefore, this study exploited various pre-trained deep learning networks (DenseNet121, DenseNet169, ResNet50, ResNet101, Xception, InceptionV3, MobileNetV2, EfficientNetB0, and VGG16) to analyze the effect of the mask while identifying the gender using facial images of human beings. The study comprises two strategies. First, the experimental part involves the training of models using facial images with and without masks, while the second strategy considers images with masks only, to train the pre-trained models. Experimental... [more]

The deleterious consequences of snake envenomation are due to the extreme protein complexity of snake venoms. Therefore, the identification of their components is crucial for understanding the clinical manifestations of envenomation pathophysiology and for the development of effective antivenoms. In addition, snake venoms are considered as libraries of bioactive molecules that can be used to develop innovative drugs. Numerous separation and analytical techniques are combined to study snake venom composition including chromatographic techniques such as size exclusion and RP-HPLC and electrophoretic techniques. Herein, we present in detail these existing techniques and their applications in snake venom research. In the first part, we discuss the different possible technical combinations that could be used to isolate and purify SV proteins using what is known as bioassay-guided fractionation. In the second part, we describe four different proteomic strategies that could be applied for ven... [more]

The product’s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvemen... [more]

Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to ev... [more]

The marine ecosystem is one of the richest sources of biologically active compounds, such as enzymes, among which seaweed is one of the most diverse marine species and has a rich diversity of bacteria that produce different enzymes. Among these, the bacteria-derived xylanase enzyme has many applications in the fruit juice, paper, and baking industries; so, to consider the economic value of the xylanase enzyme and the isolation and identification of xylanase-producing bacteria is of particular importance. In this study, specimens of the alga Ulva flexuosa species were collected from the coasts of Bandar Abbas and Qeshm Island. The bacteria coexisting with the algae were isolated using a nutrient agar medium. The bacteria producing the xylanase enzyme were then screened by a specific solid culture medium containing xylan, and the activity of the xylanase enzyme isolated from the bacteria was measured using a xylan substrate. The bacteria with the highest enzymatic activity were selected... [more]

Industry 4.0 has embraced process models in recent years, and the use of model-based digital twins has become even more critical in process systems engineering, monitoring, and control. However, the reliability of these models depends on the model parameters available. The accuracy of the estimated parameters is, in turn, determined by the amount and quality of the measurement data and the algorithm used for parameter identification. For the definition of the parameter identification problem, the ordinary least squares framework is still state-of-the-art in the literature, and better parameter estimates are only possible with additional data. In this work, we present an alternative strategy to identify model parameters by incorporating differential flatness for model inversion and neural ordinary differential equations for surrogate modeling. The novel concept results in an input-least-squares-based parameter identification problem with significant parameter sensitivity changes. To stu... [more]

Bubble size and its distribution are the important parameters which have a direct impact on mass transfer in bubble column reactors. For this, a new robust image processing technique was presented for investigating hydrodynamic aspects and bubble behavior in real chemical or biochemical processes. The experiments were performed in a small-scale bubble column. The study was conducted for the wide range of clear liquid heights and superficial gas velocities. However, a major challenge in image analysis techniques is identification of overlapping or cluster bubbles. This problem can be overcome with the help of the proposed algorithm. In this respect, large numbers of videos were recorded using a high-speed camera. Based on detailed experiments, the gas−liquid dispersion area was divided into different zones. A foam region width was found as inversely proportional to the clear liquid height. An entry region width was found as directly proportional to the clear liquid height. Hydrodynamic... [more]

Identifying combustion regimes is important for understanding combustion phenomena and the structure of flames. This study proposes a combustion regime identification (CRI) method based on rotated principal component analysis (PCA), clustering analysis and the back-propagation neural network (BPNN) method. The methodology is tested with large-eddy simulation (LES) data of two turbulent non-premixed flames. The rotated PCA computes the principal components of instantaneous multivariate data obtained in LES, including temperature, and mass fractions of chemical species. The frame front results detected using the clustering analysis do not rely on any threshold, indicating the quantitative characteristic given by the unsupervised machine learning provides a perspective towards objective and reliable CRI. The training and the subsequent application of the BPNN rely on the clustering results. Five combustion regimes, including environmental air region, co-flow region, combustion zone, prehe... [more]

The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400−900 nm. Five pretreatment methods were used to pretreat the original spectra. The important characteristic wavelength variables were extracted by random frog (RF), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS) algorithms. The classification models were established by using support vector machine (SVM), k-nearest neighbor (KNN), and partial least squares-discriminant analysis (PLS-DA). The results showed that the mean normalization pretreatment method was preferable, and overall classification accuracy of SVM-based models was higher than that of KNN-based and PLS-DA-based models. The correct classification rate (CCR) of the full-spectrum SVM model (Full-SVM) could reach 96.25%. The SPA method extracted 13... [more]

The use of multiphase electric drives in industrial applications has increased in the last few years. These machines’ advantages over the three-phase system make them appropriate for harsh working situations. To increase their inherent reliability, some authors have been working in sensorless control schemes, where the absence of an encoder ensures proper system performance. Nevertheless, these sensorless control systems present some problems due to the uncertainties of the parameters. In this regard, using extended Kalman filters overcomes this situation, since Kalman filters consider the system error and measurement error in the estimation process. However, when the three-phase Kalman filters are extended to the five-phase case of study, the complexity of the problem increases substantially. In this work, the authors propose an extended Kalman filter, which discomposes the original state equation, reducing the complexity of the estimation stage. In addition, the system suppresses the... [more]

The online measurement of coal ash has overcome the shortcomings of chemical tests. However, there could be large fluctuations and errors in the results of online ash monitors because of the transient change in coal quality resulting from different geological conditions in the mining process. In this study, to resolve the problems of the dual-energy γ-ray online ash monitor in the Linhuan Coal Preparation Plant, we investigated the internal factors, such as the composition of multimineral and multicoal, and external factors, such as the moisture and impurities, which affect the measurement results of the coal ash monitor. Furthermore, we developed a mathematical model to determine the effect of relevant factors on the coefficient of the online ash monitor, which revealed the relationship between coal composition and the parameters of the ash monitor, ensuring the stable and accurate measurement of ash in clean coal. The method of determining parameters used in the case of coal blending... [more]

The existing research and practices have shown that the coordinated implementation of lean-green manufacturing can have a positive impact on the economic and environmental benefits, which is an effective means to ensure the environmental protection of the production process of manufacturing without damaging their profitability. Within the field of lean-green research, there is still a lack of research to analyze the driving factors for the collaborative implementation of integrated lean and green integration. Although, some scholars and researchers have studied lean and green integration paradigms, their research has mostly focused on lean-green integration practices and their impact on environmental performance and their respective operations. In the context of Industry 4.0, this article investigates the driving forces behind the collaborative integration implementation of a lean-green manufacturing system from the viewpoint of stakeholders. Specifically addressing the issues of corre... [more]

Currently, intelligent ships are still in the early stages of development in terms of autonomous navigation and autonomous berthing, so almost no source of fault data can be obtained. Conducting an in-depth analysis of the failure modes of intelligent ships is critical to optimizing the design of smart ships and ensuring their normal and safe navigation. In this paper, the fixed-weight Failure Mode Effects and Criticality Analysis (FMECA) is combined with the decision-making trial and evaluation laboratory (DEMATEL) method to analyze the failure modes and effects of intelligent ship positioning systems. This combined method not only overcomes the failure of traditional FMECA methods to differentiate between severity, incidence, and detection rates but also allows the correlation of failure causes to be analyzed, bringing the results of the analysis closer to reality. Through the expert scoring of failure modes, the failure modes of this system are risk-ranked, and the key failure cause... [more]

The existing results show the applicability of the Over-Parameterized Model based Hammerstein-Wiener model identification methods. However, it requires to estimate extra parameters and performer a low rank approximation step. Therefore, it may give rise to unnecessarily high variance in parameter estimates for highly nonlinear systems, especially using a small and noisy data set. To overcome this corruptive phenomenon. To overcome this corruptive phenomenon, in this paper, a robust Hammerstein-Wiener model identification method is developed for highly nonlinear systems when using a small and noisy data set, where two parsimonious parametrization models with fewer parameters are used, and an iteration method is then used to retrieve the true system parameters from the parametrization models. Such modification can improve the parameter estimation performance in terms of accuracy and variance compared with the over-parametrization model based identification methods. All the above-mentione... [more]

It is significant to scientifically identify what factors influence the green growth of manufacturing enterprises and analyze the relationship among these factors, thus promoting green growth. Firstly, the corresponding conceptual model is designed; then, the DEMATEL method and steps used to identify the influencing factors are introduced; finally, the DEMATEL method is adopted to empirically analyze wooden flooring manufacturing companies so as to identify influencing factors of their green growth. According to the results, there are six reason factors, namely environmental standard constraints, green market demand, market competition, green technology advancement, upstream and downstream synergy of green industrial chain, and policy support, which provide the most important external support to enterprises’ green growth and main driving power to wooden flooring manufacturing ones.

The COVID-19 period has had a significant impact on both the global environment and daily living. The COVID-19 lockdown may provide an opportunity to enhance environmental quality. This study has evaluated the effect of the COVID-19 lockdown on the distribution of polycyclic aromatic hydrocarbons (PAHs) in the street dust (diameter < 20 µm) of different land use areas in Dhaka city, Bangladesh, using gas chromatography−mass spectrometry (GC−MS). The maximum (2114 ng g−1) concentration of ∑16 PAHs was found in the industrial area during without lockdown conditions and the minimum (932 ng g−1) concentration was found in the public facilities area during the complete lockdown. Meanwhile, due to the partial lockdown, a maximum of 30% of the ∑16 PAH concentration decreased from the situation of without lockdown in the industrial area. The highest result of 53% of the ∑16 PAH concentration decreased from the situation without lockdown to the complete lockdown in the commercial area. The 4... [more]

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental res... [more]

The operation of reforming catalysts in a fixed bed reactor undergoes a high level of interaction between the operating parameters and the reaction mechanism. Understanding such an interaction reduces the catalyst deactivation rate. In the present work, three kinds of nanocatalysts (i.e., Pt/HY, Pt-Zn/HY, and Pt-Rh/HY) were synthesized. The catalysts’ performances were evaluated for n-heptane reactions in the fixed bed reactor. The operating conditions applied were the following: 1 bar pressure, WHSV of 4, hydrogen/n-heptane ratio of 4, and the reaction temperatures of 425, 450, 475, 500, and 525 °C. The optimal reaction temperature for all three types of nanocatalysts to produce high-quality isomers and aromatic hydrocarbons was 500 °C. Accordingly, the nanocatalyst Pt-Zn/HY provided the highest catalytic selectivity for the desired hydrocarbons. Moreover, the Pt-Zn/HY-nanocatalyst showed more resistance against catalyst deactivation in comparison with the other two types of nanocatal... [more]