DAE Model Research Articles

Fault Detection and Diagnosis in Tennessee Eastman Process with Deep Autoencoder

Data-driven modeling has been considered as an attractive approach for fault detection in chemical processes. Of special interest to industry are methods that represent nonlinear phenomena and detect complex faults. In this paper, a semi-supervised deep learning method - deep autoencoder for fault detection in Tennessee Eastman Process (TEP) is proposed. The TEP process is a simulated benchmark for evaluating process control and monitoring methods. The performance of the proposed method is evaluated and compared to Principal Component Analysis (PCA). The experimental results demonstrate that the proposed optimized five-layers DAE model for fault detection outperforms the standard PCA. Of special importance to real-world applications is its capability for automatic variable selection. In comparison to PCA it demonstrated higher prediction accuracy for most of the generated faults. Deep autoencoder has the potential to become an excellent approach for process monitoring and fault detection in chemical processes.

A DAE perspective on the interface between adaptive and maladaptive personality development: A conceptual replication

This study aimed to examine Dispositional, Adaptational, and Environmental (DAE) variables at the intersection of adaptive and maladaptive personality development as a conceptual replication of the DAE-model (Asendorpf & Motti-Stefanidi, European Journal of Personality, 32(3), 167-185, 2018). In a community sample of adolescents (N = 463; Mage = 13.6 years; 51% female) hypotheses-driven cross-lagged panel models were tested. Longitudinal associations between Dispositional (i.e., neuroticism, disagreeableness and unconscientiousness), Adaptational (i.e., social problems), and Environmental (i.e., perceived quality of the parent-child relationship) variables were investigated. The results partially support the DAE hypotheses. High levels of neuroticism, disagreeableness and social problems were found to predict the perceived quality of the parent-child relationship. In turn, the perceived quality of the parent-child relationship was found to predict levels of unconscientiousness and social problems. No mediation effects were found and, in contrast to DAE hypotheses, results did not indicate bidirectional influences between dispositions and adaptations. The results shed light on differential person-environment interactions that shape personality development and the importance of the perceived quality of the parent-child relationship. These findings provide insight in pathways of personality development, that may lead to personality pathology, and demonstrate the value of the DAE model as a structured guideline that provides testable hypotheses.

Multiple ECG segments denoising autoencoder model.

Denoising autoencoder (DAE) with a single hidden layer of neurons can recode a signal, i.e., converting the original signal into a noise-reduced signal. The DAE approach has shown a good performance in denoising bio-signals, like electrocardiograms (ECG). In this paper, we study the effect of correlated, uncorrelated and jittered datasets on the performance of the DAE model. Vectors of multiple concatenated ECG segments of simultaneously recorded Einthoven recordings I, II, III are considered to establish the following dataset cases: (1) correlated, (2) uncorrelated, and (3) jittered. We consider our previous work in finding the optimal number of hidden neurons receiving the input signal with respect to signal quality and computational burden by applying Akaike's information criterion. To evaluate DAE, these datasets are corrupted with six types of noise, namely mix noise (MX), motion artifact noise (MA), electrode movement (EM), baseline wander (BW), Gaussian white noise (GWN) and high-frequency noise (HFN), to simulate real case scenario. Spectral analysis is used to study the effects of noise whose power spectrum may overlap with the power spectrum of the wanted signal on DAE performance. The simulation results show (a) that the number of hidden neurons to denoise multiple correlated ECG is much lower than for jittered signals, (b) QRS-complex based ECG alignment preferable, (c) noises with slightly overlapping power spectrum, like BW and HFN, can be easily removed with sufficient number of neurons, while the noise with completely overlapping spectrum, like GWN, requires a very low-dimensional and thus coarser reduction to recover the signal. The performance of DAE model in terms of signal-to-noise ratio improvement and the required number of hidden neurons can be improved by utilizing the correlation among simultaneous Einthoven I, II, III records.

New Aspects of Solution Feasibility in a Context of Personalized Therapy Optimization

In this work, the feasibility of a personalized therapy design is considered. We attempt to determine whether all of the obtained results of computer simulations should be presented to medical personnel. For this purpose, a two-drug displacement problem was used, which is the starting point of this research work. The relationships that can be used to characterize the progress as well as the efficiency of treatment in advanced cases can be modeled by a system of nonlinear dynamical equations with additional algebraic dependencies (differential-algebraic equations, DAEs). Then, to improve the efficiency of the therapy, an optimization task needs to be formulated and solved. The solution should meet all the assumed requirements and expectations. Therefore, a control vector parametrization (CVP) procedure for a DAE model is often suggested as an appropriate tool for solving the optimization-based therapy design tasks. In this work, a general iterative optimization framework is discussed in detail together with the proposed three levels of solution feasibility which try to decide if the iteratively obtained solution is trustworthy. The CVP optimization procedure with the designed levels of solution feasibility are implemented and tested. The obtained results are discussed from the perspective of their practical use in the treatment process. It is worth noting that solutions that are valuable from the perspective of creating new optimization algorithms may be rejected by the final recipient as devoid of application possibilities. Some of the presented solutions can be considered as a reference in further clinical research.

Hyperparameter Tuned Deep Autoencoder Model for Road Classification Model in Intelligent Transportation Systems

Unmanned aerial vehicles (UAVs) have significant abilities for automatic detection and mapping of urban surface materials due to their high resolution. It requires a massive quantity of data to understand the ground material properties. In recent days, computer vision based approaches for intelligent transportation systems (ITS) have gained considerable interest among research communities and business people. Road classification using remote sensing images plays a vital role in urban planning. It remains challenging because of scene complexity, fluctuating road structures, and inappropriate illumination circumstances. The design of intelligent models and other machine learning (ML) approaches for road classification has yet to be further explored. In this aspect, this paper presents a metaheuristics optimization with deep autoencoder enabled road classification model (MODAE-RCM). The presented MODAE-RCM technique mainly focuses on the classification of roads into five types, namely wet, ice, rough, dry, and curvy roads. In order to accomplish this, the presented MODAE-RCM technique exploits modified fruit fly optimization (MFFO) with neural architectural search network (NASNet) for feature extraction. In order to classify roads, an interactive search algorithm (ISA) with a DAE model is used. The exploitation of metaheuristic hyperparameter optimizers helps to improve the classification results. The experimental validation of the MODAE-RCM technique was tested by employing a dataset comprising five road types. The simulation analysis highlighted the superior outcomes of the MODAE-RCM approach to other existing techniques.

Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification

Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.

H∞ Control of a Planar 3-DOF Flexible-Cable Manipulator

This paper deals with the dynamic modeling and the H∞ control of a cable-driven parallel robot with flexible cables. The modeling of planar 3-DOF (two translation and one rotation) robot pulled by four-cables has been performed using the assumed modes approach and the Euler-Lagrange equation, leading to a differential-algebraic equations model (DAE model). This model has been then transformed into an ordinary-differential equations model (ODE model) whose linearization is observable and controllable. An H∞ controller has been synthesized, considering a four-bloc synthesis scheme, in order to control the translation and the rotation of the platform and the mean tension of the cables. An original tuning strategy of the weighting functions is proposed, in order to ensure different performances on the different DOF, that suits to the physical nature of the process. The approach is evaluated in simulation on the nonlinear model and shows good disturbance rejection and reference tracking.

A Hybrid Seasonal Autoregressive Integrated Moving Average and Denoising Autoencoder Model for Atmospheric Temperature Profile Prediction.

Atmospheric parameter profile plays a vital role in the studies on meteorology and quantitative remote sensing. Besides measurement of the radiosonde and satellite remote sensing, the atmospheric temperature profile can be predicted by the time series model. However, the performance of the time series model is limited by the weak correlation of temperature data in the stratosphere. In this study, to predict monthly mean atmospheric temperature profile, a hybrid model named seasonal autoregressive integrated moving average and denoising autoencoder (SARIMA-DAE), which combines the traditional time series model with an artificial neural network (ANN), is proposed. The SARIMA is first used to predict the temperature at each altitude level independently; then, the DAE is employed to denoise the predicted temperature profile by SARIMA. The data set used is the L3 monthly mean gridded product of atmospheric infrared sounder, an instrument aboard NASA's Aqua satellite. Taking Maoming, a city in China, as an example, the absolute errors are within 1 K. The mean squared error and mean absolute percentage error on the test set are 0.12 and 0.0012, respectively. Compared with SARIMA, support vector machine regression, and ANN models, experimental results show that the SARIMA-DAE model improves the prediction accuracy at almost all altitude levels. Especially in the stratosphere, the advantage of the hybrid model is more pronounced. The model we proposed should, therefore, be of value to estimate missing data and predict atmospheric parameter profiles.

Research on Acoustic Emission Signal Denoising Based on Autoencoder

Acoustic emission signal denoising is the premise of acoustic emission signal identification. The traditional filter and wavelet analysis have the problems of relying on prior information and poor adaptability in acoustic emission signal denoising. Therefore, a noise denoising model of acoustic emission signal based on noise reduction autoencoder is proposed. By unsupervised learning training, the noise reduction autoencoder has more stable invariant characteristics, so that the error between the reconstructed signal and the original signal converges to a minimum, thus achieving the purpose of denoising. The denoising experiments are carried out on the basis of processing 3000 corrosive acoustic emission signal samples. The experimental results show that the model has better denoising effect when the number of hidden layer neurons is 400. The proposed DAE model has better performance and robustness than Donoho wavelet threshold denoising method.

Evaluation of ecological economic development efficiency based on intelligent DEA model

In order to better promote the development of circular economy, this paper introduces the current international eco-efficiency indicator system used to measure the development of circular economy, and points out that the eco-efficiency indicator system will be an important method for analyzing environmental efficiency in social economic activities. Moreover, this paper combines the actual needs to construct an ecological economy development efficiency evaluation system based on the intelligent DAE model. In addition, this paper combines the specific situation of regional circular economy development, further points out the main indicators that the eco-efficiency indicator system should include, and introduces the experience of establishing circular economy indicators in developed countries. Finally, this paper uses the eco-efficiency index system to evaluate the development of Qingdao’s circular economy, and design experiments to evaluate the efficiency of the system constructed in this paper. The research results show that the evaluation system of the intelligent ecological economy development efficiency constructed in this paper has a certain effect and has certain significance for the development of ecological economy.

An approximation for nonlinear differential-algebraic equations via singular perturbation theory

In this paper, we study jumps of nonlinear DAEs caused by inconsistent initial values. First, we propose a simple normal form called the index-1 nonlinear Weierstrass form (INWF) for nonlinear DAEs. Then we generalize the notion of consistency projector for linear DAEs to the nonlinear case. By an example, we compare our proposed nonlinear consistency projectors with two existing consistent initialization methods to show that the two existing methods are not coordinate-free, i.e., the consistent points calculated by the two methods are not invariant under nonlinear coordinates transformations. Next we propose a singular perturbed system approximation for nonlinear DAEs, which is an ordinary differential equation (ODE) with a small perturbation parameter, we show that the solutions of the proposed perturbation system approximate both the jumps resulting from the nonlinear consistency projectors and the $\mathcal C^1$-solutions of the DAE. At last, we use a numerical simulation of a nonlinear DAE model arising from an electric circuit to illustrate the effectiveness of the proposed singular perturbed system approximation of DAEs.

ECG signal de-noising based on deep learning auto encoder and discrete wavelet transform

ECG is very important tool for diagnosis of heart disease, this signal is suffered from different types of noises such as baseline wander (BW), muscle artifact (MA) and electrode motion (EM) , which lead to wrong interpretation. In order to prevent or reduce the effect of these noises, different approaches have been applied to enhance the ECG signal. In this paper, we have proposed a new method for ECG signal de-noising based on deep learning Auto encoder (DL-DAE) and wavelet transform named as (WT-DAE). The proposed system (WT-DAE) is constructed from two stages, in the first stage, the wavelet transform is used to isolate the most significant coefficient of the signal (approximation sub-band) from de-tails coefficients (details sub-band). The details coefficients is fed to new proposed threshold method , which is used to evaluate the threshold value according to the feature of ECG signal, this threshold value is used to threshold the detail coefficients, in order to remove the details noise that is contained as high frequencly component , then invers wavelet transform is used to reconstruct the signal . Different wavelet filters and threshold functions are applied in this stage. The second stage of signal de-noising is performed by using DAE method, which is designed for reconstruct the de-noised sig-nal. The proposed DAE model is constructed from 14 layers of convolutional, relu and max_ pooling layer with different parameters. We perform training and testing the model with MIT-BIH ECG database and the performance of the pro-posed system is evaluated by terms of MSE, RMSE, PRD and PSNR. The experimental results are compared with other approaches and show that, the proposed system demonstrated the superiority for de-noising ECG signal.Â

Mediated Disposition–Environment Transactions: The Dae Model

We propose a new model of personality development, the disposition– adaptation–environment (DAE) model. It is based on the assumption that two types of individual characteristics can be distinguished: Dispositions make up the relatively stable core of personality at a particular age, and adaptations are the joint outcome of effects of dispositions and environmental characteristics and mediate transactions between dispositions and environments. Whereas distinctions between dispositions and adaptations have been drawn before, the DAE model is unique in that it (i) entails testable hypotheses whether individual characteristics are adaptations or dispositions, (ii) is based on quasi–causal cross–lagged effects, (iii) assigns adaptations a functional role as longitudinal mediators of disposition–environment transaction, and (iv) is developmentally sensitive. We illustrate application of the DAE model with a three–wave longitudinal study of 1118 adolescents who were observed from the first to the third year in middle school, using the Big Five as dispositions, conduct and self–esteem with peers as adaptations, and peer acceptance and rejection as the environmental measures. Hypotheses–driven and exploratory analyses were combined to yield both safe conclusions and novel hypotheses. We compare the model with other models of personality development and discuss extensions that include stable genetic and socio–economic effects. Copyright © 2017 European Association of Personality Psychology

Switch induced instabilities for stable power system DAE models

It is well known that for switched systems the overall dynamics can be unstable despite stability of all individual modes. We show that this phenomenon can indeed occur for a linearized DAE model of power grids. By making certain topological assumptions on the power grid, we can ensure stability under arbitrary switching for the linearized DAE model.

Application of Economics Optimizing Control to a Two-step Transesterification Reaction in a Pilot-Scale Reactive Distillation Column

The challenges in the chemical process industry of tighter environmental and safety constraints, a higher economic efficiency and an operation in a more dynamic environment motivate the utilization of optimizing control where economic policies are integrated into a (often nonlinear) model predictive control scheme. This so-called one-layer approach or dynamic real-time optimization (D-RTO) has the advantage that the processes are dynamically steered towards the most profitable region. High-fidelity dynamic process models are a basic prerequisite for a good controller performance, and building such models is a challenge. Using highly complex models also may lead to long computation times and thus feedback delays. These issues are in practice avoided by applying only steady-state optimization based on nonlinear models (RTO) and/or using simplified models in MPC. However, the development of computational methods that are able to solve large-scale dynamic optimization problems efficiently have paved the way for applications of economics optimizing control to complex chemical processes. In this contribution, we will demonstrate that a real complex pilot-scale chemical processes, a two-step transesterification realized by reactive distillation that is described by a large DAE model can be operated at the economic optimum by using direct optimizing control. We discuss the problem formulation and the numerical methods used and show experimental data that were obtained at the real process.

Review Paper: Efficient numerical methods for the solution of stiff initial-value problems and differential algebraic equations

In recent years, after a prolonged period of intense activity, the study of numerical methods for solving stiff initial-value problems for ordinary differential equations and differential algebraic equations has reached a certain maturity. There now exist some excellent codes which are both efficient and reliable for solving these particular classes of problems. In this paper, we sketch some of the main theory which underpins stiff integration methods and we use this to describe, and put into context, some of the best codes currently available. By referencing only codes which have been thoroughly tested and are widely available, our aim is to direct users of numerical software to those codes which they should try initially if faced with the problem of solving ordinary differential equations of this type. An additional feature is that the codes which we propose serve as benchmarks against which any new codes can be evaluated.

Solving a singular DAE model of unconfined detonation

We consider a simplified model of an unconfined one-dimensional detonation problem, giving a brief survey of the history of the problem and of its numerical solution. This problem with its mathematical features is typical of those solved commercially by ICI plc, and the specific values used for the chemical constants in the example are typical of those of interest. Unfortunately, not all obvious methods work well, because of the singular nature of the problem at the Chapman–Jouguet shock front. We concentrate on shooting methods for the detonation problem based on backward differentiation formula integrators, and present a new analysis which explains how these methods work. Finally, we outline some possibilities for further work, including discussing a more general detonation problem, previous solutions and potential future solution methods.

Dynamic data reconciliation for a class of nonlinear differential-algebraic equation models using the extended kalman filter

In this paper a method is presented for dynamic data reconciliation of nonlinear systems described by a class of differential-algebraic models, using the extended Kalman filter. A time-varying linearization is derived for a semi-explicit index one DAE. A simplified extended Kalman filter algorithm is then presented, and its integration with the DAE model is described. Finally, a simulation study is presented using a DAE model of a batch chemical reactor.

A DAE Framework for Modeling and Control of Reactive Distillation Columns

A detailed dynamic model incorporating the vapor dynamics is derived for reactive distillation columns. The modeling equations consist of nonlinear coupled differential and algebraic equations of index two. The open-loop behavior of the DAE model is compared with that of conventional ODE models derived under the assumption of negligible vapor holdup, to illustrate the importance of including the vapor phase dynamics in the combined reaction/separation operation. An output feedback controller is derived on the basis of the DAE model and its superior performance over an analogous controller derived on the basis of the ODE model is demonstrated through simulations.

Modeling and velocity stabilization of constrained mechanical systems

An application of some one- and multistep methods for the numerical integration ofconstrained mechanical systems with and without stabilization is demonstrated to compare their efficiency. In particular, the use of (high order) multistep methods is considered. Choosingsupernumerary coordinates, constrained mechanical systems are formulated in descriptor form (DAE). For 2D systems at least, an assembling technique methodically different from, but equivalent to, the Lagrange formalism is presented for the computer generation of the equations of motion with constant mass matrix and quadratic constraints. This modeling, which can be generalized to 3D multibody systems, is applied on two non-stiff test problems. An example of the automatic modeling and simulation is given by a seven body mechanism. Computer programs are presented. By index reduction, the DAE model is transferred to an equivalent ODE representation whose unstable numerical solution is stabilized byprojection onto the constraint manifold. A perturbation analysis shows thatvelocity stabilization is the most efficient projection with regard to improvement of the numerical integration. How frequently the numerical solution of the transferred ODE should be stabilized is discussed. For a class of multistep methods, a strategy of stabilizing at certain time steps and performing a much less demandingquasi-stabilization at the others is suggested, especially for high order methods. With these high order methods, stabilization is not even necessary for the second test problem.