Theproposed Method Research Articles

Variational approach for the fractional exothermic reactions model with constant heat source in porous medium

In this paper, a new fractional exothermic reactions model with constant heat source in porous media considering the memory effect is proposed. Applying the fractional complex transform, the fractional model is converted into its partner. Then the variational principle of the problem is successfully established. Based on the obtained variational principle, the Ritz method is used to seek the solution of the fractional model. Finally, the correctness and effectiveness of the proposed method are illustrated by the numerical results with the aid of the MATLAB. The obtained results show that the proposed method is easy but effective, and is expected to shed a bright light on practical applications of fractional calculus.

Generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition

The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results.

Image Super-Resolution using Auto-Encoders with Parallel Skip-Connections

Super-resolutionimages are highly desired when employed for numerous analytical purposes andobviously because of their superior attractive visual effect. To create aHigh-Resolution (HR) image from one or more Low-Resolution (LR) images, animage super-resolution technique is used. When dealing with naturalenvironments and settings, it is not always easy to access high-resolutionimages. The main barriers to the same are limitations in acquisition methods.In the domains of forensics investigation, remote sensing, digital monitoring,and medical imaging high-resolution images are always required. Modern methodsusing deep learning models have improved performance when compared to classic image processing methods. Using a convolutionauto-encoder architecture with six parallel skip connections, a novel techniquefor improving lower solution natural images of size 256 × 256 tohigh-resolution images is proposed in this paper. The use of parallel skipconnections between the encoder and decoder allows the network to reconstructhigh-resolution images while still extracting relevant features fromlow-resolution images. Furthermore, the network is skillfully tuned using theright filters and regularization techniques. To create high-resolution outputimages, the decoder component makes use of the features' reduced representationin latent space. The model was evaluated using DIV 2K, CARS DATA, Set5, Set14,and the General data set after being trained using various data sets like CARSDATA and DIV 2K. Peak Signal to Noise Ratio, Structural Similarity Index, MeanSquared Error, and model behavior on numerous data sets are used to compare theproposed method to various current methods. Results reveal that the suggestedmodel works better than the existing method and is reliable.

Aerial target spatial-spectral discrimination for imaging spectrometer based on the acousto-optic tunable filter.

To solve the problem caused by jamming, an acousto-optic tunable filter (AOTF)-based imaging spectrometer and a corresponding spatial-spectral discrimination method are proposed for aerial targets. The system has the capability of staring imaging and is electronically tunable, which provides the spatial location and a distinguishable spectral feature in a few images. Since AOTF operates in a frame mode, the spectral brightness of the targets can be predicted by Kalman filtering, like with the motion model. The final target state is updated by using synthetic spatial-spectral information to realize fast decision-making. The results show that the proposed method is more targeted to solve the problem caused by jamming, compared with the traditional energy discrimination method.

Derivative-free MLSCD conjugate gradient method for sparse signal and image reconstruction in compressive sensing

Finding the sparse solution to under-determined or ill-condition equations is a fundamental problem encountered in most applications arising from a linear inverse problem, compressive sensing, machine learning and statistical inference. In this paper, inspired by the reformulation of the ?1-norm regularized minimization problem into a convex quadratic program problem by Xiao et al. (Nonlinear Anal Theory Methods Appl, 74(11), 3570-3577), we propose, analyze, and test a derivative-free conjugate gradient method to solve the ?1-norm problem arising from the reconstruction of sparse signal and image in compressive sensing. The method combines the MLSCD conjugate gradient method proposed for solving unconstrained minimization problem by Stanimirovic et al. (J Optim Theory Appl, 178(3), 860-884) and a line search method. Under some mild assumptions, the global convergence of the proposed method is established using the backtracking line search. Computational experiments are carried out to reconstruct sparse signal and image in compressive sensing. The numerical results indicate that the proposed method is stable, accurate and robust.

3D shape measurement method for high-reflection surface based on fringe projection.

3D measurement methods based on fringe projection have attracted extensive research. However, it is a challenge to deal with overshooting on a high-reflection or specular surface. To eliminate the saturated pixels caused by overshooting, we propose a projection intensity adaptive adjustment method. First, we project three uniform gray-level images and estimate the projection intensity of the measured surface through the captured uniform gray-level images. Then we can obtain the optimal projection fringes in the camera coordinate system. Second, a set of horizontal and vertical gray-coded patterns are used to establish a coordinate matching relationship between the projected image and the captured image. To check the decoding result of the gray-coded patterns, a set of horizontal and vertical sinusoidal fringes are used to calculate the high-reflection mapping area (HRMA) in the projector coordinate system. Through the distribution of HRMA, we can check whether the decoding is reliable or not. Finally, we project the optimal intensity fringes and obtain the measurement results. We develop a measurement system to verify the validity of the proposed method. Experimental results show that the proposed method can effectively avoid overshooting and obtain measurement results with a minimum rms error.

Linear Buckling Analysis Considering No-compression Property of Metal Grid Shells Stiffened by Tension Braces

In order to analyze the buckling behavior of lattice shells stiffened by cables or slender braces without pre-tension, it is necessary to consider the no-compression property of braces. This paper proposes an innovative method of linear buckling analysis that considers the no-compression property of braces. Moreover, in order to examine the proposed method's validity, its results are compared with the results from a nonlinear buckling analysis with geometrical nonlinearity and material nonlinearity to express the no-compression property of braces. The results show that the proposed method can well-predict the buckling behaviors of lattice shells stiffened by tension braces.

Endomorphisms of Classical Planning Tasks

Detection of redundant operators that can be safely removed from the planning task is an essential technique allowing to greatly improve performance of planners. In this paper, we employ structure-preserving maps on labeled transition systems (LTSs), namely endomorphisms well known from model theory, in order to detect redundancy. Computing endomorphisms of an LTS induced by a planning task is typically infeasible, so we show how to compute some of them on concise representations of planning tasks such as finite domain representations and factored LTSs. We formulate the computation of endomorphisms as a constraint satisfaction problem (CSP) that can be solved by an off-the-shelf CSP solver. Finally, we experimentally verify that the proposed method can find a sizeable number of redundant operators on the standard benchmark set.

Sparsely connected asynchronous reservoir computing network

In order to solve the reservoir computing networkconstruction problem, a sparsely connected asynchronous neuronreservoir construction method is proposed. The method connectsseveral sub-reservoirs sequentially and sets lag links amongsub-reservoirs in order to handle input signals asynchronously insub-reservoirs, and further constitutes serial memory. In order toachieve efficient information transmission, sparse connections areused among sub-reservoirs. Experimental results show that theproposed method can effectively improve the memory capacity ofreservoir and it is easy to deal with long-term dependence problems.In addition, the proposed structure makes the reservoir produce moreabundant dynamic behavior and has better robustness to the initialparameters.

Automated classification of BI-RADS in textual mammography reports

The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined with the hospital information system (HIS) reports and calledWith-HIS. The different classifiers like multiclass support vector machine (SVM), naïve Bayesian (NB), extreme gradientboosting (XGBoost), and multilevel fuzzy min-max neural network (MLF) are used so as to compare the accuracy ofWith-HIS and without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach(Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within theproposed method with MLF classifier is 0.89% but Without-HIS is 0.85%.

A real-time inverse kinematics solution based on joint perturbation for redundant manipulators

Abstract. Aiming at the problem that the calculation of the inverse kinematics solution of redundant manipulators is very time-consuming, this paper presents a real-time method based on joint perturbation and joint motion priority. The method first seeks the pose nearest to the target pose in the manipulator's pose set through fine-tuning all the joints with different angle deviations at the same time and then regards this pose as the starting one to perform iterative calculations until the error between the current pose and the target pose is less than the predetermined error, thus obtaining the inverse kinematics solution corresponding to the target pose. This method can avoid the pseudo-inverse calculations of the Jacobian matrix and significantly reduce the solving complexity. Two types of manipulators are taken as examples to validate the proposed method. Under the premise that the manipulator motion trajectory is satisfied, the Jacobian pseudo-inverse method and the proposed method are both adopted to solve the inverse kinematics. Simulations and comparisons show that the proposed method has better real-time performance, and the joint motions can be flexibly controlled by setting different joint motion priorities. This method can make the work cycle faster and improve the production efficiency of redundant manipulators in real applications.

A causal mixture model decomposition for root cause identification

Multivariate statistical process monitoring methods usually assume the Gaussianityof data. However, in practice, data are multi-modal. Therefore, it’s not always reasonable andenough to use methods that only deal with the data overall covariance matrix. As the lattermay wrap less information compared to the data distribution. Also, such prior assumption is prejudicial to the estimation of the data’ structure and the causal direction of variables. An interesting challenge would then be the development of relevant metrics to monitor variablesand address their causal nature in the context of the non-Gaussianity of the data. Therefore, adequate parametric tests are required to ensure an acceptable and adjustable compromise between false positives and false negatives. In this paper, a new statistical approach is introducedto root cause and fault path propagation analysis. The obtained results demonstrate that theproposed method performs better than the existing methods.

Solvent holder-assisted liquid-phase microextraction using nano-structure biomass-derived carbonaceous aerogel combined with ion mobility spectrometry for simultaneous determination of ethion and chlorpyrifos.

A biomass-derived sponge-like carbonaceous aerogel was prepared through a two-step procedure, including hydrothermal carbonization and freeze-drying process. The aerogel was employed as the solvent holder for liquid-phase microextraction of organophosphorus pesticides, ethion, and chlorpyrifos (as the model compounds), prior to determination by secondary electrospray ionization-ion mobility spectrometry. The carbonaceous aerogel is an appropriate candidate to beused as the solvent holder due to some properties such as three-dimensional structure, porous nature, and very low density. So, the aerogel, including extraction solvent, can tumble on the top of the sample solution, without loss of solvent during its agitation. A comparison of the extraction efficiency was performed at similar conditions between theproposed method and single-drop microextraction, as well as hollow-fiber solvent bar microextraction. Different parameters affecting the extraction efficiency, such as stirring rate, salt concentration, temperature, and extraction time were investigated by using the response surface methodology. The linear dynamic range was in the ranges 1-30 and 1-70μgL-1 with regression coefficients of 0.9973 and 0.9956 for ethion and chlorpyrifos, respectively. The limit of detection was 0.09 and 0.21μgL-1 for ethion and chlorpyrifos, respectively. The method was used for extracting analytes from environmental water and vegetable samples, with the spiking recoveries in the range 80-121%. Graphical abstract Schematic representation of three-dimensional biomass-derived carbonaceous aerogel as the solvent holder in solvent bar microextraction (as a mode of the liquid-phase microextraction method). It was used for the extraction of two kinds of organophosphorus pesticides prior to the determination by ion mobility spectrometry.

The generalized Gegenbauer-Humberts wavelet for solving fractional differential equations

In this paper we present a new method of wavelets, based on generalized Gegen?bauer-Humberts polynomials, named generalized Gegenbauer-Humberts wave?lets. The operational matrix of integration are derived. By using the proposed method converted linear and non-linear fractional differential equation a system of algebraic equations. In addition, discussed some examples to explain the efficiency and accuracy of the presented method.

Parameter Optimization of Generalized S Transform Based on Improved Genetic Algorithm

Background: For the traditional Fourier Transform (FT), it cannot effectively detect the frequency of non-stationary signals with time. Analyzing the local characteristics of time-varying signal by using FT is hard to achieve. Therefore, many time-frequency analysis methods which can meet different needs have been proposed on the basis of the traditional Fourier transform, like the Short Time Fourier Transform (STFT), the widely used Continuous Wavelet Transform (CWT), Wigner-Ville Distribution (WVD) and so on. However, the best time and frequency resolution cannot be achieved at the same time due to the uncertainty criterion. Methods: From the point of view of optimizing time-frequency performance, a new Generalized S Transform (GST) window function optimization method is proposed by combining time frequency aggregation with an improved genetic algorithm in this paper. Results: In the noiseless condition, the Linear Frequency Modulation (LFM), Nonlinear Frequency Modulation (NLFM) and binary Frequency Shift Keying (2FSK) signals are simulated. The simulation results prove that the method can improve the time-frequency concentration and decreasing Rényi entropy effectively. Compared with other four traditional time-frequency analysis methods, the improved GST has more advantages. Conclusion: In this paper, a new method of optimizing the window function in GST based on improved GA is proposed in this paper. Experiments on LFM, NLFM and 2FSK signals show that the proposed method not only has the advantages of high resolution, but also determines the optimal parameters via the time frequency focusing criterion and the Rényi entropy. Compared with the other four kinds of time-frequency analysis methods, the optimized GST based on improved GA in this paper has the best time-frequency focusing.

Static Friction Detection Based on Artificial Neural Networks Method

Introduction: Poor product quality and high energy consumption ofmany control loops is due to the presence of static friction. This phenomenon is monitored by human in many industrials. The decision ismade based on human’s brain which is not effective and reliable. Methods: A model-based method of stiction detection based on an artificialneural network (ANN) is proposed. The ANN which is run in parallel tothe process predicts a dynamic model of the process using data obtainedfrom control signal and process output. Results: It can be seen that theproposed method based on ANN can be replaced with human monitoring method. Conclusions: Capability of the proposed method of staticfriction detection for the process with the sticky valve is confirmed bydata obtained from the simulation in a control loop with sticky valve.

Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

A Generalized Approach towards Soft Expert Sets via Neutrosophic Cubic Sets with Applications in Games

Games are considered to be the most attractive and healthy event between nationsand peoples. Soft expert sets are helpful for capturing uncertain and vague information.By contrast, neutrosophic set is a tri-component logic set, thus it can deal with uncertain,indeterminate, and incompatible information where the indeterminacy is quantified explicitly andtruth membership, indeterminacy membership, and falsity membership independent of each other.Subsequently, we develop a combined approach and extend this concept further to introduce thenotion of the neutrosophic cubic soft expert sets (NCSESs) by using the concept of neutrosophiccubic soft sets, which is a powerful tool for handling uncertain information in many problems andespecially in games. Then we define and analyze the properties of internal neutrosophic cubicsoft expert sets (INCSESs) and external neutrosophic cubic soft expert sets (ENCSESs), P-order,P-union, P-intersection, P-AND, P-OR and R-order, R-union, R-intersection, R-AND, and R-OR ofNCSESs. The NCSESs satisfy the laws of commutativity, associativity, De Morgan, distributivity,idempotentency, and absorption. We derive some conditions for P-union and P-intersection of twoINCSESs to be an INCSES. It is shown that P-union and P-intersection of ENCSESs need not be anENCSES. The R-union and R-intersection of the INCSESs (resp., ENCSESs) need not be an INCSES(resp. ENCSES). Necessary conditions for the P-union, R-union and R-intersection of two ENCSESsto be an ENCSES are obtained. We also study the conditions for R-intersection and P-intersectionof two NCSESs to be an INCSES and ENCSES. Finally, for its applications in games, we use thedeveloped procedure to analyze the cricket series between Pakistan and India. It is shown that theproposed method is suitable to be used for decision-making, and as good as or better when comparedto existing models.

A new approach for Steganography: Bit shifting operation of encrypted data in LSB (SED-LSB)

In this article, a new method is presented by adding bit shift of encrypted data in LSB method (SED-LSB). The message to be embedded in the cover image is encrypted by the key received from the user. Then, secret data are obtained by bit shifting operation using this encrypted text. Finally, the secret data is hidden inside the cover image using the LSB method. SED-LSB is performed by preparing GUI in MATLAB. The quality of the cover image is measured by Peak Signal-to-Noise Ratio (PSNR), besides the similarity between the cover and stego image is determined by the Structural Similarity Index (SSIM). The 512 x 512 size of colored images as Lena, Peppers, Fruits and Baboon are used to hide 87373 byte data. PSNR values are between 51.615 – 51.656 and SSIM values are between 0.99937 – 0.99982. In the proposed method, when data are hidden at a higher capacity, stage image is found not only secure but also high quality.

An Iterative Method for Empirically-Based Q-Matrix Validation

In cognitive diagnosis modeling, the attributes required for each item are specified in the Q-matrix. The traditional way of constructing a Q-matrix based on expert opinion is inherently subjective, consequently resulting in serious validity concerns. The current study proposes a new validation method under the deterministic inputs, noisy “and” gate (DINA) model to empirically validate attribute specifications in the Q-matrix. In particular, an iterative procedure with a modified version of the sequential search algorithm is introduced. Simulation studies are conducted to compare the proposed method with existing parametric and nonparametric methods. Results show that the new method outperforms the other methods across the board. Finally, the method is applied to real data using fraction-subtraction data.