International Journal of Modeling, Simulation, and Scientific Computing

Network simulation is a method of using software or a tool which can be used to mimic the network conditions that exist in pre-defined places. This kind of simulation allows the developers, designers, researchers and the network planners to intelligently plan, design, develop and test their applications or research work in changing network conditions. With varying network conditions either because of wireless nature or because of user mobility, it is very difficult to simulate the exact network conditions with the existing network simulators. These network simulators are flexible, re-usable and reliable. But they have a limitation of not being able to replicate the actual network conditions in the laboratories. This calls for a system in the loop or hardware in the loop concept to be extended to the network simulators. The idea of system in the loop is not new. In this paper, an overview with the fundamental understanding of the hardware-in-loop concept for network simulators, their applications and a review of the existing hardware-in-loop network simulators with their advantages and disadvantages is presented.

The rapid development of 3D scanning has allowed us to digitally capture cultural heritage objects as large-scale point clouds, recording their complex internal and external structures. We have developed a 3D multi-view transparent visualization using edge highlighting as visual guide, improving accuracy of perceived depth for reliable visual analyses of the 3D scanned point clouds. When transparently visualizing a 3D object on a 3D multi-view display, the depth information tends to become less distinct. Therefore, we aimed to explore whether emphasizing the statistically extracted 3D edges of the scanned object can enhance the perceived depth of transparently visualized 3D objects. We conducted psychophysical experiments where participants were asked to report their perceived magnitudes of depth for multiple types of 3D structures of cultural heritage presented on a multi-view 3D display. The results indicated that our edge highlighting reduced the underestimation of depth from 46.8% to 34.0% on average. This improvement persisted even when the 3D structures produced an atypical linear perspective, normally caused by reverse depths, where the front and back are reversed. These findings suggest that edge highlighting brings us an effective visual guide to improve the accuracy of perceived depths on a 3D multi-view display.

Genetic oscillators have been widely used in modeling key processes of biological systems, especially cell cycles and circadian rhythms. In particular, repressilatory genetic oscillators have been employed in modeling the dynamics of mRNA and protein interactions with transcriptional and translational feedback loops at the molecular level. In addition, synchronization of these oscillators is crucial for understanding the underlying mechanisms of the associated biological processes. In this paper, models of fractional-order genetic oscillators and their coupling are established, where the aspects of time delay, coupling strength, noise, and stability are all taken into consideration. Communication in the proposed coupling model is based on quorum sensing. The synchronization of the fractional-order repressilator model has been examined through simulations which show three main findings. Firstly, the synchronization of the fractional-order repressilator model can be optimized through coupling weight selection. Secondly, the synchronization can be enhanced by increasing the fractional order and decreasing the time delay and the noise intensity. Finally, transitions between the states of the fractional-order repressilatory oscillator can be achieved through varying the fractional order. The simulation results verify the biological relevance of the genetic oscillator models, and their potential for explaining the underlying mechanisms of the associated biological processes.

Selecting a system or a subset of systems that have the best performance among many alternative systems is an important area of research. All efforts are paid to do this selection with a high probability of correct selection $P(CS)$. In this paper, we compare the performance of two popular procedures used to select the best systems, namely, the method of Kim and Nelson $({𝒦}{𝒩})$ which is considered as a ranking and selection procedure, and the Optimal Computing Budget Allocation $({𝒪}{𝒞}{ℬ}{𝒜})$ procedure. Both of these procedures determine the number of samples for each system that are needed to achieve the maximum $P(CS)$. Recently, the ${𝒪}{𝒞}{ℬ}{𝒜}$ has been developed in order to be able to approximate the value of $P(CS)$, so both procedures approximate the value of $P(CS)$. To do the comparison, we implement the two methods on two examples; one is a generic example and the other one involves a management of inventory model. The two examples assure that the ${𝒪}{𝒞}{ℬ}{𝒜}$ method always outperforms the ${𝒦}{𝒩}$ method.

In this paper, an efficient Twice Finite Point-set Method (TFPM) coupled with difference scheme is proposed to solve the time-fractional Cahn–Hilliard (TF-CH) equation, and then it is extended to predict the phase separation process under nonlocal memory dominated by two-component TF-CH equations for the first time. The proposed meshless schemes are motivated by the following: (a) a high-order accuracy difference scheme is employed to approximate the time Caputo fractional derivative; (b) the fourth-order spatial derivative is divided into two second-order derivatives, and it is discretized by the FPM scheme continuously twice based on Taylor expansion and weighted least squares; (c) the Neumann boundary can be accurately imposed on the FPM scheme. In the numerical experiments, the error and numerical convergence of the proposed meshless method are first tested, which has near second-order convergent rate. Subsequently, the evolution of phase separation under memory dominated by single CH equation versus time is numerically investigated by the proposed method, and compared with the results in other literatures. The influence of fractional parameter on the separation phenomena is also discussed. Finally, the proposed method is used to predict the phase separation process under different parameters dominated by coupled TF-CH equations. All the numerical results show that the proposed coupled method is accurate in solving the TF-CH and efficient in predicting the phase separation evolution.

The increasing uncertainties in power systems have brought non-negligible influences on the dynamic behaviors. An accurate and efficient simulation method considering the effects of stochastic disturbances is of critical importance for the analysis of the dynamic performance of power systems. In this paper, a methodology for the simulation of stochastic transients in power systems is developed based on frequency shifting theory. The stochastic differential equations describing the stochastic process of parameter migration are represented through analytic signals. The frequency shifting operation is introduced. The Fourier spectra of analytic signals are shifted to reduce their maximum frequency contents, thereby permitting a larger time-step in time-domain simulation in accordance with Shannon’s sampling theory. Through the trapezoidal Milstein scheme, the stochastic differential equations with shifted analytic signals are discretized. Branch companion models that process analytic signals for the simulation of stochastic transients are formulated. The numerical properties of branch models are further examined. The analysis of test cases demonstrates that the proposed method can be used to represent stochastic transients caused by parameter migrations accurately and efficiently.

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.

Realistic simulation of dynamic interaction between plants and different natural environmental factors is a key topic in the field of natural landscape simulation. At present, few researches focus on the accumulation of snow on plants and the simulation of the dynamic deformation of a branch under the influence of the accumulation of snow. Therefore, we propose a simulation method for the dynamic interaction between snowfall and plants based on physical models. We first analyze the structure of the target plant and establish its physical structure model. Then we use the particle system and the plant branch collision detection method to calculate the amount of snow accumulation on the branches and generate the fluctuating snow grid surface based on the Berlin noise function. Finally, the cantilever beam model is used to calculate the deformation of branches under snow pressure. This modeling method proposed in this paper can provide a new research idea and technical reference for simulating the dynamic interaction between plants and environmental factors represented by snow.

This paper describes the novel mathematical modeling of the 3-phase induction motor, various operations and study of its dynamic behavior and regenerative braking based on the model. This model uses v/f scheme to apply braking operation with the energy flow to the supply system instead of wasting the energy in braking resistor. It discusses the theory of induction motors, which is explored through both equations and computer simulation model using SIMULINK. The model also helps to study the behavior of the motor during its starting and load variation. The results from the analysis prove the demonstration of regenerative braking and the dynamic behavior and can give an opportunity to learn the different characteristics during these conditions.

We introduce two models of industrial drying — a simplified one-equation model, and a detailed three-equation model. The purpose of the simplified model is rigorous validation of numerical methods for PDE-constrained optimal control. The purpose of the detailed model is to be able to predict and control the behavior of an industrial disk drier. For both models, we introduce a fully validated numerical method to compute the optimal source term to maintain the outlet temperature as close as possible to the set-point temperature. By performing simulations using realistic parameters for industrial driers, we illustrate potential applications of the method.

This paper plans to implement a novel detection model of maliciously encrypted internet protocol network flow using the deep structured concept. The major processing levels are (i) data collection, (ii) feature extraction, (iii) optimal feature selection, and (iv) detection. In the beginning, the standard dataset is taken from online databases. The deep convolutional neural network (DCNN) is introduced for the deep feature extraction process. The accurate features are chosen by the crossover decision-based krill herd algorithm (CD-KHA) which helps to minimize the training complexity of the deep structured architecture. These selected features are given to the hybridized deep learning with long short-term memory (LSTM) and deep neural network (DNN). Here, the structural design of the model is improved by the same CD-KHA. Through the comparison and analysis, the accuracy rate of the offered method shows higher performance than the other baseline approaches.

Recently, the Distributed Generation (DG) structure has become an important aspect of daily planning and has achieved a variety of functions while maximizing the functionality of utilities. It should balance the energy equilibrium of the system components and the production limit. Uncertainties arise during the operation of the DG, represented by generator power, load requirements and fluctuations in electricity prices. The reactive power problem is the major concern associated with the DG system. This paper analyzes both DG and an energy storage system along with their optimal performance using a specific algorithm. For optimal analysis of DG and ESS, an Improved Artificial Bee Colony Algorithm (IABC) is proposed. The IABC evaluates the performance of distribution network. It takes DG and ESS into account for analyzing optimal design problems, the main goal of which is to minimize their reactive power dispersion functions. In addition, there are barriers to reducing the DG’s operating costs. The proposed method is implemented on the MATLAB platform and tested using the IEEE 33-bus system. To confirm the effectiveness of the proposed method, it will be compared with the existing methods such as artificial bee colony (ABC) and levy-based method, respectively.

Distant supervision has been proven to be an efficient way of generating labeled instances for Named Entity Recognition (NER). However, it suffers from dictionary biases and ambiguous entities, resulting in noisy and incomplete labels. To overcome this drawback, this paper proposes a template augmented distant supervision framework, which generates high-quality labeled training data with minimal human effort. Specifically, we use distant supervision to extract sentences that contain entities and apply a pre-trained language model to encode these sentences. The encoded sentences are clustered and then for each cluster, three sentences are sampled out to form a seed template pool. The seed templates are calibrated and decomposed to decouple the connection between different parts. Finally, the seed templates and entity dictionary are combined with pre-trained language model to generate semantically coherent and precisely labeled training data. Experimental results on the EC and NEWS datasets and a practical electronic after-sale Q&A dataset with multiple pre-trained language models demonstrate that the proposed framework is able to improve the F1 score of the distantly supervised NER models by 7.9%–12.9%.

In this paper, we solved a model of a well-known infectious disease called dengue fever via fractional natural decomposition and modified Predictor–Corrector (PC) methods. A study of the dengue epidemic in the Cape Verde Islands off the coast of West Africa in 2009 has been resumed here for a better understanding of the results. The results are obtained using Liouville–Caputo and new generalized Caputo-type fractional derivatives. The numerical simulations are presented for various orders of given derivatives. Existence and uniqueness analysis of the given problem are also performed in the new generalized Caputo sense. The explored results are verified using figures. The main target of this paper is to explore the different dynamics of the given dengue fever model via two types of fractional numerical algorithms.

Cervical cancer (CC) has become one of the serious and deadly diseases in women around the world. Predicting a CC is at risk because testing is not possible at the early stage. Doctors predict the types of cancer by collecting cervical cells and by intervening in person. This approach affects the level of prediction due to human negligence, high cost, and is time consuming. In this paper, we propose the automatic cancer classification using deep neural network to overcome this problem. The proposed work has four stages namely, pre-processing, outlier elimination, dimensionality reduction, and classification. Initially, the missing test data should be removed. Second, the inconsistent data are eliminated based on identical values. Third, principal component analysis (PCA) is used to overcome the limitations caused by high-volume data. Finally, enter the reduced size database in the proposed neural network (DNN) classifier and classify the input data as normal or abnormal. We implement the suggested method using Python and we inspect the performance in terms of different metrics.

Rapid development of artificial intelligence techniques significantly promotes important medical imaging applications such as disease diagnosis, medical image analysis and cross-modality synthesis. It is valuable to synthesize Computed Tomography (CT) scans of human brains from Magnetic Resonance Imaging (MRI) scans because CT images contain particular information for diseases such as malignant tumors, cerebrovascular malformations and aneurysms. Although there has been many studies trying to apply machine learning methods (e.g., fully convolutional neural networks and generative adversarial networks) to predict CT images based on MRI, there are still opportunities to increase the quality of synthesized CT images using the latest machine learning algorithms. In this study, we aim to synthesize high-credibility human brain CT images from MRI images using machine learning approaches. We build a dataset of both CT and MRI images of 41 patients from The Cancer Imaging Archive. A data pre-processing pipeline is designed to guarantee that the processed images are suitable as inputs to machine learning algorithms. The pipeline includes (1) pairing MRI and CT scans according to a certain time interval between CT and MRI exams of the same patient, (2) registering all MRI images to a standard MRI template, (3) registering all CT images to paired MRI images, (4) intensity normalization and (5) extracting 2D slices from 3D CT and MRI volumes. Three deep neural networks with encoder–decoder structure are selected and implemented including U-Net, U-Net++ and pix2pix to synthesize human brain CT images from MRI images. Experimental results illustrate that U-Net++ shows better performance than U-Net and pix2pix in synthesizing CT images from MRI by achieving a Mean Squared Error of 0.025, a Mean Absolute Error of 0.082 and a Peak Signal-to-Noise Ratio of 67.90, respectively. The trained machine learning models from this work can be applied in future human brain CT image prediction where CT images are absent and MRI images are available for specific patients, thereby supporting disease diagnosis based on CT images.