International Journal of Modeling, Simulation, and Scientific Computing

As an emerging simulation technology in the field of system modeling and simulation, the equipment symbiotic simulation has become research emphasis. In the field of equipment maintenance support, the outstanding problem of equipment remaining useful life (RUL) prediction is analyzed, i.e., the stable model parameters without self-evolution ability, which has become the primary factor that hinders self-adaptive prediction of equipment RUL. Combined with parallel systems theory, the equipment RUL prediction oriented symbiotic simulation framework is proposed on the basis of modeling analysis and Wiener state space model (SSM) is taken as the basic simulation model in the framework. Driven by the dynamic injected equipment degradation observation data, the model parameters are updated online by using expectation maximum (EM) algorithm and the data assimilation between simulation outputs and observation data is executed by using Kalman filter, so as to realize dynamic evolution of the simulation model. The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for predicting equipment RUL accurately. The framework is verified by the performance degradation data of a bearing. The simulation results show that the symbiotic simulation method can accurately simulate the equipment performance degradation process and the self-adaptive prediction of equipment RUL is realized on the basis of improving prediction accuracy, proving the feasibility and effectiveness of symbiotic simulation method.

Technology of brain–computer interface (BCI) provides a new way of communication and control without language or physical action. Brain signal tracking and positioning is the basis of BCI research, while brain modeling affects the treatment analysis of (EEG) and functional magnetic resonance imaging (fMRI) directly. This paper proposes human ellipsoid brain modeling method. Then, we use non-parametric spectral estimation method of time–frequency analysis to deal with simulation and real EEG of epilepsy patients, which utilizes both the high spatial and the high time resolution to improve the doctor’s diagnostic efficiency.

In order to solve to the technical bottleneck that powder is easy to reunite and without refining in the vibration ultrafine grinding (UFG) technology, the energy field of medium flow was studied by analyzing crushing energy and energy transfer. The numerical simulation model of medium flow based on Particle Flow Code (PFC) was established. By setting four kinds of working conditions of amplitude and frequency, the dynamic graphics and curves of the energy field (such as kinetic energy, strain energy, velocity field, force chain and so on) were obtained. In the situation of mid-frequency with large amplitude, the average speed of front medium flow was 1.3–5.03 times that of others and the low-energy region was decreased by 6% to 10%. The largest kinetic energy and strain energy were 3.25 and 2.94 times the average value of others, respectively. The diamond UFG was analyzed in new vibration mills under the conditions of low frequency with large amplitude and mid-frequency with large amplitude. Utilizing a laser particle size analyzer, it was discovered that the particle sizes d(50) in these two models were 3.840$\mu$m and 0.260$\mu$m and bandwidths were 9.940$\mu$m and 3.825$\mu$m. This highlights the effect of mid-frequency with large amplitude in particle refining and bandwidth narrowing, which is of great importance in the fields of ultra-hard particle refining research and energy utilization.

In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.

Conical picks are the key cutting components used on roadheaders, and they are replaced frequently because of the bad working conditions. Picks did not meet the fatigue life when they were damaged by abrasion, so the pick fatigue life and strength are excessive. In the paper, in order to reduce the abrasion and save the materials, structure optimization was carried out. For static analysis and fatigue life prediction, the simulation program was proposed based on mathematical models to obtain the cutting resistance. Furthermore, the finite element models for static analysis and fatigue life analysis were proposed. The results indicated that fatigue life damage and strength failure of the cutting pick would never happen. Subsequently, the initial optimization model and the finite element model of picks were developed. According to the optimized results, a new type of pick was developed based on the working and installing conditions of the traditional pick. Finally, the previous analysis methods used for traditional methods were carried out again for the new type picks. The results show that new type of pick can satisfy the strength and fatigue life requirements.

A coupled adsorption–desorption thermo-kinetic model is developed incorporating both adsorption and desorption reactions. A local pseudo-equilibrium condition at the interface of adsorbent and adsorbate bulk phases was used as isotherm equation which can even be applied for multi-pollutants scenarios. The developed model is then validated using collected experimental data of heavy metal ions (Pb, Cu, Cd, Zn, and Ni). Comparisons were made for a number of isotherm and kinetic models to examine the performance of the proposed model. The developed model revealed desirable accuracy and superiority over other models in predicting the adsorption behavior and can be used for other systems of concern. The model correlates the adsorption kinetic with an $R^2$ value of 0.9391 and desorption kinetic with an $R^2$ value of 0.9383. By application of the proposed model to any available adsorption datasets, the individual characteristics of adsorption and desorption can be determined.

In this paper, we present the effect of process parameters variations on $I$–$V$ characteristics of LDD MOSFETs through a simulation study, applying also to any submicron device. In particular, we examine the effect of variation of ionic implantation for different channel doping involved in MOSFET production. At last, we examine a linear $I$–$V$ model to simulate more adequately the effects of variation of the process parameters as correction to the base model.

Crowd modeling and simulation have drawn much attention in recent decades due to the functionality of recurrence of the crowd movement pattern in an efficient way. Much effort has been paid aiming at generating an accurate simulation result with respect to different aspects of crowd movement pattern. A fact has been observed that footprint left in mud or turf significantly affects pedestrian’s decision making and moving trajectory since those footprints help other pedestrians walk comfortably. Inspired by this, we in this paper propose a crowd simulation model aiming to model how the movement of previous pedestrians affects decision making process of the pedestrians coming later. Unlike pedestrians leaving footprint in mud or turf, pedestrians leave no marks on hard surface. We consider each step of pedestrian moving on hard surface as a mutable invisible footprint which further forms a virtual trail. We first build a model to simulate how the invisible footprint forms and evolves on hard surface, upon which an agent-based crowd simulation model is then built to simulate how pedestrian makes trade-off between the invisible trajectory and the shortest path. The proposed model is validated by case studies with two scenarios. The simulation results indicate that we are able to simulate the impact on pedestrian’s decision making by the invisible footprint.

Facility layout problem (FLP) contemplates the optimization of space utilization, costs, and material flow. Commercial simulation softwares are commonly used to validate layouts by simulating the real world material flow and production process. To validate a layout scenario of a solar module assembly workshop, a 3D simulation model is presented which was designed on VisualComponents software. To begin with, we introduce the layout characteristics and use the systematic layout planning (SLP) method to design the block layout of workshop. Then, we built components library which contains all the facilities needed in the production line, specifically conveyors, processing equipments, robots, buffers. Finally, we have established a simulation layout model using the components in the customized library according to the block layout, and configured the components which were allocated in the layout by setting the parameters and building the connection between components via signal. To improve the performance of the workshop, we have found the best work-in-process (WIP) value for the workshop and the optimal number of workers. Additionally, the layout model was used to test collision and reachability. It demonstrated the production process in a realistic simulation environment which helped in the sales process as well. The reusable component models we have built make it easy to build a layout within a short time and validate the effectiveness of the layout scenarios for any other projects.

In this work, dynamics of diffusion of innovation in the smartphone markets is modeled by using an agent-based simulation in a period of 28 quarters starting from 2009. Rapid changes in smartphone technology affected the consumer preferences and two operating systems, namely Android and iOS, out of over 10 operating systems, and dominated the smartphone markets in a period of 6 years. The model aims to study the conditions of competition and adoption among similar high technology products. Relative roles of the essential parameters, namely affordability and social effects have been the main interest points in these studies. For this end, a simple, Cobb–Douglas production relation-based model on simple square lattice is introduced. The model parameters are adjusted to match the simulation results with the actual market shares data obtained from Statista. A clear relation between the model parameter values and global sales of different operating systems is observed.

A high-order accuracy time discretization method is developed in this paper to solve the one-dimensional nonlinear Dirac (NLD) equation. Based on the implicit integration factor (IIF) method, two schemes are proposed. Central differences are applied to the spatial discretization. The semi-discrete scheme keeps the conservation of the charge and energy. For the temporal discretization, second-order IIF method and fourth-order IIF method are applied respectively to the nonlinear system arising from the spatial discretization. Numerical experiments are given to validate the accuracy of these schemes and to discuss the interaction dynamics of the NLD solitary waves.

Various Wireless Sensor Network (WSN) applications require the common task of collecting the data from the sensor nodes using the sink. Since the procedure of collecting data is iterative, an effective technique is necessary to obtain the data efficiently by reducing the consumption of nodal energy. Hence, a technique for data reduction in WSN is presented in this paper by proposing a prediction algorithm, called Hierarchical Fractional Bidirectional Least-Mean Square (HFBLMS) algorithm. The novel algorithm is designed by modifying Hierarchical Least-Mean Square (HLMS) algorithm with the inclusion of BLMS for bidirectional-based data prediction and Fractional Calculus (FC) in the weight update process. Data redundancy is achieved by transmitting only those data required based on the data predicted at the sensor node and the sink. Moreover, the proposed HFBLMS algorithm reduces the energy consumption in the network by the effective prediction attained by BLMS. Two metrics, such as energy consumption and prediction error, are used for the evaluation of performance of the HFBLMS prediction algorithm, where it can attain energy values of 0.3587 and 0.1953 at the maximum number of rounds and prediction errors of just 0.0213 and 0.0095, using air quality and localization datasets, respectively.

Multi-level splitting algorithm is a famous rare event simulation (RES) method which reaches rare set through splitting samples during simulation. Since choosing sample paths is a key factor of the method, this paper embeds differential evolution in multi-level splitting mechanism to improve the sampling strategy and precision, so as to improve the algorithm efficiency. Examples of rare event probability estimation demonstrate that the new proposed algorithm performs well in convergence rate and precision for a set of benchmark cases.

In order to understand and organize the document in an efficient way, the multi-document summarization becomes the prominent technique in the Internet world. As the information available is in a large amount, it is necessary to summarize the document for obtaining the condensed information. To perform the multi-document summarization, a new Bayesian theory-based Hybrid Learning Model (BHLM) is proposed in this paper. Initially, the input documents are preprocessed, where the stop words are removed from the document. Then, the feature of the sentence is extracted to determine the sentence score for summarizing the document. The extracted feature is then fed into the hybrid learning model for learning. Subsequently, learning feature, training error and correlation coefficient are integrated with the Bayesian model to develop BHLM. Also, the proposed method is used to assign the class label assisted by the mean, variance and probability measures. Finally, based on the class label, the sentences are sorted out to generate the final summary of the multi-document. The experimental results are validated in MATLAB, and the performance is analyzed using the metrics, precision, recall, $F$-measure and rouge-1. The proposed model attains 99.6% precision and 75% rouge-1 measure, which shows that the model can provide the final summary efficiently.