International Journal of Modeling, Simulation, and Scientific Computing

Three transmit antennas Uncoded Space-Time Labeling Diversity (USTLD) systems were recently proposed as a direct extension to the two transmit antennas USTLD system. By employing more antennas at the transmitter, antenna — as well as — labeling diversity (LD) can be further achieved. Existing mapper designs for three transmit antennas USTLD systems are based on symmetrical heuristics, and hence no asymmetrical mapper designs can be produced. This paper proposes a multi-function optimization genetic algorithm for the three transmit antennas USTLD system that produces mapper designs irrespective of size or symmetry. The genetic algorithm was tested on 16-, 32QAM, 16-, 32APSK and 16APSK constellations that do not exhibit diagonal symmetry. The proposed genetic algorithm had produced mapper designs that match but did not improve upon existing mapper designs for 16QAM and 32QAM, respectively. The 16APSK and 32APSK mapper designs produced achieved diversity gains of $\approx 2.5\mathrm{\text{dB}}$ and $\approx 1.8\mathrm{\text{dB}}$ when compared to existing third mapper designs, as well as $\approx 3\mathrm{\text{dB}}$ and $\approx 4\mathrm{\text{dB}}$ gain over the two transmit antennas USTLD scheme at the BER of ${10}^{-5}$. Furthermore, when tested on 16APSK constellations that do not exhibit diagonal symmetry, these mappers achieved diversity gains between $\approx 1.6\mathrm{\text{dB}}$, $\approx 5\mathrm{\text{dB}}$ and $\approx 2\mathrm{\text{dB}}$ over the two-transmit USTLD scheme at the BER of ${10}^{-5}$, respectively. The computational complexity of the proposed genetic algorithm was analyzed based on a previous study, which was shown to be $\mathrm{\text{O}}(M!)$.

This paper delineates the mathematical modeling and dynamics of a novel coronavirus (COVID-19) an outbreak, and it is a control measurement; the effect of lockdown in terms of lakhs of cases and deaths. The lockdown effect is studied with a different lockdown success rate and also describes the multiple transmission route in the infection dynamics, and pushes the role of the environmental reservoir in the transmission and the spread of this disease. In this situation, mathematical models are an important tool to assign an impressive strategy in order to fight against this pandemic. We exhibit the boundedness of the system, the local stability analysis and global stability analysis of the equilibrium to examine its epidemiological relevance. We have also carried out numerical simulations to validate the analytical results.

This paper starts from the basic principles of speech recognition, starting from acoustic models and features, and applies deep convolutional neural networks to speech recognition processing. It also introduces the overall process of speech recognition and mainstream algorithms to classify and summarize the speech recognition system. Due to the technological innovation of speech recognition and offline speech recognition systems, we found that traditional systems have low interactivity, poor flexibility, poor recognition command library, and other problems. We provide solutions for this paper according to the specific application environment. This paper introduces the design and implementation of an artificial intelligence teaching management system based on the Internet of Things (IoT) technology. Based on the design of the entire system, the system databases such as the video surveillance module, artificial intelligence security module, and remote control module are designed and realized the functions of these modules. Finally, RFID technology and database technology will be used to realize the informatization of training room personnel and equipment management, and the positioning and storage inspection of training room equipment. Secondly, from the perspective of environmental monitoring of the training room, temperature/ humidity sensors and optical sensors are used to complete the collection of real-time environmental data in the training room, thereby transmitting the data to the host system. It realizes the pairing through the Zigbee wireless communication module. Real-time monitoring of training room and management system. This paper introduces speech recognition and artificial intelligence into the management system of the training room so that the training can be better recorded and managed.

According to the principle of minimum information, new information priority, and difference information, most existing grey forecast models and their improvement are inconsistent with the grey theory. Therefore, a novel discrete multivariable grey model with unequal fractional-order accumulation is proposed. To improve the accuracy and stability of the model, an optimization algorithm for unequal fractional-order is proposed. The proposed model and algorithm are evaluated with four actual cases. The results show that the novel model has better performance and the proposed unequal fractional-order accumulation operator is better than other existing accumulation operators. Considering the energy consumption, the carbon dioxide emissions in the USA have been forecasted to decrease but remain at a high level by using the novel discrete multivariable grey model. Reducing energy consumption is conducive to reducing carbon dioxide emissions.

The precise investigation of the Moore and Bilikam (MB) family of distributions is essential since they cover several distributions as special cases. We evaluate different estimation procedures for parameters of MB family comprehensively. Moreover, the confidence interval and highest posterior density interval estimators of parameters are provided. The different estimation methods of the parameters of two certain cases of the MB family are compared via the simulation data. Based on the simulation approach, we realized the estimates are convergent to the real values of the parameters and the root mean squared error (RMSE) values derived by the expectation-maximization (EM) algorithm are less than Newton–Raphson (NR), stochastic EM (SEM) and Bayes estimators. Also, the higher values of the sample size lead to better estimates in the sense close to true parameter values and having smaller RMSE values. The reliability analysis of MB family is another topic in this paper, with simulation discussion. The Monte Carlo simulations are provided to assess the performances of estimation methods. We concluded the Bayesian shrinkage estimators are better than other methods of the reliability function, which have the smallest values of the RMSE. The analysis of real dataset has been presented for illustrative purposes, which confirmed simulation results.

To accurately describe the killing effect of missiles especially dealing with the small or fast target, a new method for calculating the killing effect of missiles is proposed in this paper. The missile and the target are divided into several basic geometries based on their shapes, and then the equations of these geometries are established in their local frame. The mass center motion equations of the target and the missile are determined in the light of their initial positions and velocities, respectively, and every geometry moves with its center of mass according to its attitude. The equations of the fuze view field and the target can confirm whether the fuze detects the target, and whether the fragment hit the target can be certified by the same approach. This paper also writes a MATLAB simulation program for this calculation method and designs a new algorithm to determine the position relationship of space geometry using existing functions, and the simulation verifies the effectiveness of this method.

In December 2019, the novel Coronavirus, also known as 2019-nCoV or SARS-CoV-2 or COVID-19, was first recognized as a deadly disease in Wuhan, China. In this paper, we analyze two different nonclassical Coronavirus models to observe the outbreaks of this disease. Caputo and Caputo–Fabrizio (C–F) fractional derivatives are considered to simulate the given epidemic models by using two separate methods. We perform all required graphical simulations with the help of real data to demonstrate the behavior of the proposed systems. We observe that the given schemes are highly effective and suitable to analyze the dynamics of Coronavirus. We find different natures of the given model classes for both Caputo and C-F derivative sense. The main contribution of this study is to propose a novel framework of modeling to show how the fractional-order solutions can describe disease dynamics much more clearly as compared to integer-order operators. The motivation to use two different fractional derivatives, Caputo (singular-type kernel) and Caputo–Fabrizio (exponential decay-type kernel) is to explore the model dynamics under different kernels. The applications of two various kernel properties on the same model make this study more effective for scientific observations.

In our research work, we suggest the modified minimal model of fractional order and analyze it using the homotopy decomposition method (HDM). The minimal model is quite a useful mathematical model which describes the behavior of glucose-insulin metabolism. The original model was given in the 80s and has been updated over a different period. In this modified model, we add a one-factor diet which plays an important role in the blood-glucose analysis. We obtained the numerical results by using the homotopy decomposition method. HDM is extremely useful, significant, and very simple. We also discuss the existence and uniqueness of the fractional model.

In this paper, we aim to study the effects of fear of predation in the presence of a competitor species and availability of refuge for the prey population. A discrete-time model of prey–predator–competitor type is formulated to study the dynamical relationship between the interacting species by deriving the conditions for local stability, permanence and existence of Neimark–Sacker bifurcation. A hybrid control technique based on state feedback strategy is implemented to control the chaos in the system. Finally, some numerical simulations are carried out to illustrate the theoretical discussions.

Diabetes is a chronic disease which indicates the high level of body glucose level. As per the World Health Organization (WHO), 422 million people were diabetic until 2014. This paper develops an accurate classification machine learning model and an efficient usage of data pre-processing pipeline to improve overall accuracy. For the purpose, six algorithms: Support Vector Machine with Linear kernel (Linear-SVM), Support Vector Machine with RBF kernel (RBF-SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Decision Tree and Random Forest are used for classification purpose and their comparative accuracy is analyzed. Data Imputation, Oversampling and Feature scaling techniques are the constituents of Data preprocessing pipeline. Experiments are performed on a well-known dataset of National Institute of Diabetes and Digestive and Kidney Diseases, the PIMA diabetes dataset. The data preprocessing techniques, data imputation and Synthetic Minority Oversample Technique (SMOTE) analysis improved classification accuracy from 77% on raw data, to 88.12% (on Random Forest Classifier) and 91% (on ANN Classifier), respectively. Furthermore, a new feature generation approach is applied and performance is analyzed using the SVM model. Original data attributes BMI and Insulin are replaced with new features BMI_NORMAL and INSULIN_NORMAL, respectively. The significant improvement by proposed technique is confirmed by statistical testing followed by post-hoc analysis.

Breast cancer is one among the dreadful cancer which is caused due to formation in breast cells. Earlier recognition of breast cancer is most required in the medical field to avoid the dangerous threat to human life. This is carried out in the existing work, namely Predictive Modeling Technique (PMT). Existing work cannot handle the database with noises properly which might lead to inaccurate prediction outcome. These problems are addressed by introducing Deep Learning-based Breast Cancer Disease Prediction Framework (DLBCDPF). The proposed research framework objective is to present the structures for the disease diagnosis in a further accurate way. In this work, feature selection is achieved through optimization algorithm, namely ranking-based bee colony approach by which the most optimal feature is chosen from the training dataset. The fitness values considered in this work for optimal feature selection are F-score values. Each feature’s F-score and N numbers of feature’s F-score are arranged in a descending manner; in addition, feature subset with more than one feature are produced. In this phase, diagnosis of various stomach-related problems is done through a unique hybridized classification methodology. In this hybridization methodology, clustering is accomplished before classification, and data pruning is attained in every classification iteration. This leads to improved classification accuracy owing to efficient diagnosis. The clustering is attained by fuzzy C-means clustering, and classification is done using the improved deep neural network. The entire research analysis is carried out in python platform for breast cancer dataset from which it is substantiated that the suggested research work tends to outperform in an enhanced way than prevailing work.

Microservice-based software networks consist of a large number of critical, multidisciplinary, and interdependent elements in which the failure of critical microservices will have a destructive effect on the system reliability and resiliency. We overcome this problem by identifying critical microservices that play a vital role and then propose a fault-tolerant candidate for each critical microservice. The approach finds critical microservices based on the proposed parameter-based greedy strategies and analyzes the vulnerability of the software design in terms of resiliency while removing the important microservices from the software network. We examine our proposed microservice architectural style to fit the requirements of a smart city in cloud-native applications using a new metric introduced in terms of a microservice runtime degree (RD) and aggregative importance links. The results show the decline rate of network efficiency is maximum with removing the nodes based on this new structural metric. Furthermore, the resilience loss triangle metric based on RD, which corresponds to the shaded area between the expected and full performance of the observed curves, will have a greater value than other metrics.

With the characteristics of high-speed transportation, the elevator has an overwhelmingly crucial meaning footnote in an auxiliary evacuation high-rise building with more than 30 floors causing the time to be the deciding factor under the emergency condition. The study shows that using the stairway or elevator separately cannot achieve the best effect. The elevator’s evacuation time depends on the number of occupants on each floor and the elevator’s carrying capacity. This paper discusses how to use the stair and elevator to optimize the hybrid evacuation in high-rise buildings and proposes two goals for the evacuation model of high-rise buildings under emergency conditions. One is that both the last occupant to use the stairway and the last occupant to use the elevator reach the safe place simultaneously. The other is that we fine-tune the evacuation destinations of the occupants to balance the utilization of each evacuation path as much as possible. We present the optimization method with three steps, and the evacuation time shrunk by up to 57% according to the baseline. Finally, we compare the optimized hybrid evacuation and the single evacuation method to the previous work and give the results of the experiments.

The traditional generation of combat simulation scenarios often requires a manual understanding of conceptual scenarios and transformation into simulation scenarios. This method has the problems of long development time and high development threshold. Conceptual scenarios are usually visual Unified Modeling Language (UML) diagrams, so we can use artificial intelligence technology to extract key semantics from them, and automatically map the extracted semantics to simulation scenarios. This method is called the intelligent generation of combat simulation scenarios. To extract the key semantics from conceptual scenarios in UML form, we propose the UML diagram recognition method based on Keypoint Region-based Convolutional Neural Network (R-CNN). This method includes three parts: image character recognizer, primitive object detector, and image semantic extractor. First, we use optical character recognition (OCR) technology to achieve image character recognition. Second, we manually annotate the primitive target-detection dataset and propose a new primitive target-detection model—Keypoint R-CNN. This model considers the direction of connecting lines and realizes the detection of symbols and connecting lines. Third, we propose a targeted combined primitive detection and primitive relationship extraction method to extract the high-level semantics of UML diagrams. Then, we carried out experiments and evaluations on the self-made dataset. Compared with other methods, the F1 score of our method is improved by about 7%, and the Jaccard coefficient is improved by about 10%. Finally, we use a case study to implement the intelligent generation process of combat simulation scenarios using the UML recognition method we proposed. This case shows the operability and efficiency of our method. Our method greatly reduces the labor cost and development threshold of combat simulation scenario generation and improves the development efficiency of the combat simulation system.

In this paper, we define a splitting scheme for the N-level Bloch model which makes use of exact numerical solutions of sub-equations. These exact solutions involve matrix exponentials which we want to avoid to calculate at each time-step. The resulting scheme is nonstandard and preserves qualitative properties of the Bloch equations. We explore and compare numerically multiple ways to implement it and in particular take into account the specific structure of the Bloch equations.

The consideration of the polarization scattering characteristics of radar echo in the detection of small and dim targets in a sea clutter background leads to high detection probability and high recognition accuracy. In this study, a target detection method based on canonical polar decomposition (CPD) was developed. In particular, a polarization matrix T was constructed for the radar echo of each range bin; the absolute value A and phase $𝜃$ of T were extracted; the diagonal elements of the characteristic matrix H were extracted and converted into the singular spectrum form; and the negative entropies $E_A$ and $E_{𝜃}$ of the singular spectrum were calculated. We set $E=E_A{e}^{{jE}_{𝜃}}$; then, the negative entropy value $E$ of each range bin was obtained, and the range bin of the minimum value was identified, which was the target position. Thereafter, a seven-component scattering power decomposition (7SD) was performed on T. The basic scattering structure of the target was analyzed, and a polar feature description word (PFDW) was formed to recognize the target characteristics. The effectiveness of the proposed method was verified by using the sea clutter dataset obtained with the IPIX radar. The comparison with the statistical model-based method is also made for indicating the advantage of the proposed method.

Oil spill accidents occur in marine traffic accidents. Once the accidents happen, oil spill will drift and diffuse on the ocean as impacted by wind and tidal current, which has considerably impacted the work on wharfs and their surrounding environment. This study aimed to investigate the effect of wind, ocean current and tides on oil spill movement for the building of the drift and weathering models of oil particles to predict and assess the risks of oil spill on the ocean. In these models, the oil film drift algorithm considers the joint action of wind, ocean current, wave and density flow on oil spill. In this study, the fluid dynamic model was adopted to determine the drift motion generated by the tidal field, and the diffusion process was calculated with the random walk method. In addition, the random movement model of oil particles was employed for simulating the transport and weathering of the oil spill, as an attempt to simulate marine oil spill. Furthermore, the Ningbo port was taken as an example to obtain the numerical sum of the flow field obtained by the simulation, which can guide the prediction of oil spill risks near ports.