International Journal of Modeling, Simulation, and Scientific Computing

Object detection is the most fundamental but challenging issues in the field of computer vision. Object detection identifies the presence of various individual objects in an image. Great success is attained for object detection/recognition problems in the controlled environment, but still, the problem remains unsolved in the uncontrolled places, particularly, when the objects are placed in arbitrary poses in an occluded and cluttered environment. In the last few years, a lots of efforts are made by researchers to resolve this issue, because of its wide range of applications in computer vision tasks, like content-enabled image retrieval, event or activity recognition, scene understanding, and so on. This review provides a detailed survey of 50 research papers presenting the object detection techniques, like machine learning-based techniques, gradient-based techniques, Fast Region-based Convolutional Neural Network (Fast R-CNN) detector, and the foreground-based techniques. Here, the machine learning-based approaches are classified into deep learning-based approaches, random forest, Support Vector Machine (SVM), and so on. Moreover, the challenges faced by the existing techniques are explained in the gaps and issues section. The analysis based on the classification, toolset, datasets utilized, published year, and the performance metrics are discussed. The future dimension of the research is based on the gaps and issues identified from the existing research works.

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality (PQ) disturbances. Features are extracted from the signals using fast kurtogram, envelope of filtered voltage signal and amplitude spectrum of squared envelop. Proposed algorithm can be implemented for the recognition of the complex PQ disturbances, which include the combination of voltage sag and harmonics, voltage momentary interruption (MI) and oscillatory transient (OT), voltage MI and harmonics, voltage sag and impulsive transient (IT), voltage sag, OT, IT and harmonics. Proposed work has been performed using the MATLAB software. Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform (DWT) and fuzzy C-means clustering (FCM).

Just-in-time (JIT) part feeding is adopted by more and more automobile producers. Based on this part feeding policy, vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts required until the next arrival of the vehicle. However, if there are uncertain travel times, a shortage of materials in stations will be caused. In this paper, the JIT part feeding optimization problem under travel time uncertainty is studied. The uncertain travel time is represented by the interval number according to the actual situation. To minimize the largest possible vehicle trip time, the optimization model is developed based on robust optimization. In the model, a route-dependent uncertain parameter is introduced. Through this model, the route of each vehicle and the parts load needed to be delivered by the vehicle can be calculated. A hybrid simulated annealing algorithm is designed to solve this model. The parts feeding planning for an engine assembly line is taken as an example. By the Monte Carlo simulation, the relationship between the line stoppage probability and the uncertain parameter is studied to obtain the final solution. The effectiveness of the method is demonstrated by this case study.

To improve the uniformity of the flow field and the poor axial velocity in the chamber of Si₃N₄ dry granulation, the influence of geometric parameters at open turbine combined structure on the flow field distribution is studied. The Euler–Euler gas-solid two-phase flow model is established and the physical model of dry granulation chamber under the combined structure is simplified. Under the same radial structure, the volume distribution and velocity field of Si₃N₄ particles in the granulation chamber with a different number and angle of the axial structure at the open turbine are analyzed by the CFD method. The influence of the axial structure at the open turbine on the flow field distribution of Si₃N₄ particles under different geometric parameters is compared. The results show that the axial structure of the open turbine in the granulation chamber is the most uniform when the number of blades is 6 and the inclination angle is $45^{\circ }$, and the circulating flow of the upper and lower parts of Si₃N₄ powder is strong.

In this paper, we investigated the day-ahead scheduling of network consisting of several distributed generation sources and connected loads. This scheduling is an optimization problem that has two objective functions, namely, operating costs and the cost caused by the emission of pollutants. In this scheduling, while introducing a new structure for micro-grid utilization, the possibility of exchanging power among networks and exchanging power between networks and the upstream distribution network is provided. The uncertainty of distributed wind generation sources and solar units has been investigated using appropriate probabilistic distributions. Incent-driven load-responsiveness management programs have been used as to encourage subscribers to reduce their power consumption during high-load hours. Whale optimization algorithm has been used for scheduling of network.

Mobile Cloud Computing (MCC) is a modern architecture that brings together cloud computing, mobile computing and wireless networks to assist mobile devices in storage, computing and communication. A cloud environment is developed to support a wide range of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictable increases in the resource requests that have a crucial impact on policies of resource allocation. How can the cloud system efficiently handle such burstiness when the cloud resources are limited? This problem becomes a hot issue in the MCC research area. In this paper, we develop a system model for the resource allocation based on the Semi-Markovian Decision Process (SMDP), able of dynamically assigning the mobile service requests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finite-state Markov-Modulated Poisson Process (MMPP). Numerical results show that our proposed model performs much better than the Greedy algorithm in terms of achieving higher system rewards and lower service requests blocking probabilities, especially when the burstiness degree is high, and the cloud resources are limited.

In a wholesale shop, the retailer is required to keep an inventory of various ranges of a product due to uncertainty in consumer preferences and behavior. Different Economic Order Quantity (EOQ) models for each variety of a product are needed for optimum level of inventory. EOQ model decides how much stock a shopkeeper has to store, according to demand and deterioration of different items to optimize the inventory policy of multiple products. Items like fashion, food, vegetables, pharmaceuticals, etc., deteriorate in the retailer’s warehouse during storage. An investment is required in preservation technology to significantly reduce the deterioration rate, reduce economic losses, and increase business competitiveness. Here, a multi-item deteriorating EOQ model with a fixed cycle duration has been developed incorporating the preservation technology. The demands for the items are a quadratic function of the selling price, and shortages are allowed. The main objective is to determine the optimal selling price, investment under preservation, the time length up to zero inventory, order quantity of the proposed inventory model such that the overall profit will be maximized. First, we prove that the optimal selling price and investment in preservation exist and are unique. Next, the total profit per unit time is a concave function of selling price and preservation technology investment. An algorithm is designed to obtain the optimum joint policy. A numerical example has been given to illustrate the model, and sensitivity analysis is conducted, which gives some managerial insights.

Based on the equivalent integro-differential form of the considered problem, a numerical approach to solving the two-dimensional nonlinear time fractional wave equations (NTFWEs) is considered in this paper. To this end, an alternating direction implicit (ADI) numerical scheme is derived. The scheme is established by combining the second-order convolution quadrature formula and Crank–Nicolson technique in time and a fourth-order difference approach in space. The convergence and unconditional stability of the proposed compact ADI scheme are strictly discussed after a concise solvability analysis. A numerical example is shown to demonstrate the theoretical analysis.

Human emotions are considered to be contagious that can be transmitted from one person to another during social interactions. However, interaction with people having excessive negative emotions may make other people depressed. Taking this aspect into consideration, a SIR nonlinear model is formulated to investigate the transmission dynamics of depression in social connections. Through qualitative analysis of the model, critical value of depression is determined. Two equilibria, viz. depression free and depression state, are obtained and their stability behaviors are analyzed. The stability analysis reveals the fact that both the equilibria are locally and globally stable, depending on critical value of depression. The sensitivity analysis of crucial parameters of critical value of depression is performed to point out the key parameters that can drastically influence the propagation of depression. The results demonstrate the importance of counselling for depressed persons in order to suppress the growth of depression in social connections. The model calibration has been done for the annual depression cases of Zimbabwe. The results of our study show that individuals will catch major depression disorders more quickly once they get initially (primary) depressed. For this, counselling rate for primary depressed population should be increased.

In this paper, based on the Lagrange Multiplier approach in time and the Fourier-spectral scheme for space, we propose efficient numerical algorithms to solve the phase field crystal equation. The numerical schemes are unconditionally energy stable based on the original energy and do not need the lower bound hypothesis of the nonlinear free energy potential. The unconditional energy stability of the three semi-discrete schemes is proven. Several numerical simulations in 2D and 3D are demonstrated to verify the accuracy and efficiency of our proposed schemes.

This article describes the mathematical model derivation describing river blindness disease transmission in human and vector (blackflies) host population. The effect of incomplete resistance to re-infection in human individuals who recovered from the disease after treatment but are still subjected to repeated exposures to infected blackflies bite is investigated. Also, the basic reproduction number $(R_{hb})$ is obtained and it is shown that if $R_{hb}<1$, the onchocerciasis-free equilibrium is locally and globally asymptotically stable. Also, if $R_{hb}>1$, the onchocerciasis-endemic equilibrium is globally asymptotically stable. Moreover, the Differential Transform Method (DTM) and Runge–Kutta fourth-order method is employed via the computational software Maple 18 to solve and obtain the approximate solutions of the model system equations, which showed that the numerical results favorably compare with each other. Simulations reveal that increase in biting and transmission rates leads to an increase of $R_{hb}$ and incomplete resistance to re-infection due to consistent exposure to blackflies bites. Also, simulations of the approximate solutions of the model state equations are provided.

In this study, the flow structure and effect of different pump rotational speeds on a centrifugal pump’s performance are experimentally and numerically investigated. The internal flow field pattern within the pump has been analyzed and discussed using the CFD technique. The numerical results are compared with experimental data under a wide range of operating conditions. The comparison results between them have indicated a considerable agreement. The pressure variations are gradually increasing from inlet to outlet impeller of the pump. The results note that when the impeller rotates near the tongue region, the pressure in this region was higher than in other parts. Also, the interaction between the impeller and volute tongue region is actually according to the impeller blades’ relative position concerning the tongue region. Furthermore, the pressure and velocity variations within a centrifugal pump increase with rotational impeller speed.

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.