International Journal of Modeling, Simulation, and Scientific Computing

Power grid partitioning decomposes a large power grid into several clusters. Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases. As a result, a cluster will inevitably be assigned to two or more power grid corporations. This assignment obstructs inner-cluster monitoring and control applications of the transmission system. To overcome the limitation, this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy. A line-based binary coding technique is developed to ensure the cluster connectivity. Three partitioning indices are considered: the coherency, the cluster connectivity, and the number of clusters. Finally, the proposed partitioning method is applied to IEEE 9-bus system, IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid (FCMdd) algorithm.

This paper improves the algorithm of point cloud filtering and registration in 3D modeling, aiming for smaller sampling error and shorter processing time of point cloud data. Based on collaborative sampling among several Kinect devices, we analyze the deficiency of current filtering algorithm, and use a novel method of point cloud filtering. Meanwhile, we use Fast Point Feature Histogram (FPFH) algorithm for feature extraction and point cloud registration. Compared with the aligning process using Point Feature Histograms (PFH), it only takes 9min when the number of points is about 500,000, shortening the aligning time by 47.1%. To measure the accuracy of the registration, we propose an algorithm which calculates the average distance of the corresponding coincident parts of two point clouds, and we improve the accuracy to an average distance of 0.7mm. In the surface reconstruction section, we adopt Ball Pivoting algorithm for surface reconstruction, obtaining image with higher accuracy in a shorter time.

Cloud Manufacturing (CMfg), combining with the technologies of Cloud computing and Internet of Things, is an intelligent networked manufacturing model, which can quickly integrate various distributed manufacturing resources for collaboratively completing the complex and customized manufacturing tasks. One of the key technologies supporting this model is the optimal manufacturing resources in the CMfg systems, typically machine tools (MTs). In this paper, the attributes of MTs in cloud environment are analyzed, the constraint relationship between the attributes and the optimization criteria of MTs is established, and an optimization method of MTs based on rough set is proposed. Finally, a case study is discussed to validate the feasibility and effectiveness of the proposed method.

The main objective of this work is to check the compatibility of Finite element method (FEM) to determine the modal parameters of the rotor system and to compare the results with analytical method. Current work is an attempt to implement different finite element (FE) models for the estimation of modal parameters. Two different order elements from Beam, Axisymmetric and Solid FE models are considered. The analysis is carried out on the rotor system and the accuracy of the results and the computation time of the FE models are estimated. The FE results are compared with the analytical method. It is observed that FE model can be used to simulate even at higher order frequencies, which is a limitation of analytical model. The analysis shows that the FE results closely match with analytical method and also the study summarizes that proper choosing of FE helps in developing rotor models in both a desirable accuracy and efficiency. Finally, a Campbell diagram is generated using the analytical method and FEM. The critical speeds are also calculated from the Campbell diagram. The closeness of the results between the two methods is checked.

The probabilistic model to represent the behavior of an applied multi-agent system that introduces the interaction between a set of agents and Player has been developed within the framework of player-centered probabilistic computer games. The approach features are given with the aid of a game developed for testing cognitive abilities. The agent’s behavior is nondeterministic and therefore unpredictable from Player viewpoint. The system allows both coordinated and autonomous agent’s behavior that depends on availability of information about the presence and position of workable agents for each other. Agent’s behavior is determined with the aid of the algorithm that includes identification of the probabilistic model parameters using maximized objective functions representing individual and group probabilities for Player defeating. Both the model and algorithm ensure the behavior control for relevant applied multi-agent systems.

In the distributed data-intensive computing environment, securely assigning tasks to appropriate machines is a big job scheduling problem. The complexity of this problem increases with the number of jobs and their job times. Several meta-heuristic algorithms including particle swarm optimization (PSO) technique and variable neighborhood particle swarm optimization (VNPSO) technique are employed to solve the problem to a certain extent. This paper proposes a modified PSO with scout adaptation (MPSO-SA) algorithm, which uses a cyclic term called mutation operator, to solve the job scheduling problem in the cloud environment. The comparative study between the proposed MPSO-SA scheduling mechanism and the conventional scheduling algorithms show that the proposed method decreases the probability of security risk on scheduling the jobs.

With the advancement in the science and technology, cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources. Load balancing of cloud computing environments is an important matter of concern. The migration of the overloaded virtual machines (VMs) to the underloaded VM with optimized resource utilization is the effective way of the load balancing. In this paper, a new VM migration algorithm for the load balancing in the cloud is proposed. The migration algorithm proposed (EGSA-VMM) is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory. In our approach, the migration is done based on the migration cost and QoS. The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA. The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization, which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.

The seat-cabin system of fork lift trucks has a great influence on ride comfort. On the basis of the structure and characteristics of the seat-cabin system for a high-grade fork lift truck, a seat-cabin system vertical dynamic model was created. The vertical dynamic responses of the model under the random irregularities excitation were calculated. The calculated responses including the seat dynamic response and the cabin dynamic responses at the four corners are very close to the field measurement data and the relative deviations of the (root mean square) RMS acceleration responses are all less than 6.0%. The results show that the vertical dynamic model is acceptable and can truly describe the basic mechanical behavior of the seat-cabin system. The model provides a good basis for investigating the relations between the dynamic parameters and the vibration attenuation characteristics of the seat-cabin system to further improve ride comfort.

Deep neural networks have achieved state-of-the-art performance on many object recognition tasks, but they are vulnerable to small adversarial perturbations. In this paper, several extensions of generative stochastic networks (GSNs) are proposed to improve the robustness of neural networks to random noise and adversarial perturbations. Experimental results show that compared to normal GSN method, the extensions using adversarial examples, lateral connections and feedforward networks can improve the performance of GSNs by making the models more resistant to overfitting and noise.

To investigate the dynamical load sharing behaviors of multi-floating components in the heavy load planetary gear system, a multi-floating planetary gear system that includes a floating central component and a quasi-floating planet flexible supporting pin is employed. Then a 21 degree of freedom lumped parameters dynamical model of this system is presented to study the dynamical load sharing behaviors. Some influencing factors, such as supporting stiffness, positions error of sun or carrier, and external input load are analyzed on the dynamical load sharing of the planetary gear system with multi-floating components. The results demonstrate that the load sharing condition of the system is best when both the sun gear and planet gears are multi-floating at the same time. When the planet gear position errors remain constant, reducing the flexible pin stiffness of planet gear or increasing external input load can effectively improve the load sharing. These conclusions are verified by the relevant experiments.

Difficult decisions and problems arise in almost all areas of human activities. In most tasks, the choice of the best alternatives is produced on the basis of task of numeral estimations of alternatives on the criteria of quality. The article considers the problem of multi-criteria choice with nonuniform scales of criteria. Algorithm for pairwise comparison of alternatives, based on the construction of the criteria preference matrices, containing information on the degree of superiority of one alternative over another, is offered. Transitivity of aggregated relation, built for two criteria, is proved. The results, obtained using the method of additive convolution and the proposed algorithm, are compared.

Virtual simulation technology has become one of the most popular technologies in the field of engineering education after the multimedia information technology in recent years. This paper, based on the comprehensive integrated simulation and verification module of UG NX software, describes and discusses a novel virtual simulation system teaching (VSST) for numerically controlled machining to support the student engineering training to achieve the theoretical knowledge and practical techniques in numerically controlled machining. The findings of a study designed to evaluate the impact of VSST for the development of numerically controlled machining course are presented here. In addition, analysis of the follow-up surveys indicates that the VSST method enables to provide the concrete experience of interaction between the students and the simulation environment and to further stimulate students’ interest in learning, so that the students who used VSST achieve significantly higher results than their co-workers.

A nonlinear SEIR mathematical model is developed to investigate the impact of migrated population, infected with Ebola virus, on human-to-human transmission of Ebola Virus Disease (EVD) in a disease-free area. In view of the dynamics of Ebola virus disease, here, the infected class is supposed to be divided into subclasses, viz. primary and secondary infected. The proposed model is analyzed qualitatively using the stability theory of differential equations and quantitatively using numerical simulation. The obtained results, qualitatively and quantitatively, suggest that migration and contact rates play an important role in controlling the spreading of disease. Critical values for migration and contact rates are evaluated and it is revealed that if these rates go beyond their critical values, it leads to delay in the stabilization of the system. It is also found that primary reproductive number increases with increase in migration rate. Besides this, the approximate time required to attain stability of the disease model system is also determined. The model analysis recommends quarantining the noninfected from the secondary infected in order to control the spreading out of disease.