International Journal of Modeling, Simulation, and Scientific Computing

A mixed integer linear programming (MILP) approach for the joint simulation of electric control reserve and electricity wholesale markets is presented. This generation dispatch model extends an existing integrated grid and electricity market (IGEM) model covering the Continental European electric power system. By explicitly incorporating the markets for primary and secondary control reserves (PCR and SCR), the model can reproduce the decisions of generating unit operators on which markets get involved. Besides, the introduction of the integrality conditions allows considering start-up costs and the calculus of generating units to pass through the economically unattractive periods with low or even negative prices in order to avoid another start-up. Since this model is too large to be solved with common MILP solvers for the intended simulation time of one year, temporal and geographical interdependencies are used to solve it heuristically. The heuristic therefore splits the model into various sub-problems so that on the one hand, the number of variables, especially of integer variables, per sub-problem is reduced significantly and on the other hand, the relevant interdependencies remain considered. The heuristic is evaluated in terms of accuracy and computation time by means of two case studies. Both case studies show satisfactory accuracy and significant advantages in computation time.

To meet the rising demand of performing complex tasks in a highly technologized world, the development of mechatronic systems faces two major challenges: First, the system is integrated in a fast changing environment and has to cope with dynamically modified tasks and circumstances. At the same time, the development of new methods has to be fast, cost-effective and efficient. By taking care of both aspects simultaneously, simulations have become an acknowledged tool to cost-effectively and rapidly test and optimize new solutions for complex mechatronic systems, as they are common in, e.g., robotics. Mostly, simulation methods are specialized for one purpose and thus used as a stand-alone tool to analyze the behavior of single components or certain aspects of the whole system. But with size and complexity of the system, the susceptibility to errors rises when the interaction between components fails. If a component shows minimal deviations to its nominal behavior, huge interdependencies between components of complex systems might cause system failure. The structural behavior of a single component is therefore as crucial to the functionality of the whole system as the interplay of all components. Both aspects have to be analyzed in parallel, which is nowadays barely considered. Consequently, this work presents the integration of structural simulations into the overall picture. The developed approach consists of a concept, an implementation and the validation for an automated bidirectional interaction to integrate results from Finite Element Analysis (FEA) into an existing, real-time capable Overall System Simulation for mechatronic systems in general and robotics in particular.

In this note, we consider a new unknown input observer design for nonlinear systems. The main idea consists in determining the estimation error and mean value theorem parameters ($\beta$) to introduce them into proposed observer structure. This process is designed on the basis of mean value theorem and genetic algorithm. The stability study relies on the use of a classical quadratic Lyapunov function. The observer’s gains are determined systematically. For the validation of theoretical development proposed in this paper, we consider two practical realizations that deals with the secure communication problem.

We propose a novel Laplacian-based algorithm that simplifies triangle surface meshes and can provide different preservation ratios of geometric features. Our efficient and fast algorithm uses a 3D mesh model as input and initially detects geometric features by using a Laplacian-based shape descriptor (L-descriptor). The algorithm further performs an optimized clustering approach that combines a Laplacian operator with K-means clustering algorithm to perform vertex classification. Moreover, we introduce a Laplacian weighted cost function based on L-descriptor to perform feature weighting and error statistics comparison, which are further used to change the deletion order of the model elements and preserve the saliency features. Our algorithm can provide different preservation ratios of geometric features and may be extended to handle arbitrary mesh topologies. Our experiments on a variety of 3D surface meshes demonstrate the advantages of our algorithm in terms of improving accuracy and applicability, and preserving saliency geometric features.

The theory and technology of human–machine coordination and natural interaction have a wide range of application prospect in future smart factories. This paper elaborates on the design and implementation of a body-following wheeled robot system based on Kinect, as well as the use of gesture recognition function to enhance the interactive performance. An improved optical flow method is put forward to obtain the direction and speed of the target movement. The smoothing parameters in traditional optical flow are replaced by variables. The new smoothing parameter is related to the local gradient value. Compared with the traditional optical flow method, it can reflect the status of moving objects more clearly, reduce noise and ensure real-time performance, solving the problem of tracking state oscillation caused by the skeleton node drifts when the target is occluded. The experiment on the wheeled robot confirms that the system can accomplish the tracking task in a preferable way.

This paper presents some novel entropy estimators of a continuous random variable using simple random sampling (SRS), ranked set sampling (RSS), and double RSS (DRSS) schemes. The theoretical results of the proposed entropy estimators are derived. The proposed entropy estimators are compared in terms of the bias and the root mean squared errors, theoretically and numerically, with the Vasicek O. [A test for normality based on sample entropy, J. R. Stat. Soc. B 38:54–59, 1976.] entropy estimators using SRS, RSS, and DRSS schemes. It turns out that the new novel entropy estimators are substantially better than the existing Vasicek’s entropy estimators.

Network clustering is the process of partitioning a network into a number of virtual entities mastered by certain nodes, called cluster centers that are responsible for collecting and maintaining topology information and managing the routing processes. In ad hoc networking, clustering has been introduced to deal with the dynamic topology by providing a temporarily stable network core. Clustering process mainly depends on the metric upon which the selection of cluster centers is performed. A wide range of clustering metrics were introduced in the literature based on network issues including mobility and connectivity degree, giving rise to a variety of clustering schemes. Although clustering provides energy consumption reduction, residual energy has not received enough attention and few studies have addressed the clustering on the basis of this feature. This paper discusses the current clustering metrics and proposes an energy-degree evaluation metric with mobility consideration taking into account the nodes residual energy and the network connectivity as two main keys of clustering.

Background and Objective: For dengue outbreak prevention and vectors reduction, fundamental role of control parameters like vaccination against dengue virus in human population and insecticide in mosquito population have been addressed theoretically and numerically. For this purpose, an existing model was modified to optimize dengue fever. Methodology: Using Pontryagin’s maximum principle, the dynamics of infection for the optimal control problem was addressed, further, defined cost functional, established existence of optimal control, stated Hamiltonian for characterization of optimization. Results: Numerical simulations for optimal state variables and control variables were performed. Conclusion: Our findings demonstrate that with low cost of control variables, state variable such as recovered population increases gradually and decrease other state variables for host and vector population.

Combining symplectic algorithm, splitting technique and compact method, a compact splitting symplectic scheme is proposed to solve the fourth-order dispersive Schrödinger equation with cubic-quintic nonlinear term. The scheme has fourth-order accuracy in space and second-order accuracy in time. The discrete charge conservation law and stability of the scheme are analyzed. Numerical examples are given to confirm the theoretical results.

Exchanging simulation models is currently of utmost importance. To improve interoperability between suppliers and original equipment manufacturers (OEMs), the functional mock-up interface (FMI) is exchanged in a standard format called functional mock-up unit (FMU). Since its first release, many simulation tools took the initiative to support FMI. However, since then, there have been many complaints stating that exchanging models via FMI does not work as stable as expected. The reason usually turned out to be the implementation of tool vendors that sometimes fail to comply with the standard fully. This paper introduces a methodology for testing FMI compliance of importing simulation tools using a set of reference FMUs. The standard defines the implementation of FMI functions calling sequence in a state machine. Therefore, conformance testing (also called fault detection) from automata theory is utilized to produce reference FMUs based on the FMI state-machine.