Execution Time Criteria Research Articles

Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

The effective dwell scheduling algorithm must be studied to make full use of the radar system's limited resources. In this study, a real-time dwell scheduling in phased array radar is investigated, where the optimisation problem model that takes the importance, urgency and desired execution time criteria into consideration is built. A real-time dwell scheduling algorithm for phased array radar based on a backtracking strategy is proposed to solve this problem. Based on the scheduled task pointed by the time pointer, when it is not interleaved with the previously scheduled tasks, two previously interleaved blocks are backtracked, and the actual execution time of tasks in these two blocks are adjusted. In order to guarantee real-time scheduling, the concept of the virtual dwell task is introduced to reduce the dimension of the original execution time optimisation problem first, then it is converted into two convex optimisation problems and their analytical solutions are deduced. Simulation results demonstrate that the proposed algorithm can realise real-time dwell scheduling, and it has the lowest Task Drop Ratio, the highest Hit Value Ratio, Time Utilisation Ratio and low Average Time Shifting Ration compared with conventional and up-to-date algorithms.

An online context-aware mechanism for computation offloading in ubiquitous and mobile cloud environments

In ubiquitous environments today, there are numerous sensors that generate a large amount of data. The results of processing this massive data can be applied through peripheral actuators in the surrounding environment of users. On the other hand, users are more interested in running compute-intensive applications on their mobile devices in the minimum possible time and regardless of resource limitations. Due to resource limitations in ubiquitous and mobile environments, processing in a completely local manner is either impossible or very time-consuming. As a result, researchers propose running computational ubiquitous and mobile applications on clouds within a minimum possible time and without any resource limitations. This can be achieved by utilizing the elastic features of computational clouds and the offloading technique. The present research proposes a mechanism for computation offloading decision making, whose main purpose is to simultaneously reduce energy consumption and execution time in ubiquitous and mobile devices. Based on energy saving and execution time criteria and the consideration of some context-aware parameters, the proposed online mechanism performs the decision-making process for task offloading. Through simulation and the usage of real workload data, the current work evaluates the efficiency of the proposed offloading decision mechanism and the results show it to be satisfactory.

Programming by Demonstration Using Two-Step Optimization for Industrial Robot.

The paper presents programming by demonstration approach for industrial robots that uses two steps trajectory optimization. On the first stage, optimization is done by statistical methods, on the second stage, the robot end-effector trajectory is obtained based on minimization of energy consumption and task execution time criteria. We verified our approach using 7 degree-of-freedom redundant robot KUKA iiwa and Microsoft Kinect 2.0 motion capture system.

Probabilistic power flow of correlated hybrid wind‐photovoltaic power systems

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies such as wind and solar, is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system context, consequently, necessitates probabilistic analysis of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognised as an appropriate probabilistic method. This study proposes a new methodology for probabilistic power flow studies for such a problem by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables, but the proposed method has been equipped with this ability. To justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the IEEE118-bus test systems are examined using the proposed method, then the obtained results are compared against the Monte Carlo simulation results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

Real time probabilistic power system state estimation

Smartening of contemporaneous power delivery systems in conjunction with the increased penetration of renewable energies (REs), change the way to energize consumers who are willing to maximize their utility from energy consumption. However, there is a high degree of uncertainty in the electricity markets of such systems. Moreover, the unprecedented ascending penetration of distributed energy resources (DERs) mainly harvesting REs is a direct consequence of environmentally friendly concerns. This type of energy resources brings about more uncertainties into power system operation resulting in, necessitates probabilistic analysis of the system performance. In the smarter power markets, encountered the restructuring and deregulation, the online studies of system performance is of huge interest. This paper proposes a new methodology for real time state estimation, e.g. energy pricing by probabilistic optimal power flow (P-OPF) studies using the concept of hybrid artificial neural networks (ANN) and differential evolutionary (DE) method. In order to examine the effectiveness and applicability of the proposed method, two case studies are conducted and the obtained results are compared against those of Monte Carlo simulation (MCS) technique. Comparison of the results reveals the impressiveness of the method regards to both accuracy and execution time criteria.

Probabilistic Optimal Power Flow in Correlated Hybrid Wind–Photovoltaic Power Systems

As a matter of course, the unprecedented ascending penetration of distributed energy resources (DERs), mainly harvesting renewable energies (REs), is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system, consequently, necessitates probabilistic analyses of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognized as an appropriate probabilistic method in small scale or even medium scale problems. This paper develops a new methodology for probabilistic optimal power flow (P-OPF) studies for such problems by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables but the proposed method has been equipped with this ability. In order to justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the Mathpower 30-bus test systems are examined using the proposed method, then, the obtained results are compared against the Monte Carlo simulation (MCS) results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

Probabilistic Load Flow in Correlated Uncertain Environment Using Unscented Transformation

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies, is a direct consequence of environmental concerns. This type of energy resource brings about more uncertainties in power system operation and planning; consequently, it necessitates probabilistic analyses of the system performance. This paper develops a new approach for probabilistic load flow (PLF) evaluation using the unscented transformation (UT) method. The UT method is recognized as a powerful approach in assessing stochastic problems with/without correlated uncertain variables. The capability of the UT method in modeling correlated uncertain variables is very appealing in the power system context, in which noticeable inherent correlation exists. The salient features of the UT method in probabilistic applications have been well proven in other engineering aspects. Following adaptation of the UT method for the PLF problem, three dimensionally different case studies are examined in order to inspect the performance of the proposed methodology. The obtained results are then compared with those of the Monte Carlo simulation as well as two-point estimation method with regards to both accuracy and execution time criteria.