Energy and spectral efficiency are the main challenges in 5th generation of mobile cellular networks. In this paper, we propose an optimization algorithm to optimize the energy efficiency by maximizing the spectral efficiency. Our simulation results show a significant increase in terms of spectral efficiency as well as energy efficiency whenever the mobile user is connected to a low power indoor base station. By applying the proposed algorithm, we show the network performance improvements up to 9 bit/s/Hz in spectral efficiency and 20 Gbit/Joule increase in energy efficiency for the mobile user served by the indoor base station rather than by the outdoor base station.

Civil engineering is one of the many fields of occurrences of uncertain parameters. The present paper in an attempt to present and describe the most common methods used for inclusions of uncertain parameters . These methods can be applied in the area of civil engineering as well as for a larger domain. Definitions and short explanations of methods based on probability, interval analysis, fuzzy sets, and convex sets are presented. Selected advantages, disadvantages, and the most common fields of implementation are indicated.

An example of a cantilever beam presented in this paper shows the main differences between the methods. Results of the performed analysis indicate that the use of convex sets allows us to obtain an accuracy of results similar to stochastic models. At the same time, the computational speed characteristic for interval methods is maintained.

In this paper the controllability properties of the convex linear combination of fractional, linear, discrete-time systems are characterized and investigated. The notions of linear convex combination and controllability in the context of fractional-order systems are recalled. Then, the controllability property of such a linear combination of discrete-time, linear fractional systems is proven. Further, the reduction of an infinite problem of transition matrix derivation is reduced to a finite one, which greatly simplifies the numerical burden of the controllability issue. Examples of controllable and uncontrollable, single-input, linear systems are presented. The possibility of extension of the considerations to multi-input systems is shown.

The purpose of the following paper is to present the experimental field investigations in jointless railway track subjected to the author’s generated imperfections on its static work. The main concept for the executed investigations is to induce an intentional imperfection (both a concave and convex irregularity) in an actual railway track, propose a way of appropriate measurement (using the PONTOS system), and utilize author’s field investigations results to calibrate necessary parameters for theoretical calculations. An experimental formula describing the value of the force transferred from the rail to the railway sleeper on the grounds of the survey site caused by a locomotive is provided. Furthermore, the deflection of the chosen railway rail and sleeper due to the generated imperfection is subjected to analysis. Finally the objective of the present consideration is to resolve the calculations into the beam element such that the results can be used in computational railway practice. The scheme of the so-called a “hanging sleeper” is particularly unfavourable, a gap arises between the sleeper and the foundation, for which the significant changes appear, especially in the rail deflections and stresses. A work scheme of the railway track elements is described on the generated short concave and convex irregularity.

The asymptotic stability of the convex linear combination of continuous-time and discretetime linear systems is considered. Using the Gershgorin theorem it is shown that the convex linear combination of the linear asymptotically stable continuous-time and discretetime linear systems is also asymptotically stable. It is shown that the above thesis is also valid (even simpler) for positive linear systems.

To comprehensively investigate the diversity of a chamfer technology and a convex roll technology under the same soft reduction process (i.e., section size, reduction amount, casting speed and solid fraction), a three-dimensional mechanical model was developed to investigate the effect of the chamfer profile and roll surface profile on the deformation behavior, cracking risk, stress concentration and reduction force of as-cast bloom during the soft reduction process. It was found that a chamfer bloom and a convex roll can both avoid the thicker corner of the as-cast bloom solidified shell, and significantly reduce reduction force of the withdrawal and straightening units. The convex profile of roll limits lateral spread along bloom width direction, therefore it forms a greater deformation to the mushy zone of as-cast bloom along the casting direction, the tensile strain in the brittleness temperature range (BTR) can obviously increase to form internal cracks. The chamfer bloom is much more effective in compensating the solidification shrinkage of mushy zone. In addition, chamfer bloom has a significant decrease of tensile strain in the brittleness temperature range (BTR) areas, which is expected to greatly reduce the risk of internal cracks.

The model predictive control (MPC) technique has been widely applied in a large number of industrial plants. Optimal input design should guarantee acceptable model parameter estimates while still providing for low experimental effort. The goal of this work is to investigate an application-oriented identification experiment that satisfies the performance objectives of the implementation of the model. A- and D-optimal input signal design methods for a non-linear liquid two-tank model are presented in this paper. The excitation signal is obtained using a finite impulse response filter (FIR) with respect to the accepted application degradation and the input power constraint. The MPC controller is then used to control the liquid levels of the double tank system subject to the reference trajectory. The MPC scheme is built based on the linearized and discretized model of the system to predict the system’s succeeding outputs with reference to the future input signal. The novelty of this model-based method consists in including the experiment cost in input design through the objective function. The proposed framework is illustrated by means of numerical examples, and simulation results are discussed.

In sequencing situations, it may affect parameters used to determine an optimal order in the queue, and consequently the decision of whether (or not) to rearrange the queue by sharing the realized cost savings. In this paper, we extend one machine sequencing situations and their related cooperative games under fuzzy uncertainty. Here, the agents costs per unit of time and processing time in the system are fuzzy intervals. In the sequel, we define sequencing fuzzy interval games and show that these games are convex. Further, fuzzy equal gain splitting rule is given. Finally, a numerical example is illustrated priority based scheduling algorithm.

This paper is devoted to study robust efficiency in terms of variational inequality for a class of multi-dimensional multi-objective first-order PDE-constrained fractional control optimization problems with data uncertainty (MMFP). We derive a robust controlled vector variational inequality (VI) together with its weak form and discuss equivalence between the solutions of (VI) and (MMFP) via imposing the suitable assumptions. Later on, we study a sufficient condition for the robust weak efficient solution of (MMFP) to be its robust efficient solution under the strict convexity assumption and give some applications to illustrate the established results.

The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.