Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex Optimization problem that guarantees the global optimal solution. The convex Optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

In this paper, firstly, we obtain some new results about bornological convergence in locally convex cones (which was studied in [1]) and then we introduce the concept of bornological completion for locally convex cones. Also, we prove that the completion of a bornological locally convex cone is bornological. We illustrate the main result by an example.

We characterize bounded sets and bounded operators in locally convex cones. Also, we study some relations between barreledness and boundedness in locally convex cones.

We consider the locally convex product cone topologies and prove that the product topologyof weakly cone-complete locally convex cones is weakly cone-complete. In particular, we deduce that a product cone topology is barreled whenever its components are weaklycone-complete and carry the countable neighborhood bases.

In this paper, we define the almost uniform convergence and the almost everywhere convergence for cone-valued functions with respect to an operator valued measure. We prove the Egoroff theorem forP-valued functions and operator valued measure q: ®L (P, Q), where R is a s-ring of subsets of X¹q, (P, V) is a quasi-full locally convex cone and (Q, W) is a locally convex complete lattice cone.

Design of sparse array antenna that can create the desired radiation patterns with minimum number of elements, is a favorite research area. The synthesis sparse array problem can be modeled with appropriate constraints on the number of solve space members, namely l_0-norm of the weight elements. But it is a non-convex problem that requires to solving a NP-hard problem. An interesting ideas is mentioned to relax problem to convex problem. The proposed solution is based l_1-norm,The algorithm used here, first determines the optimal radiation pattern with convex Optimization. then by using iterative weighting l_1-norm, sparse array is obtained by removing those elements that weights of them are almost zero and optimally determines the position of the element. As a result, by solving the non-convexity property of the problem, the optimal solution is provided with a reasonable computational time. The purpose of the Optimization method is to minimize the number of elements, observe the constraints related to the requirements of the radiation pattern and reduce the calculation time. This research, in its case study, was able to sparse the 11×11 array (121 elements) to 42 elements (increase PSL) and 37 elements (increase mainlobe beamwidth) by adjusting the relevant parameters such as DRR, γ and ε.