Research of Methods of Optimal Design of Special Electric Drives

Problems of optimization of special electrical and electromechanical systems in modeling, creation and design are solved mainly by methods of mathematical programming. The task of mathematical programming is to find extremes of the function of many variables in the presence of restrictions on variables, which creates fundamental difficulties. To solve such problems, the number of methods for solving the general problem of mathematical programming is currently expanding significantly. In this regard, the trend in the development of mathematical programming is following the path of highlighting and studying various subclasses of problems. The use of certain specific features of the tasks of the selected subclass creates opportunities for their more effective investigation and solution. Examples of such subclasses give convex, quadratic, linear programming problems, transport-type problems, and others. Geometric programming is also a section of mathematical programming that research a certain class of optimization problems. However, when using geometric programming, you have to apply linear, nonlinear programming, the concept of convex programming. Using optimization methods will allow you to correctly investigate, design and create special electric drives.

Optimal Resource Allocation For Video Streaming Over Cognitive Radio Network Via Geometric Programming

Cognitive Radio (CR) is a new paradigm in wireless communications to enhance utilization of limited spectrum resources. In the cognitive radio networks, each secondary user can use wireless channels for data transmission to improve the spectrum utilization. This thesis focus on the resource allocation problem for video streaming over cognitive radio networks, where secondary users and primary users transmit data simultaneously in a common frequency band. Respectively, we investigate CR in both single channel and multiple channels scenarios for single-layered and multi-layered streaming video, which is encoded into multiple layers delivered over a separate channel. Moreover, the source rate, the transmission rate, and the transmission power at each video session in each channel are jointly optimized to provide Quality of Service (QoS) guarantee to all video sessions in the secondary network. The optimization problem is formulated into a Geometric Programming (GP) problem, which can be solved efficiently. In the simulations, we demonstrate that the proposed scheme can achieve a lower Packet Loss Rate (PLR) and queuing delay, thus leading to a higher video quality for the video streaming sessions, compared to the uniform scheme.

Optimal Resource Allocation For Video Streaming Over Cognitive Radio Network Via Geometric Programming

Cognitive Radio (CR) is a new paradigm in wireless communications to enhance utilization of limited spectrum resources. In the cognitive radio networks, each secondary user can use wireless channels for data transmission to improve the spectrum utilization. This thesis focus on the resource allocation problem for video streaming over cognitive radio networks, where secondary users and primary users transmit data simultaneously in a common frequency band. Respectively, we investigate CR in both single channel and multiple channels scenarios for single-layered and multi-layered streaming video, which is encoded into multiple layers delivered over a separate channel. Moreover, the source rate, the transmission rate, and the transmission power at each video session in each channel are jointly optimized to provide Quality of Service (QoS) guarantee to all video sessions in the secondary network. The optimization problem is formulated into a Geometric Programming (GP) problem, which can be solved efficiently. In the simulations, we demonstrate that the proposed scheme can achieve a lower Packet Loss Rate (PLR) and queuing delay, thus leading to a higher video quality for the video streaming sessions, compared to the uniform scheme.

A FUZZY PROGRAMMING APPROACH TO INVENTORY CONTROL PROBLEM

Purpose of study: Main aim of this study is to deals with the problem of inventories. Their holding cost, set-up cost, and many more related to that. All the problems are flexible and having fuzzy nature. Methodology: The model takes the form of a Geometric Programming problem. Hence geometric programming algorithm is used here. Main Finding: The developed models may be used for a single item with a single constraint of limitation on storage area and multi-item inventory problems. Application of this study: This study is useful in the area of inventories. There holding cost and set-up cost etc. The originality of this study: This study may help the stockholders for storing goods and minimizing the cost of holding.