Abstract
The main objective of modeling a switched reluctance machine is to derive a mathematical function to relate the outputs to the inputs. Due to the nonlinear relationship between the variables of torque, flux linkage, current and angular position of the rotor, Switched Reluctance Machine (SRM) modeling is a very challenging task and an open problem. Modeling is usually done in two situations, modeling a single machine, or modeling a set of machines. Each one must fulfill different requirements. This work presents a survey of different SRM modeling approaches, evaluating its advantages and limitations when modeling a single machine or a set of machines.

Abstract
The use of robotic solutions in tasks such as the inspection and monitorization of offshore wind farms aims to, not only mitigate the involved risks, but also to reduce the costs of operating and maintaining these structures. Performing a complete inspection of the platforms in useful time is crucial. Therefore, multiple agents can prove to be a cost-effective solution. This work proposes a trajectory planning algorithm, based on the Ant Colony metaheuristic, capable of optimizing the number of Autonomous Surface Vehicles (ASVs) to be used, and their corresponding route. Experiments conducted on a simulated environment, representative of the real scenario, proves this approach to be successful in planning a trajectory that is able to select the appropriate number of agents and the trajectory of each agent that avoids collisions and at the same time guarantees the full observation of the offshore structures.

Abstract
The ability of a consumer friendly demand response based voltage control (DR-VC) program to improve the voltage regulation in a low voltage distribution network (LVDN) with high penetration of DG is investigated. The use of active and reactive power management to regulate the nodal voltage in a distribution network with simple incremental reduction algorithm, in conjunction with DR, is proposed as a solution for over voltage and undervoltage issues in the LVDN. The algorithm micromanages the load and generation in the network enabling the operator to utilize grid resources economically and efficiently while maintaining fairness between consumers with minimum inconvenience. The algorithm is tested on a representative. 74-load radial urban distribution network (Dublin, Ireland) using consumer load and DG generation profiles. The system is modelled and analysed using COM interface between OpenDSS and MATLAB. The DR is modelled through a mixed integer linear programming (MILP), implemented in CVX, such that consumer inconvenience is prioritized. The DR-VC algorithm is capable of regulating load and generation within normal operation limits during undervoltage and overvoltage scenarios.

Abstract
Reverse engineering may be helpful for extracting information from existing apps to understand them better and ease their maintenance. Reverse engineering may be performed by a static analysis of the apps’ code but, when the code is not available, a dynamic approach may be useful. This paper presents a tool that allows extracting dynamically, in a complete black-box approach, the explored activities of Android applications. It is an extension of iMPAcT testing tool that combines reverse engineering, dynamic exploration, and testing. The extracted information is later used to construct an HFSM (Hierarchical Finite State Machine) with three distinct levels of abstraction. The top-level shows the interactions needed to traverse the activities of the mobile application. The middle level shows the screens traversed while in a specific activity. The bottom level shows all screens traversed during exploration. This information helps to understand better the application which facilitates its maintenance and errors fixing. This paper provides a complete description of the tool, its architecture and the results of some case studies conducted on mobile apps publicly available on the Google Store.

Abstract

Abstract
Natural teeth eventually fall out as one becomes older, making it more difficult chewing, speaking and get a reference plane for the body postural equilibrium. To minimize the problem, the missing teeth are eventually replaced by implants that restore the referred functions but miss the sensing of the applied force. As a consequence, the masticatory forces become erratic as the brain receives no feedback (or inaccurate) sensing information. The present work aims at developing a preliminary prototype of a smart dental implant meant to restore the proprioceptive control of the masticatory and chewing muscle activity. After the description of the physiological and biomechanical aspects related to tooth loss, details are provided on the force sensing and electrical stimulation provided by the active implant being proposed. Simulation results obtained with the development tool of the GreenPAK programmable chip being used are included.