Abstract: In this paper, we investigate how Cognitive Radio as a means of communication can be utilized to serve a smart grid deployment end to end, from a home area network to power generation. We show how Cognitive Radio can be mapped to integrate the possible different communication networks within a smart grid large scale deployment. In addition, various applications in smart grid are defined and discussed showing how Cognitive Radio can be used to fulfill their communication requirements. Moreover, information security issues pertained to the use of Cognitive Radio in a smart grid environment at different levels and layers are discussed and mitigation techniques are suggested. Finally, the well-known Role-Based Access Control (RBAC) is integrated with the Cognitive Radio part of a smart grid communication network to protect against unauthorized access to customer’s data and to the network at large.
Abstract: The efficient use of electrical energy is a topic that has attracted attention for its environmental consequences. On the other hand, induction motors represent the main component in most industries. They consume the highest energy percentages in industrial facilities. This energy consumption depends on the operation conditions of the induction motor imposed by its internal parameters. Since the internal parameters of an induction motor are not directly measurable, an identification process must be conducted to obtain them. In the identification process, the parameter estimation is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables. Under this approach, the complexity of the optimization problem tends to produce multimodal error surfaces for which their cost functions are significantly difficult to minimize. Several algorithms based on evolutionary computation principles have been successfully applied to identify the optimal parameters of induction motors. However, most of them maintain an important limitation: They frequently obtain sub-optimal solutions as a result of an improper equilibrium between exploitation and exploration in their search strategies. This paper presents an algorithm for the optimal parameter identification of induction motors. To determine the parameters, the proposed method uses a recent evolutionary method called the gravitational search algorithm (GSA). Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions. Numerical simulations have been conducted on several models to show the effectiveness of the proposed scheme.
Abstract: Brain–computer interfacing (BCI) is a promising technique for regaining communication and control in severely paralyzed people. Many BCI implementations are based on the recognition of task-specific event-related potentials (ERP) such as P300 responses. However, because of the high signal-to-noise ratio in noninvasive brain recordings, reliable detection of single trial ERPs is challenging. Furthermore, the relevant signal is often heterogeneously distributed over several channels. In this paper, we introduce a new approach for recognizing a sequence of attended events from multi-channel brain recordings. The framework utilizes spatial filtering to reduce both noise and signal space considerably. We introduce different models that can be used to construct the spatial filter and evaluate the approach using magnetoencephalography (MEG) data involving P300 responses, recorded during a BCI experiment. Compared to the accuracy achieved in the BCI experiment performed without spatial filtering, the recognition rate increased significantly to up to 95.3% on average (SD: 5.3%). In combination with the data-driven spatial filter construction we introduce here, our framework represents a powerful method to reliably recognize a sequence of brain potentials from high-density electrophysiological data, which could greatly improve the control of BCIs.
Abstract: We present an accurate method to compute the minimum distance between a point and an elliptical torus, which is called the orthogonal projection problem. The basic idea is to transform a geometric problem into finding the unique real solution of a quartic equation, which is fit for orthogonal projection of a point onto the elliptical torus. Firstly, we discuss the corresponding orthogonal projection of a point onto the elliptical torus for test points at six different spatial positions. Secondly, we discuss the same problem for test points on three special positions, e.g., points on the z-axis, the long axis and the minor axis, respectively.
Abstract: A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.