Abstract-The main notion of this paper is to identify the cognitive load during a mental arithmetic task experiment using fNIRS signals. The first objective is to classify the difficulty level and the state of inactivity during the given task. To identify the classes, the feature vectors have to undergo all the possible steps of a pattern classification problem. In this paper, we have developed a novel Feature Selection technique to reduce the dimension of the feature vectors by omitting the redundant features. For this purpose, an objective function depending upon the class density or likelihood functions is optimized using the well-known Differential Evolution algorithm. General type-2 fuzzy classifier is used for subsequent classification step. The proposed Feature selection technique gives a satisfactory accuracy results over principal component analysis. Also the fuzzy classifier outperforms the other well-known classifier like support vector machine, k-nearest neighborhood. The load of a subject undergoing the experiment is measured at a particular class relying upon the mean type-1 fuzzy value of all feature entities.Keywords-Brain computer interfacing, functional nearinfrared spectroscopy, fuzzy type-2 classifier, principal component analysis, differential evolution algorithm.
This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90º left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent neural network classifier offers better performance using proposed differential evolution-induced feature selection technique, when compared with principal component analysis in such situation and provides the highest classification accuracy of 98.472%. In addition, false classification rate/misclassification rate is also found much higher when using principal component analysis instead of proposed differential evolution-induced feature selection algorithm. The performance of the proposed differential evolution-induced fuzzy recurrent neural network classifier has been compared with a list of standard classifiers including linear support vector machines, k-nearest neighbor and support vector machines with radial basis function kernel, where fuzzyrecurrent neural network classifier outperforms its competitors with an average classification accuracy of 95.472% and 95.647 for steering left and acceleration motor intensions respectively.
This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.
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