Intelligent buildings are responsible for ensuring the indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events due to accidental or malicious actions. An emerging environmental control application is monitoring the intelligent buildings against the presence of such events, by incorporating various sensing technologies and distributed detection and isolation algorithms. The needed simplicity, the improved scalability and fault tolerance are some of the main reasons for choosing distributed approaches over centralized ones. Hence, the effective partitioning of buildings into smaller sections for contaminant detection and isolation approaches is of great importance. In this paper, we present a heuristic algorithm for partitioning the building into smaller sections. The proposed algorithm is based on matrix clustering techniques and groups the building zones in order to form the different sections while ensuring (i) maximum decoupling between the various sections, (ii) strong connectivity between the zones of a section and (iii) fairness with respect to the number of allocated zones. The partitioning is achieved in near real time, providing the ability of repartitioning and adapting to the dynamic nature of the airflows. The main contribution of this work is the automatic partitioning of the building into sections, which enables the distributed simulation, modeling, analysis and management of the intelligent building while ensuring the effective detection and isolation of contaminants in the building interior.
This paper proposes an approach to the optimal fuzzy modeling of a nonlinear servo system application represented by an electromagnetic actuated clutch system. The nonlinear model of the process is first linearized around several operating points of the input-output static map of the process. Discrete-time Takagi-Sugeno (T-S) fuzzy models of the process are derived on the basis of the modal equivalence principle, and the rule consequents of these T-S fuzzy models contain the linearized state-space models of the process. Optimization problems are defined which aim the minimization of objective functions (OFs) defined as the mean of squared modeling errors (the difference between the process output and the fuzzy model output). The variables of the OFs are represented by a part of the parameters of the input membership functions. A Particle Swarm Optimization (PSO) algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of simulation results is included to validate the PSO algorithm-based modeling approach and the optimal T-S fuzzy models for the electromagnetic actuated clutch system.
In this paper, we discuss two things. In the first part of the paper, we discuss the means of processing the speech signal using the Mel Frequency Cepstral Coefficients and the basics of voice characteristics. The problem of voice recognition was already implemented at the hardware level, but in this we propose a solution to the problem on the software end with a higher level of accuracy. Then in the second part, we will discuss the application developed on Android which is able to perform both single speaker and multi-speaker recognition. The multi-speaker feature enables the system to identify more than one person speaking by splitting the input into smaller blocks and treating each block as a single speaker identification problem. In the single speaker recognition case, the algorithm is able to correctly identify the individual 90.3% of the time. In the multi-speaker recognition case, up to 3 individuals are correctly identified roughly 86% of the time. The average identification time is 220ms across a database of 415 samples.
Electric vehicles (EVs) and their application raise new questions and pose many new demands. Accordingly, the increasing amount of charging processes by EVs is a new and growing impact on the grid load. In order to deal with this impact and the resulting demands, load shifting allows the grid load to be adjusted. Taking this into consideration this paper initially analyzes, based on field test measurements, the EVs' occupation and extracts the times they are parked at a charging point. Combining this with the EVs' charging power, options for load shifting are discussed and emulated based on real data. The database includes charging processes of 500 people and a 785.000 km mileage of pure battery electric vehicles with a DC fast-charging option and extended range electric vehicles. The results indicate that EVs can partly be used as grid controlled load without changing the utilization.
This paper introduces a new discrete-time fractional-order power system stabilizers (DTFoPSS) that can be used to stabilize synchronous machines connected to infinite bus subject to both local and inter-area mode of oscillations. The dynamics of the proposed stabilizers are of second-order IIR-type controllers that depend only on the fractional order 0 <; α ≤ 1. The DTFoPSS will be used in both single and multi-machine infinite bus systems triggered by severe disturbances. The flat phase characteristics make the DTFoPSS more appealing to adopt than their integer-order counterparts. They outperformed the continuous integer-order power system stabilizers (IoPSS) in single-machine infinite-bus systems, while exhibit identical performance to that of the IoPSS in multi-machine ones. The main points of this work are demonstrated via numerical simulations.
