Thermal Comfort Assessment of Buildings (SpringerBriefs in Applied Sciences and Technology)

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Thermal Comfort Assessment of Buildings - Salvatore Carlucci - Google Книги

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Philosophy of the Economy: Crespo — not in English Common Knowledge. Popper and His Popular Critics: Lozny — not in English Common Knowledge. Quantum Black Holes by Xavier Calmet. Traditional adaptive control technologies in machining process optimization are limited in applications because they depend much on sensors, controllers and other hardware. An off-line optimization method for end milling process with constant cutting power is presented.

Building energy performance assessment in Southern Europe

On taking advantage of virtual machining which simulates milling process, acquires cutting parameters and predicts cutting forces, method taking constant cutting power as an objective is discussed to optimize feed rates and cutting speeds. Based on optimal result, the feed rates and spindle revolutions in NC program are re-scheduled. Controlled milling experiments show that machining time is reduced and machining stability is improved by using the optimized NC program. In the context of real-world damage detection problems, the lack of a clear objective function advises to perform simultaneous optimizations of several objectives with the purpose of improving the performance of the procedure.

Evolutionary algorithms have been considered to be particularly appropriate to these kinds of problems. However, evolutionary techniques require a relatively long time to obtain a Pareto front of high quality. Particle swarm optimization PSO is one of the newest techniques within the family of optimization algorithms. The PSO algorithm relies only on two simple PSO self-updating equations whose purpose is to try to emulate the best global individual found, as well as the best solutions found by each individual particle.

Since an individual obtains useful information only from the local and global optimal individuals, it converges to the best solution quickly. PSO has become very popular because of its simplicity and convergence speed. However, there are many associated problems that require further study for extending PSO in solving multi-objective problems. The goal of this paper is to present the first application of PSO to multiobjective damage identification problems and investigate the applicability of several variations of the basic PSO technique.

The potential of combining evolutionary computation and PSO concepts for damage identification problems is explored in this work by using a multiobjective evolutionary particle swarm optimization algorithm. Jun Zhang, Kan Yu Zhang. Good dynamic performance of a system have great significance in the traditional sense, furthermore,it is more important at the point of energy saving.

Particle swarm optimization PSO is a novel evolutionary algorithm which has a better convergence rate and computation precision compared with other evolutionary algorithms. In this paper an optimal design of PID controller based on particle swarm optimization approach for temperature control in HVAC is presented. The results show the adjustment of PID parameters converting into the optimal point and the good control response based on the optimal values by the PSO technique, and thus it achieves the purpose of energy savings.

In this paper, a hybrid optimization method, GA-SQP, is presented in which the genetic algorithm GA is a stochastic method is combined with the sequential quadratic programming SQP method, which is a deterministic method. The power system stabilizers parameters tuning problem is converted to an optimization problem which is solved by hybrid GA-SQP optimization algorithm.