Today, most manufacturing control systems are complex and expensive, so they are limited to employ a small number of function evaluations for optimal design. Yet, looking for optimization methods with the less-computational cost is an open issue in engineering control systems. This paper aims to propose an effective adaptive optimization approach by integrating Kriging surrogate and Particle Swarm Optimization (PSO). In this method, a novel iterative adaptive approach is utilized using two sets of training samples including initial training and adaptive sample points. The initial training points are designed by space-filling design, while the adaptive points are generated using a new jackknife resampling approach. The proposed approach can effectively convergence towards the global optimal point using a small number of function evaluations. The efficiency and applicability of the proposed algorithm are evaluated using the optimal design of the fractional-order PID (FOPID) controller for some benchmark transfer functions. Then, the introduced approach is applied for tuning the parameters and the sensitivity analysis of the FOPID controller for a dynamic production-inventory control system. The results are in good agreement with the results reported in the literature, while the proposed approach is executed with a lower computational burden.
Speech Emotion Recognition (SER) plays a vital role in human-computer interaction as an important branch of affective computing. Due to inconsistencies in the data and challenging signal extraction, in this paper, we propose a novel emotion recognition method based on the combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) with Word to Vector (Word2Vec) models. To begin, the inputs have been pre-processed, which comprise audio and text data. Second, the features were extracted using the Word2vec behind spectral and prosodic approaches. Finally, the features are selected using the Sequential Backward Floating Selection (SBFS) approach. In the end, the ANFIS-PSO model has been used to recognize speech emotion. A performance evaluation of the proposed algorithm is carried out on Sharif Emotional Speech Database (ShEMO). The experimental results show that the proposed algorithm has advantages in accuracy, reaching 0.873 and 0.752 in males and females, respectively, in comparison with the CNNs and SVM, MLP, RF models.
Imbalance in critical rotary equipment is one of the most important factors, which should be controlled to prevent great damages. In this case study we are discussing about a 24 MW steam turbine, which drives a propane compressor. The radial vibration on the DE side of the turbine was growing gradually to a high level close to the alarm's value. Using FFTs, time signals, orbit diagrams, and phase measurement led us to believe that the rotor became imbalanced. After tripping and disassembling the turbine, we found out, some blades of the impulse stage of HP section got broken. Changing the rotor with the spare one, and repair the damaged rotor, worked out. It was concluded that using the vibration analysis technique is an effective method to find critical rotating equipment’s faults at the earliest levels. And performing the essential correcting tasks to prevent secondary damages and specially decrease of production.
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