Effects of modulation techniques on the input current interharmonics of Adjustable Speed Drives

Adjustable speed drives (ASDs) based on a three-phase front-end diode rectifier connected to a rearend inverter may generate interharmonic distortion in the grid. The interharmonic components can create power quality problems in the distribution networks such as interference with the ripple control signals, and consequently they can hamper the normal operation of the grid. This paper presents the effect of the symmetrical regularly sampled space vector modulation and discontinuous pulse width modulation-30° lag (DPWM2) techniques, as the most popular modulation methods in the ASD applications, on the drive's input current interharmonic magnitudes. Further investigations are also devoted to the cases where the random modulation technique is applied to the selected modulation strategies. The comparative results show how different modulation techniques can influence the ASD's input current interharmonics and consequently may not be a suitable choice of modulation from an interharmonics perspective. Finally, the theoretical analysis and simulation studies are validated with obtained experimental results on a 7.5-kW motor drive system

Study Short Term and Long Term Impact of Effective Real Exchange Rate on Oil Price Growth in Iran

Exchange rate is considered as a criterion of money equivalent value for a country in compare to other countries and it reflects economic condition of that country in compare to economic status of other ones. Variation of exchange rate can be extremely effective on oil price in oil-rich countries specifically Iran. Since effects of macroeconomic variables are different in short term and long term time, in this study, short term and long term impact of effective real exchange rate on oil price growth in Iran is studied. In order to estimate long term relationship between effective real exchange rate and oil price, monthly and compiled data of OPEC were utilized from 2001-2015. Firstly, variables' durability was studied and then lack of durability Johansen differentiation and accumulation. Finally, results of the study indicated that effective real exchange rate has effect on oil price in long term while, based on BVAR, this effectiveness is not true in short term. Keywords: Exchange rate, oil price, BVAR JEL Classification: Q

Oil Price and Inflation in Iran: Non-Linear ARDL Approach

In this paper we study the relationship between oil price (or revenue) changes and inflation rate in Iran seasonally in 2003-2015. Method of the study was able to identify asymmetry of oil price and inflation which is recognized as nonlinear autoregressive distributed lag model. Estimated model clarified nonlinear effect of oil price on inflation. Clearly, we found out that there was significant relationship between the reduction in oil price and inflation growth while there was no significant relationship between increase in oil price growth and inflation rate. Keywords: inflation behavior, oil price, asymmetry JEL Classifications: E31, Q

Design and fabrication of polycaprolactone/gelatin composite scaffolds for diaphragmatic muscle reconstruction

Diaphragmatic wall defects caused by congenital disorders or disease remain a major challenge for physicians worldwide. Polymeric patches have been extensively explored within research laboratories and the clinic for soft tissue and diaphragm reconstruction. However, patch usage may be associated with allergic reaction, infection, granulation, and recurrence of the hernia. In this study, we designed and fabricated a porous scaffold using a combination of 3D printing and freeze-drying techniques. A 3D printed polycaprolactone (PCL) mesh was used to reinforcegelatin scaffolds, representing an advantage over previously reported examples since it provides mechanical strength and flexibility. In vitro studies showed that adherent cells were anchorage-dependent and grew as a monolayer attached to the scaffolds. Microscopic observations indicated better cell attachments for the scaffolds with higher gelatin content as compared with the PCL control samples. Tensile testing demonstrated the mechanical strength of samples was significantly greater than adult diaphragm tissue. The biocompatibility of the specimens was investigated in vivo using a subcutaneous implantation method in BALB/c adult mice for 20 days, with the results indicating superior cellular behavior and attachment on scaffolds containing gelatin in comparison to pure PCL scaffolds, suggesting that the porous PCL/gelatin scaffolds have potential as biodegradable and flexible constructs for diaphragm reconstruction

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

A self-tuning feedforward active noise control system

This paper proposes a self-tuning feedforward active noise control (ANC) system with online secondary path modeling. The step-size parameters of the controller and modeling filters have crucial rule on the system performance. In literature, these parameters are adjusted by trial-and-error. In other words, they are manually initialized before system starting, which require performing extensive experiments to ensure the convergence of the system. Hence there is no guarantee that the system could perform well under different situations. In the proposed method, the appropriate values for the step-sizes are obtained automatically. Computer simulation results indicate the effectiveness of the proposed method

Benefiting white noise in developing feedforward active noise control systems

In many applications of active noise control (ANC), an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to adjust the secondary path estimation. Comparative simulation results shown in this paper indicate effectiveness of the proposed method

Designing a new robust on-line secondary path modeling technique for feedforward active noise control systems

Several approaches have been introduced in the literature for active noise control (ANC) systems. Since the filtered-x least-mean-square (FxLMS) algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of the FxLMS algorithm, as a first novelty. In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system. As a second novelty, this paper proposes a new approach for on-line secondary path modeling on the basis of a new variable-step-size (VSS) LMS algorithm in feed forward ANC systems. The proposed algorithm is designed so that the noise injection is stopped at the optimum point when the modeling accuracy is sufficient. In this approach, a sudden change in the secondary path during operation makes the algorithm reactivate injection of the white noise to re-adjust the secondary path estimate. Comparative simulation results shown in this paper indicate the effectiveness of the proposed approach in reducing both narrow-band and broad-band noise. In addition, the proposed ANC system is robust against sudden changes of the secondary path model

An optimized online secondary path modeling method for single-channel feedback ANC systems

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method