This paper aims to design a hybrid PD-Fuzzy position controller for high performance linear switched reluctance motor (LSRM). The mathematical model of LSRM is developed and simulated by Matlab/Simulink package. The essential feature of the proposed hybrid PD-Fuzzy controller is to use a PD controller only for coarse position error, then for fine position error LSRM position control is done using a fuzzy logic controller only. New force distribution function (FDF) is proposed to overcome the problems of electromagnetic force ripples due to LSRM nonlinear magnetic characteristics. Through a hysteresis controller, asymmetric three phase power converters can be controlled to drive the LSRM. Simulation results prove the effectiveness of the proposed hybrid PD-Fuzzy controller in position setpoint precise tracking under different loading disturbances and different position setpoint.

This paper presents the design of an adaptive hybrid controller for linear switched reluctance motor (LSRM) to be used in pick and place industrial applications. The hybrid controller consists of two main controllers, the proportional-derivative controller (PDC) and the fuzzy logic controller (FLC). In this paper, the proposed Adaptive hybrid PD-fuzzy logic controller (AHPDFLC) uses, at the beginning, the adaptive PDC (APDC) for coarse position error while for fine position error the position control process is achieved using conventional FLC. This hybrid controller exploits the advantages of both controllers, APDC which can vary its gains online to avoid the overshoot problems and FLC which can decrease the steady-state error to roughly zero value to get an acceptable position tracking response of the underlying LSRM. The adaptivity of the hybrid controller refers to the variation of PDC's proportional and derivative …

Roads extraction from VHR satellite images are very paramount for GIS and map updating. Due to the high resolution of satellite images, there are many obstacles broken roads such as shadow, and vehicles. The present work aims to find the most suitable road extraction approach that can be applied in the Egyptian environment. In this study, two satellite images from WorldView-2 and WorldView-3 were used. Classification of image by pixel-based and object-based was carried out to find the appropriate classification method for road extraction. Then, road class refinement by morphology and angular texture signature are performed to decrease the misclassifications between roads and other spectrally similar objects. After that, an iterative and localized Hough transform method was compared with the thinning algorithm method to find the proper method that can extract road centerline segments from the refined images. The performance of the extracted roads was estimated by using the common metrics; completeness, correctness, and quality. The results of this work demonstrate that the random tree in object-based classification achieves the highest overall accuracy than other classification methods. Also, thinning algorithm has more advantages than Hough transform.

Solar energy is considered alternative crucial to fulfilling the increasing of energy requirements. Passive cooling systems are considered alternatives to mechanical ventilation systems. In this work, a highlight for a solar chimney with water heater and Phase Change Material is applied as a passive solar technique for cooling building integrated with short wind tower for low energy building in the hot arid climate. This paper aims to investigate the performance of new passive air condition system with the solar chimney and solar water heater as a full system for cooling air and heating water to be applied during day and night time based on the improvement of the solar chimney in Assiut climate, Egypt. Measurements, for air temperatures and surface temperature of the aluminium and air velocity inside the chimney, water temperature and room temperature were conducted with different solar radiations to evaluate the …

Master curves (MC) of mixtures and asphalt binders are typically implemented in the linear viscoelastic range for assessing their rheological behavior. Although there are several theoretical models to represent the MC of mixtures and asphalt binders, their performance is variable. The use of MC to model the binder’s phase angle has received less attention compared to the MC of the complex modulus. Thus, this paper investigates the possibility of using several predictive equations to represent the phase angle (δ) of neat asphalt (virgin asphalt and sulfur-extended asphalt, SEA) and modified asphalt [recycled polyethylene-modified asphalt and recycled polyethylene-modified SEA (PMSEA)]. The investigated models include the standard sigmoidal (SS) model, generalized logistic sigmoidal (GLS) model, Christensen (CA) model, and Anderson and Marasteanu (CAM) model. The viscoelastic properties of these

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.