Nonlinear dynamics and control of process systems with recycle

Process systems with material and energy recycle are well-known to exhibit complex dynamics and to present significant control challenges, due to the feedback interactions induced by the recycle streams. In this paper, we address the dynamic analysis and control of such process systems. Initially, we establish, through an asymptotic analysis, that (i) small recycle flowrates induce a weak coupling among individual processes, whereas (ii) large recycle flowrates induce a time scale separation, with the dynamics of individual processes evolving in a fast time scale with weak interactions, and the dynamics of the overall system evolving in a slow time scale where these interactions become significant; these slow dynamics is usually nonlinear and of low order. Motivated by this, we present (i) a model reduction methodology for deriving nonlinear low-order models of the slow dynamics induced by large recycle streams, and (ii) a controller design framework consisting of properly coordinated controllers in the fast and the slow time scales. The theoretical results are illustrated in a reaction-separation network with a large recycle compared to the throughput.     

An improved vessel extraction scheme from retinal fundus images

Vessel extraction from retinal fundus images is essential for the diagnosis of different opthalmologic diseases like glaucoma, diabetic retinopathy and hypertension. It is a challenging task due to presence of several noises embedded with thin vessels. In this article, we have proposed an improved vessel extraction scheme from retinal fundus images. First, mathematical morphological operation is performed on each planes of the RGB image to remove the vessels for obtaining noise in the image. Next, the original RGB and vessel removed RGB image are transformed into negative gray scale image. These negative gray scale images are subtracted and finally binarized (BW₁) by leveling the image. It still contains some granular noise which is removed based on the area of connected component. Further, previously detected vessels are replaced in the gray-scale image with mean value of the gray-scale image and then the gray-scale image is enhanced to obtain the thin vessels. Next, the enhanced image is binarized and thin vessels are obtained (BW₂). Finally, the thin vessel image (BW₂) is merged with the previously obtained binary image (BW₁) and finally we obtain the vessel extracted image. To analyze the performance of our proposed method we have experimented on publicly available DRIVE dataset. We have observed that our algorithm have provides satisfactory performance with the sensitivity, specificity and accuracy of 0.7260, 0.9802 and 0.9563 respectively which is better than the most of the recent works.

     

Obtaining model parameters of drying kinetics for highly shrinkable materials without knowing the surface area a priori

Shrinkage parameters of highly shrinkable materials such as length, diameter and surface area during drying are difficult to quantify in situ. However, these are significant components of an accurate model. In this study, an attempt to isolate the surface area effect is reported in order to fetch the REA model (reaction engineering approach) parameters without knowing it a priori. Carrot cube and cabbage leaf were selected as experimental material and dried with hot air under a range of conditions. Shrinkages was calculated using an optical method which is used to qualitatively compare with that “calculated” using the current approach. By matching the experimental temperature and moisture content profiles against time after obtaining REA parameters for both samples without knowing the surface area, the surface areas can be “calculated” numerically. Surface area was found to be affected by sample temperature as well as the moisture content. Drying simulations can be well carried out when correlating the surface area against sample moisture content X and temperature T, and it provides the best accuracy in predicting data on T and X vs. time. In addition, carrot cube can shrink ideally while cabbage leaf cannot. The overall relative errors of predicted moisture content and temperature were less than 1%.     

Directional filter bank-based fingerprint image quality

Pattern Analysis and Applications - Fingerprint image quality ensures that only the high-quality fingerprints containing a good amount of features are used for verification. Fingerprint matching...     

Anisotropy of Charge Transport in a Uniaxially Aligned Fused Electron-Deficient Polymer Processed by Solution Shear Coating

Precise control of the microstructure in organic semiconductors (OSCs) is essential for developing high-performance organic electronic devices. Here, a comprehensive charge transport characterization of two recently reported rigid-rod conjugated polymers that do not contain single bonds in the main chain is reported. It is demonstrated that the molecular design of the polymer makes it possible to achieve an extended linear backbone structure, which can be directly visualized by high-resolution scanning tunneling microscopy (STM). The rigid structure of the polymers allows the formation of thin films with uniaxially aligned polymer chains by using a simple one-step solution-shear/bar coating technique. These aligned films show a high optical anisotropy with a dichroic ratio of up to a factor of 6. Transport measurements performed using top-gate bottom-contact field-effect transistors exhibit a high saturation electron mobility of 0.2 cm² V⁻¹ s⁻¹ along the alignment direction, which is more than six times higher than the value reported in the previous work. This work demonstrates that this new class of polymers is able to achieve mobility values comparable to state-of-the-art n-type polymers and identifies an effective processing strategy for this class of rigid-rod polymer system to optimize their charge transport properties.     

Comparative study on the reachable workspaces of regular and irregular axially symmetric hexapod robots

Journal of Mechanical Science and Technology - Six-legged hexapod walking robots are well-known for their intrinsic stability during navigation and 6-DoF object manipulation. The robot must be...     

Impacts of electrification & automation of public bus transportation on sustainability—A case study in Singapore

Forschung im Ingenieurwesen - Electrification and automation are attracting interest from the public-transportation sector for their potential to improve energy efficiency, cost efficiency, and...     

Composite high frequency predictive scheme for efficient 2-D up-scaling performance

Multimedia Tools and Applications - This paper presents a new composite scheme to tackle the non-uniform blurring that arises because of image up-scaling. The image up-scaling results in...     

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.