Computer Vision System (CVS) for In-Line Monitoring of Visual Texture Kinetics During Shrimp (Penaeus Spp.) Drying

In this study, the effects of drying medium temperature and velocity were surveyed on the image texture features of shrimp (Penaeus spp.) batches in a dryer equipped with a perpendicular dual-view computer vision system (CVS). This was carried out by applying an innovative rotation- and scale-invariant image texture processing approach with the capability of eliminating the effects of sample shrinkage on the visual textural features. Moreover, the variations in image texture parameters were investigated with moisture ratio, color, and geometrical characteristics of the shrimp samples. Drying experiments were conducted at hot air drying (HAD) temperatures of 50–90°C and superheated steam drying (SSD) temperatures of 110–120°C with drying medium velocities of 1–2 m/s. Several configurations of a multilayer perceptron artificial neural network (MLP-ANN) were also used to predict the moisture ratio and the geometrical characteristics of the shrimp batch using the image texture parameters. Generally, the image texture features were significantly affected by drying medium temperatures (p < 0.01), and the effects of drying medium velocities on the textural properties were nonsignificant (p > 0.05). Additionally, the higher drying temperatures generated products with uniform and regular texture patterns. The SSD produced samples with somewhat nonuniform and irregular texture patterns compared with HAD at 90°C. Finally, selected MLP-ANN topologies successfully predicted the moisture ratio and the geometrical characteristics of the shrimp batch using the textural properties with correlation coefficients higher than 0.99.     

Arbitrary dual-band unequal Wilkinson power divider based on negative refractive index transmission lines

This paper focused on the application of negative refractive index transmission line (NRI-TL) in dual-band unequal Wilkinson power divider (WPD) with controllable frequency and power dividing ratio. Theory and design procedure of the dual-band NRI-TL are presented in details. For demonstration, two dual-band unequal Wilkinson power dividers (WPDs) with power dividing ratio of 2 : 1 and operating frequencies of 0.9 and 1.8 GHz are designed, fabricated and tested. The first unequal divider is based on 2-stage NRI-TLs and the second one is based on 4-stage NRI-TLs. In addition, these two types of NRI-TLs are presented to demonstrate that by increasing the number of NRI-TL unit-cells the phase response of the NRI-TLs converge to the desired characteristic. The good agreement between measured and simulated results confirmed the design concept and derived closed-form design equations. Measurements show that the first divider has 18.37% and 21.86% relative bandwidths and the second one has 33.52% and 29.12% relative bandwidths at 0.9 and 1.8 GHz, respectively. The design concept of this paper can be extended to equal dual-band power dividers with arbitrary frequency ratio.     

An expert egg grading system based on machine vision and artificial intelligence techniques

The main purpose of this research was design and development of an intelligent system based on combined fuzzy logic and machine vision techniques for grading of egg using parameters such as defects and size of eggs. The detected defects were internal blood spots, cracks and breakages of eggshell. The Hue-Saturation-Value (HSV) color space was found useful in obtaining visual features during Image Processing (IP) stage. The fuzzy inference system (FIS) was designed based on triangular and trapezoidal membership functions, fuzzy rules with logical operator of AND inference system of Mamdani and method of center average for defuzzifier. The evaluation results of IP algorithms showed that use of IP technique has good performance for defects and size detection. The Correct Classification rate (CCR) was 95% for size detection, 94.5% for crack detection and 98% for breakage detection. The overall accuracy FIS model in grading of the eggs was 95.4.     

LIQUID-LIQUID MIXING IN STIRRED VESSELS: A REVIEW

Liquid-liquid mixing is a key process in industries that is commonly accomplished in mechanical agitation systems. Liquid-liquid mixing performance in a stirred tank can be evaluated by various parameters, namely minimum agitation speed, mixing time, circulation time, power consumption, drop size distribution, breakup and coalescence, interfacial area, and phase inversion. The importance of these liquid-liquid mixing parameters, the measurement method, and the results are discussed briefly. Input parameters such as impeller type, power number, flow pattern, number of impellers, and dispersed phase volume fraction, in addition to physical properties of phases such as viscosity and density, are reviewed. Scale-up aspects are also included.     

A robust algorithm for a class of optimal control problems subject to regional stability constraints and disturbances

A robust globally convergent algorithm for solving the optimization control problem (OCP) in both state feedback controller and observation control system is investigated. Finding the OCP adjoint parameter for computing the optimal observer gain and feedback gain vectors are desired. First, the optimal control problem considering stability of degree constrains and disturbance that affects the dynamics of system is converted into a two-point boundary value problem (TPBVP). Then, we apply the He’s polynomials based on homotopy perturbation method (HPM) as an efficient method to find both optimal gains. The algorithm will be modified do decrease the number of iterations required to attain a desired control problem cost function. As a result lower computational complexity is required when compared with other state of the art methods. Applying the HPM makes the solution procedure become easier, simpler and more straightforward. In the proposed method the control problem can be solved with lower amplitudes of the input signal (control effort), comparing with analytical method. Lower control efforts may also help to avoid saturation effects, and to restrain the system to work within linear operating areas of the state space. On the other hand, there is a tradeoff between control effort and the degree of optimality obtained. For demonstrating the simplicity and efficiency of the proposed optimal control method, the algorithm is compared with a control architecture using the Kalman filter estimator and a controller gain designed by the Lyapunov’s second method.     

Automatic segmentation of digitized data for reverse engineering applications

Reverse engineering is the process of developing a Computer Aided Design (CAD)model and a manufacturing database for an existing part. This process is used in CAD modeling of part prototypes, in designing molds, and in automated inspection of parts with complex surfaces. The work reported in this paper is on the automatic segmentation of 3-Dimensional (3-D) digitized data captured by a laser scanner or a Coordinate Measuring Machine (CMM) for reverse engineering applications. Automatic surface segmentation of digitized data is achieved using a combination of region and edge based approaches. It is assumed that the part surface contains planar as well as curved surfaces that are embedded in a base surface. The part surface should be visible to a single scanning probe (21/2D object). Neural network algorithms are developed for surface segmentation and edge detection. A back propagation network is used to segment part surfaces into surface primitives which are homogenous in their intrinsic differential geometric properties. The method is based on the computation of Gaussian and mean curvatures of the surface. They are obtained by locally approximating the object surface using quadratic polynomials. The Gaussian and mean curvatures are used as input to the neural network which outputs an initial region-based segmentation in the form of a curvature sign map. An edge based segmentation is also performed using the partial derivatives of depth values. Here, the output of the Laplacian operator and the unit surface normal are computed and used as input to a Self-Organized Mapping (SOM) network. This network is used to find the edge points on the digitized data. The combination of the region based and the edge based approaches, segment the data into primitive surface regions. The uniqueness of our approach is in automatic calculation of the threshold level for segmentation, and on the adaptability of the method to various noise levels in the digitized data. The developed algorithms and sample results are described in the paper     

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.     

Two-stage robust energy management of a hybrid charging station integrated with the photovoltaic system

The optimal management of charging stations has become a critical issue in recent years. In this paper, the energy management of a hybrid charging station composed of an electrolyzer, fuel cell and hydrogen storage is analyzed that is integrated with a photovoltaic system. As well, the station is connected to the local power market to increase flexibility and it is assumed that the manager of the charging station is an intelligent decision-maker who tries to minimize the cost of vehicle. Due to the existence of uncertainties, generation of photovoltaic, market price and load demand are considered as uncertain parameters and two-stage stochastic programming is applied to model them. To achieve optimal management, a robust optimization approach is proposed for the uncertainty of day-ahead market price where the decision-maker adjusts the conservatism level. The presented method is linear risk-constrained programming that the results for risk-neutral and risk-averse strategies are compared. To validate the accuracy and robustness of the approach, interval-based stochastic programming is also implemented. According to the robust optimization, day-ahead market price uncertainty increases the total expected cost by about 8.9%. In return, the risk of scheduling is reduced significantly with the risk-averse strategy.     

Effects of Graphene Oxide Nanosheets and Al2O3 Nanoparticles on CO2 Uptake in Semi‐clathrate Hydrates

Gas hydrate/clathrate hydrate formation is an innovative method to trap CO₂ into hydrate cages under appropriate thermodynamic and/or kinetic conditions. Due to their excellent surface properties, nanoparticles can be utilized as hydrate kinetic promoters. Here, the kinetics of the CO₂ + tetra‐n‐butyl ammonium bromide (TBAB) semi‐clathrate hydrates system in the presence of two distinct nanofluid suspensions containing graphene oxide (GO) nanosheets and Al₂O₃ nanoparticles is evaluated. The results reveal that the kinetics of hydrate formation is inhibited by increasing the weight fraction of TBAB in aqueous solution. GO and Al₂O₃ are the most effective kinetic promoters for hydrates of (CO₂ + TBAB). Furthermore, the aqueous solutions of TBAB + GO or Al₂O₃ noticeably increase the storage capacity compared to TBAB aqueous solution systems.