The comparison of incomplete sensitivities and Genetic algorithms applications in 3D radial turbomachinery blade optimization

In the present work, a centrifugal pump impeller’s blades shape was redesigned to reach a higher efficiency in turbine mode using two different optimization algorithms: one is a local method as incomplete sensitivities–gradient based optimization algorithm coupled by 3D Navier–Stokes flow solver, and another is a global method as Genetic algorithms and artificial neural network coupled by 3D Navier–Stokes flow solver. New impeller was manufactured and tested in the test rig. Comparison of the local optimization method results with the global optimization method results showed that the gradient based method has detected the global optimum point. Experimental results confirmed the numerical efficiency improvement in all measured points. This study illustrated that the developed gradient based optimization method is efficient for 3D radial turbomachinery blade optimization.     

A hybrid capacitive pressure and temperature sensor fabricated by adhesive bonding technique for harsh environment of kraft pulp digesters

We have developed a compensated capacitive pressure and temperature sensor for kraft pulp digesters (pH 13.5, temperatures 25–175°C reaching a local maximum of 180°C and pressures up to 2 MPa). The gauge capacitive pressure sensor was fabricated by bonding silicon and Pyrex chips using a high temperature, low viscosity UV (ultraviolent) adhesive as the gap-controlling layer and heat curing adhesive as the bonding agent. A simple chip bonding technique, involving insertion of the adhesive into the gap between two chips was developed. A platinum thin-film wire was patterned on top of a silicon chip to form a resistance temperature detector (RTD) with a nominal resistance of 1,500 Ω. A silicon dioxide layer and a thin layer of Parylene were deposited to passivate the pressure sensor diaphragm and the sensors were embedded into epoxy for protection against the caustic environment in kraft digesters. The sensors were tested up to 2 MPa and 170°C in an environment chamber. The maximum thermal error of ±1% (absolute value of ±20 kPa) full scale output (FSO) and an average sensitivity of 0.554 fF/kPa were measured. Parylene-coated silicon chips were tested for a full kraft pulping cycle with no signs of corrosion.     

Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory

The errors resulting from satellite configuration geometry can be determined by Geometric Dilution of Precision (GDOP). Considering optimal satellite subset selection, lower GDOP value usually causes better accuracy in GPS positioning. However, GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure. This paper deals with classification of GPS GDOP utilizing Parzen estimation based Bayesian decision theory. The conditional probability of each class is estimated by Parzen algorithm. Then based on Bayesian decision theory, the class with maximum posterior probability is selected. The experiments on measured dataset demonstrate that the proposed algorithm lead, in mean classification improvement, to 4.08% in comparison with Support Vector Machine (SVM) and 9.83% in comparison with K-Nearest Neighbour (KNN) classifier. Extra work on feature extraction has been performed based on Principle Component Analysis (PCA). The results demonstrate that the feature extraction approach has best performance respect to all classifiers.     

A new fuzzy decision-making procedure applied to emergency electric power distribution scheduling

In this paper a new fuzzy decision-making procedure is developed. Two levels of weightings, called upper and lower weights, are proposed to calculate the fuzzy weightings of different criteria. The preference table is introduced to calculate the upper and lower weights. Also a new method is developed to determine the expected preference values of different alternatives. These values are used for generating the priority list of alternatives. The procedure is applied for providing an emergency electric power distribution, scheduling time table. The IEEE 14-bus standard system and a 14-bus system with four power plants are considered as case studies. Using fuzzy decision-making procedure, the regions with more priorities are more connected to the distribution net, while the maximum consumption criterion is also reasonably respected.     

Nano levels detection of beryllium by a novel beryllium PVC-based membrane sensor based on 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy

A novel poly(vinyl chloride)-based 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy (PBC) with sodium tetraphenyl borate (NaTPB) as an anion excluder, benzyl acetate (BA), acetophenon (AP) and o-nitrophenyloctyl ether (NPOE) as plasticizing solvent mediators was prepared and investigated as a beryllium selective sensor. The best performance was observed with the membrane having the PVC–NaTPB–NPOE–PBC composition 30%:3%:62%:5%, which worked well over a very wide concentration range (1.0×10⁻⁷ M to 1.0×10⁻¹ M). The sensor exhibits a Nernstian slope of 29.9 mV per decade of Be²⁺ activity. The detection limit of the sensor is 7.0×10⁻⁸ M (630 ppt). The proposed electrode shows excellent discriminating ability toward Be²⁺ ion with regard to alkali, alkaline earth, transition and heavy metal ions. It was successfully applied to the determination of beryllium in a mineral sample.     

Microencapsulation of Nigella sativa seeds oil containing thymoquinone by spray‐drying for functional yogurt production

The present work reports on the microencapsulation of Nigella sativa seeds oil containing thymoquinone (TQ) by spray‐drying, using modified starch (MS) and maltodextrin (MD) mixture as wall materials aimed at producing functional yogurt. First, the impact of different ratios of MS/MD on microencapsulation efficiency (ME) and TQ retention was investigated. The highest ME (90.10%) was found in microcapsules prepared from emulsion with 80/20 ratio of MS/MD; however, the microcapsules prepared with 50/50 ratio was selected for considering TQ stability under storage conditions and functional yogurt production due to an acceptable ME (89.48%) and better TQ retention (61.12%). The results showed that the microcapsules stored at refrigerator temperature had the highest content of TQ after 4 weeks. Moreover, the results of chemical and sensory analysis suggest that N. sativa seeds oil microcapsules can be used for producing functional yogurt due to high stability of TQ and proper chemical and sensory properties.     

Preparation of immunomagnetic beads coupled with a rhodamine hydrazine immunosensor for the detection of Mycobacterium avium subspecies paratuberculosis in bovine feces,milk, and colostrum

The aim of this study was to develop and evaluate a method for detecting Mycobacterium avium ssp. paratuberculosis (MAP) bacteria in bovine fecal, milk, and colostrum samples using immunomagnetic beads (IMB) and a rhodamine hydrazone immunosensor. Immunomagnetic beads were prepared by using purified antibodies from hyperimmunized sera that were coupled to Fe nanoparticles with diethylene triamine pentaacetic acid (DTPA) or ethyl (dimethyl aminopropyl) carbodiimide (EDC)-N-hydroxy succinimide (NHS) as linkers. Rhodamine hydrazone particles were synthesized and coupled to IgY anti-MAP antibodies using DTPA or EDC-NHS linkers. Separation efficiency of the IMB was tested on bovine fecal, milk, and colostrum samples experimentally contaminated with MAP. The studied methods were evaluated on their ability to detect MAP and separate bacteria in complex mediums. The ELISA results indicated 95% efficacy in antibody coupling to IMB, with the DTPA-IMB method being more efficient than the EDC-NHS-IMB method. By using the DTPA-IMB method, MAP bacteria were successfully recovered from fecal, milk, and colostrum samples. The DTPA-IMB method used in combination with the rhodamine hydrazone immunosensor had a limit of detection equal to 30 and 30,000 MAP cells/mL using chromogenic and fluorescent properties, respectively. Combining the DTPA-IMB separation method with the rhodamine hydrazone immunosensor provides a fast, sensitive, and cost-beneficial method for detecting MAP in bovine feces, milk, and colostrum.