Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition

This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24 Aug 201

The impact of synthetic biology in chemical engineering - Educational issues

This paper describes the development of syntheticbiology as a distinct entity from current industrial biotechnology and the implications for a future based on its concepts. The role of the engineering design cycle, in syntheticbiology is established and the difficulties in making and exact analogy between the two emphasised. It is suggested that process engineers can offer experience in the application of syntheticbiology to the manufacture of products which should influence the approach of the synthetic biologist. The style of teaching for syntheticbiology appears to offer a new approach at undergraduate level and the challenges to the education of process engineers in this technology are raised. Possible routes to the development of syntheticbiology teaching are suggested

Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: A case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran

Determination of TOC is critical to the evaluation of every source rock unit. Methods which are dependent upon extensive laboratory testing are limited by the availability and integrity of the rock samples. Prediction of TOC (Total Organic Carbon) from well Log data being available for the majority of wells being drilled provides rapid evaluation of organic content, producing a continuous record while eliminating sampling issues. Therefore, the ideal method for determining the TOC fraction within source rock units would utilize common well log data. So a model was developed to formulate TOC values in the absence of laboratory TOC measurements from conventional well log data. Consequently, with the assistance of FL (Fuzzy Logic), TOC estimated from well log data with an overall prediction accuracy of 0.9425 for the test set. Following that TOC content of the Kazhdumi formation optimally has been divided into 4 zones using K-means cluster analysis, since searching for patterns is one of the main goals in data mining. There is a general increase in TOC from zone 1 to zone 4. The optimal number of zones has been detected by means of the knee method that finds the “knee” in a number of clusters vs. Compactness, Davies-Bouldin and Silhouette values. In the last step, using SVM (Support Vector Machine) and ANN (Artificial Neural Network) algorithms, two commonly used techniques, classification rules developed to predict the source rock class-membership (zones) from well log data. The proposed method is found effective in directly extracting patterns from well log data after defining classification rules. Quantitative comparisons of the results from ANN and SVM depicts that for classification problem of source rock zonation SVM with RBF (Radial Basis Function) kernel readily outperforms ANN in term of classification accuracy (0.9077 and 0.9369 for ANN and SVM, respectively), reduced computational time and highly repeatable results. This method would enable a more elaborate assessment of Kazhdumi formation to be undertaken by providing a comprehensive quick look results derived directly from well log data while using conventional methods one can’t define patterns within the data without grouping data manually

Fuzzy Logic and Corporate Governance Theories

[Excerpt] “Fuzzy logic is a theory that categorizes concepts or things belonging to more than one group. A methodology that explains how things function in multiple groups (not fully in one group or another) offers advantages when no one definition or membership in a group accounts for belonging to multiple groups. The principal/agent model of corporate governance has some characteristics of fuzzy logic theory. Under traditional agency theory of corporate governance, shareholders, directors, and senior corporate officers each belong to groups having multiple attributes. In the principal/agent model of corporate governance, shareholders are owners or principals; directors are shareholders and agents of the corporation; and senior corporate officers are directors’ agents, shareholders’ agents, and agents of the corporation. Each one functions within multiple groups serving multiple agency roles, and each owes fiduciary duties that vary depending on whose agent they are functioning as. Such a multi-dimensional role for corporate actors is a consequence of multi-definitional corporate purpose within agency theory of governance. This multi-dimensional group membership is not easily reconciled within agency theory and is therefore not always explained. However, traditional corporate governance theory can borrow another basic tenet of fuzzy logic theory. Fuzzy theory not only accounts for membership in multiple groups, but also explains how things work because they are multidimensional or ambiguous. This article seeks to explain the ambiguities of corporate governance theory and suggests a framework that accounts for the multi-agent role of senior corporate officers of public companies. It offers a kind of fuzzy logic theory for understanding the fiduciary duties of senior officers. The purpose of this article is to evaluate other models of corporate governance that account for the multi-agent role of senior officers of public companies and assess the ability of various models to hold senior officers accountable to the corporation.

Investigating SAR algorithm for spaceborne interferometric oil spill detection

The environmental damages and recovery of terrestrial ecosystems from oil spills can last decades. Oil spills have been responsible for loss of aquamarine lives, organisms, trees, vegetation, birds and wildlife. Although there are several methods through which oil spills can be detected, it can be argued that remote sensing via the use of spaceborne platforms provides enormous benefits. This paper will provide more efficient means and methods that can assist in improving oil spill responses. The objective of this research is to develop a signal processing algorithm that can be used for detecting oil spills using spaceborne SAR interferometry (InSAR) data. To this end, a pendulum formation of multistatic smallSAR carrying platforms in a near equatorial orbit is described. The characteristic parameters such as the effects of incidence angles on radar backscatter, which support the detection of oil spills, will be the main drivers for determining the relative positions of the small satellites in formation. The orbit design and baseline distances between each spaceborne SAR platform will also be discussed. Furthermore, results from previous analysis on coverage assessment and revisit time shall be highlighted. Finally, an evaluation of automatic algorithm techniques for oil spill detection in SAR images will be conducted and results presented. The framework for the automatic algorithm considered consists of three major steps. The segmentation stage, where techniques that suggest the use of thresholding for dark spot segmentation within the captured InSAR image scene is conducted. The feature extraction stage involves the geometry and shape of the segmented region where elongation of the oil slick is considered an important feature and a function of the width and the length of the oil slick. For the classification stage, where the major objective is to distinguish oil spills from look-alikes, a Mahalanobis classifier will be used to estimate the probability of the extracted features being oil spills. The validation process of the algorithm will be conducted by using NASA’s UAVSAR data obtained over the Gulf of coast oil spill and RADARSAT-1 dat

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Design Optimization of a Natural Gas Substation With Intensification of the Energy Cycle

Abstract Design Optimization of a Natural Gas Substation with Intensification of the Energy Cycle (Arcangelo Pellegrino and Francesco Villecco) Natural gas is currently the natural substitute of petroleum as an energy source, since the foreseen ending up of this latter in the next decades. As a matter of fact, natural gas is easier to handle, less dangerous to be transported, somehow environmentally more friendly. The gas ducts operate with large flow rates over very long distances at high pressures, which are usually lowered in proximity of the final substations by lamination valves which, in fact, dissipate energy. However, a careful management of the pressure reduction may allow an energy recovery while using the gas expansion to operate a turbine. In this case, gas must be preheated to compensate for the energy required by the expansion. A proper control of all the parameters involved becomes crucial to an intelligent use of these resources. In this paper, the possibility of using a pre-heating system has been examined as a way to intensify the energy cycle in an expansion substation of the city gas network. Fuzzy logic has been used to optimize the natural gas expansion in a turbine to produce electrical energy. A fuzzy system has been designed and realized to control the whole process of gas expansion, from the gas pre-heating to the pressure reduction. The system operates over the whole year, accounting for the pressure, temperature, and gas flow rate variations experienced in the gas line. The exit values of the latter and the inlet value of the gas pressure are selected as input variables, being the output variable the temperature of the pre-heating water at the heat exchanger inlet

Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid

[Abstract]: A comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel fuel and waste cooking biodiesel fuel blends. The results were found to be very comparable. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The total sulfur content of the produced biodiesel fuel was 18 ppm which is 28 times lesser than the existing diesel fuel sulfur content used in the diesel vehicles operating in Tehran city (500 ppm). The maximum power and torque produced using diesel fuel was 18.2 kW and 64.2 Nm at 3200 and 2400 rpm respectively. By adding 20% of waste vegetable oil methyl ester, it was noticed that the maximum power and torque increased by 2.7 and 2.9% respectively, also the concentration of the CO and HC emissions have significantly decreased when biodiesel was used. An artificial neural network (ANN) was developed based on the collected data of this work. Multi layer perceptron network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. The results showed that the training algorithm of Back Propagation was sufficient enough in predicting the engine torque, specific fuel consumption and exhaust gas components for different engine speeds and different fuel blends ratios. It was found that the R2 (R: the coefficient of determination) values are 0.99994, 1, 1 and 0.99998 for the engine torque, specific fuel consumption,CO and HC emissions, respectively

Design of computerized monitoring and processing system for magnetic field controlling against the phenomenon of black powder in crude oil pipelines

Black power represents the main difficulty faced by the oil flow in pipelines. The negative effect of this powder reaches to stop the oil flow due to clogging the pipelines, in addition to the damaging of the crude oil pumps. Many solutions have been proposed in literature based on chemical or physical processes. On the other side, applying the fixed magnetic field has been presented in separation and extraction process of metal impurities in water pipelines applications. From these facts, this paper proposes an alternative solution (idea, design, and methodology for future implementation) for the black power removing from oil pipelines. The proposed system works on firstly sensing the resistivity parameter in the crude oil as an indication about the oil status with respect to the quantity of the black powder particles, then works on monitoring and controlling the level, location, and polarity of the required magnetic field that to work on cracking particles cracking function that in order to facilitate the crude oil motion in the pipelines. In addition, the proposed solution presents a new design of electrical resistivity sensor as an important indication in terms of evaluating the proposed system performanc

Stratigraphy and reservoir quality of the turbidite deposits, western sag, Bohai bay, China P.R.

Stratigraphic and subtle reservoirs such as pinchouts, sand lenses and unconformities have been discovered in Bohai basin. These reservoirs occur in sub-basins and sag structures called depressions. A prolific depression is the Liaohe depression that has been filled with rapidly changing mixed alluvial fan deposit of the Cenozoic age. Attempts made at recovering residual hydrocarbon from the subtle reservoir have necessitated the re-evaluation of available data to characterize and model the prolific Shahejie Formation turbidite deposit occurring as pinchouts and sand lenses for hydrocarbon assessment, reservoir quality and possible recovery through enhanced methods. Methods employed covered well logs analysis, clustering analysis for electrofacies and fuzzy logic analysis to predict missing log sections. Stratigraphic and structural analysis was done on SEGY 3D seismic volume after seismic to well tie. Stochastic simulation was done on both discrete and continuous upscaled data. This made it possible to correctly locate and laterally track identified reservoir formation on seismic data. Petrophysical parameters such as porosity and permeability were modeled with result of clustering analysis. Result shows that electrofacies converged on 2 rock classes. The area is characterized by the presence of interbeded sand-shale blanket formations serving as reservoir and seal bodies. The reservoir quality of the formations as seen on the petrophysical analysis done is replicated in simulation volume results. Reservoir rocks have porosity between 0.1 and 0.25, permeability between 1 and 2mD and hydrocarbon saturation as high as 89%. Lithofacies are observed to be laterally inconsistent, sub-parallel to dipping and occurring as porous and permeable continuous beds or pinchouts hosting hydrocarbon. The stochastic stratigraphic model depicts rock units in associations that are synsedimentary. The prevalent configuration gotten from the model gave an insight into exploring and developing the field for enhanced oil recovery of the heavy hydrocarbon of this area