Carbonate Reservoir Characterization Based on Grey Prediction Theory

This article aimed to determine the effectiveness of (Machine Learning, Artificial Neural Networks, Pattern Recognition and Data Analysis, and Fire Dynamics Simulations) in improving the results of arson investigations in the United Arab Emirates (UAE). The research relied on the descriptive method. The study community consisted of all members of the teams specialized in fire investigations. The research sample was taken using a simple random method that included (74) individuals from the research community. The researcher relied on the questionnaire as the main instrument for collecting data from the study sample. The results of the study showed that artificial intelligence techniques (Machine Learning, Artificial Neural Networks, Pattern Recognition and Data Analysis, and Fire Dynamics Simulations) can be effective in improving the results of arson investigations. The study recommended providing training programs to investigators on how to make full use of AI techniques in the investigation process.

Revealed the strength variation of concrete C30 and C50 after the cold in the initial .Using the gray prediction theory which from the cybernetics to analyze the test results after the initial cold.The results show that the results obtained by the formula is consistent with the experimental results, the residuals are within the control range.The experimental in cold for 120 days is much closer than theoretical values when compared with the residual for 90 days.Research shows that with the increase of initial freezing time, the loss rate of concrete strengthincreased.And the residuals showed a significant increasing trend when the number of freeze-thaw cycles increased.

Based on the statistical data during the period from 2001 to 2004 released by Henan statistical bureau in China, this paper aims to predict the amount of scientists and technicians of Henan province in China by the usage of GM (1, 1) model with the four items. The result of this etechnological policiesmpirical study is that the grey prediction theory can fit the scientists and technicians amount development precisely in Henan province. The accuracy of the prediction result is above 90% and corresponds with a distinction, which the grey theory can meet expectations with small samples or data. Research results show that this model established in this paper could provide valuable information for policy makers in their efforts to make appropriate technological policies.

In the HDFS cloud storage environment, the existing replica strategy is not able to adopt the optimal replica according to the current system environment dynamically, which affects the performance of the Cloud Storage system. This paper aims at the replica selection strategies in the load balancing problem, proposes a Replica Strategy based on grey prediction theory in Cloud Storage and predicts the node load rate of different replicas at a moment, selecting the minimum load node dynamically, eventually to load balance.

Integration of modern remote sensing technologies for faster utility mapping and data extraction

Intelligent ground vehicles require automated steering control systems. The complexity of the automated steering systems has increased as the concurrent data receiving, processing, decision-making and monitoring all happen simultaneously in super-fast sampling rates. The research reported in this paper focuses on the neural network training and machine learning approach for an automated steering system. The essential information of the steering controller was trained using artificial neural network (ANN) and pattern recognition algorithm approaches. The objective is to investigate the design of ANN training and deep machine learning on collected data from sensors of steering wheel angular position, speed, steering column torque, and ego vehicle speed and generating acceptable steering commands for the vehicle. The results of the research showed that the proposed network control system had trained and validated more than 96.5% steering system behavior patterns and adapted large random disturbances of the steering controller commands. It is, therefore, necessary to develop artificial intelligence methodologies in automated steering systems of autonomous vehicles with neural network representing the main topology blocks of the control system architecture and utilize ANN abstraction in the control system of autonomous vehicle steering control system.

Gray predicting theory and application of energy consumption of building heat-moisture system

The gray model based on the gray theory is a model of continuous differential equation. We can make a comprehensive observation and a long-range prediction to the variety and development of the safe accident by using this model. This paper mainly introduced the basic theory of the gray prediction, firstly, a prediction model of the drill stem failure in deep well was set up with this theory, and then we used it to predict drill stem failures of deep well happened in Anda district, Zhaozhou district and Datong district in Daqing oil field in recent 10 years. The fairy well tally between the predicted results and the actual facts showed that the prediction model was accurate enough for drill engineer.

The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C-H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.

An accurate estimation of porosity and permeability are extremely essential for designing an ideal and efficient program of an oil and gas field development. Numerous methods have been developed to determine the porosity and permeability including laboratory measurements and log derived models. Artificial neural network (ANN) provides an efficient technique that successfully addressed several engineering and geological challenges. In the present study ANN is applied to help in predicting porosity and permeability in carbonate reservoirs using back propagation neural network (BPNN) with high accuracy on well log data from numerous fields worldwide. ANN has the ability to understand a highly non – linear relationship and to perform simulation studies in a rapid manner. The BPNN model of porosity and permeability is developed using a set of well logging data as input layers and core porosity and core permeability as output layers. Two scenarios are considered to develop by ANN. The first scenario considers using all available logging data directly as input for ANN. In the second scenario an additional input, diagenesis parameter, is added as input to ANN which is essentially calculated from logging data. In each scenario two models are developed; the first for porosity prediction and the second predicts permeability in carbonate reservoirs. The optimal learning rate and momentum constant used in the BPNN model are achieved after serial combinative trials. The available data was assigned 80% for training and 20% for verification. The results of the developed porosity and permeability models are well compared to core data in verification. In the first scenario, cross-plot of the actual porosity versus ANN predicted Porosity exhibited a good match with a correlation coefficient equal to 0.97. Cross-plot of the actual permeability versus ANN predicted permeability exhibited a good match with a correlation coefficient equal to 0.80. In the second scenario, cross-plot of the actual porosity versus ANN predicted porosity exhibited a good match with a correlation coefficient equal to 0.97. Cross-plot of the actual permeability versus ANN predicted permeability exhibited a good match with a correlation coefficient equal to 0.98. Such data indicate that the developed models are successful in predicting the porosity and permeability for carbonate reservoirs.

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Breast cancer has an important incidence in women worldwide. Early diagnosis of this illness plays a key role in decreasing its mortality and improves its prognosis. Currently, mammography is considered as the standard examination for detection of breast cancer. However, the identification of breast abnormalities and the classification of masses on mammographic images are not trivial tasks for dense breasts, and is a challenge for artificial intelligence and pattern recognition. This work presents preliminary results of automatic classification of mammographies by texture characterization based mainly on the Haralick's descriptors. We implement an artificial neural network (ANN) for classification in three classes: normal, benign and cancer using leave one out technique. The set of images for training and testing the ANN, are taken from the Digital Database for Screening Mammography (DDSM). Results show that the percentage of correct classification occurs in average for 84.72% of the data set.

This study attempts to apply the principle of neural networks and pattern recognition (PR) technologies to real-time recognition by client-server network structure into a web-based recognizing system. In this paper, we recommend a Web-Based PR technology, which is improved recurrent neural network (RNN) from possessing feedback and non-linear activation function with its input, be taken out threshold. The purpose of this article is. to construct a Client-Server network structure for PR system with associative memory. The Server-end is built a databases management system for storage sample patterns. In proceed with training, the user can real-time assign any pattern, which is a record in the Server-end databases. In deal with retrieve task, we propose a novel PR method via databases matching, it can efficient solve spurious states problem from RNN in the WBPR system. On the other hand, taking advantage of Database Matching is to overcome the capacity restrictions on RNN. In order to clarify and corroborate the above Web-Based PR technology, thus a simulation experiment will be presented and their algorithms are also discussed.

The accurate prediction of passenger turnover is an important foundation and one of main basis of passenger transportation organization. It is also an important guarantee for the transportation industry to face the market and grasp the future. This paper used the grey correlation prediction theory to construct the GM (1,1) model. Based on the historical data of China's passenger transport before 2020, this paper respectively predicts the turnover of China's railway passenger transport, highway passenger transport and aviation passenger transport in 2030. This study shows that the prediction accuracy of the model is relatively higher. Prediction values are also basically in line with the actual development of China’s passenger transport in the future. Under the background of carbon peak, the results predicted in this paper can provide reference for the adjustment of the current passenger transport structure, and have certain significance for the development of passenger transport.

Flexible manufacturing system scheduling is a complex problem that leads to a high level of uncertainty due to the existence of only a few particular feasible solutions in an extensive search space. A framework is developed displaying the capabilities of learning and self-improvement, providing the necessary adaptive scheme to respond to the nature of flexible manufacturing systems scheduling. This proposed framework uses a hybrid architecture that integrates artificial neural networks and knowledge-based systems to generate real-time solutions. The artificial neural networks perform pattern recognition and, due to their inherent characteristics, support the implementation of automated knowledge acquisition and refinement strategies. The knowledge-based system, on the other hand, drive the inference strategy and interpret the constraints and restrictions imposed by the upper levels of the control hierarchy of the flexible manufacturing system. The level of performance thus achieved provides a system architecture with a higher probability of success than traditional approaches.