Application of fuzzy c-means algorithm as a novel method to predict density of mixtures of Athabasca bitumen and heavy n-alkane

The significant number of oil reservoir are bitumen and heavy oil. One of the approaches to enhance oil recovery of these types of reservoir is dilution of reservoir oil by injection of a solvent such as tetradecane into the reservoirs to modify viscosity and density of reservoir fluids. In this investigation, an effective and robust estimating algorithm based on fuzzy c-means (FCM) algorithm was developed to predict density of mixtures of Athabasca bitumen and heavy n-alkane as function of temperature, pressure and weight percent of the solvent. The model outputs were compared to experimental data from literature in different conditions. The coefficients of determination for training and testing datasets are 0.9989 and 0.9988. The comparisons showed that the proposed model can be an applicable tool for predicting density of mixtures of bitumen and heavy n-alkane.     

Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.     

In situ neutron diffraction measurement of strain relaxation in welds during heat treatment

Neutron diffraction (ND) is commonly used to investigate the stress redistribution before and after post-weld heat treatment (PWHT) in welded structures. However, there is a lack of information on the evaluations of strains during PWHT. The present work employed in situ ND to measure the relaxation of residual strains during conventional PWHT in multi-pass high-strength low-alloy steel welds. It was found that strain relaxation occurs principally during the heating stage of the heat treatment. The findings have important economic bearings and can be used to characterise comparable material combinations and optimise the PWHT process for high-strength low-alloy weld joints. This unique information also provides a valuable benchmark for the finite element modelling of this complex process.     

Influence of novel surface modifying macromolecules and coagulation media on the gas permeation properties of different polymeric gas separation membranes

Integrally skinned asymmetric membranes for the separation of O₂ and N₂ were fabricated by the phase inversion technique from polysulfone, polyetherimide, and polyimide. Two types of surface modifying macromolecules (SMMs) including hydrophilic SMM (LSMM) and charged SMM (cSMM) were synthesized and blended with the casting solution to modify the membrane surface. The cast film was then immersed in the first coagulant alcohol (methanol, ethanol, or isopropanol) for a predetermined period, before being immersed in the second coagulant (water). The SMMs used in these experiments were laboratory synthesized by the two‐step process of polyurethane prepolymer synthesis and end capping, before being characterized by differential scanning calorimetry. Their molecular structure was determined from the molecular weight obtained by gel permeation chromatography. The membranes were characterized by contact angle measurement and O₂ and N₂ gas permeation performance. Attempts were made to interpret the gas permeation data by delayed demixing affected by solubility parameters of polymer, solvent, and nonsolvent. Furthermore, the permeation performance of cSMM membranes was interpreted by the solvation of the charged sulfonate groups present in cSMM. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

从MATLAB到嵌入式C代码的实现

从设想到硬件实现的鸿沟令从事嵌入式开发的工程师们深感头疼,把MATLAB指令自动翻译成C代码将令他们感到鼓舞。本刊将于十、十一月连载两篇系列技术文章介绍如何实施这个技术,本月文章是第一部分。主要介绍如何从MATLAB命令行直接生成有效的嵌入式C代码。第二部分文章将讨论如何将嵌入式MATLAB代码整合到Simulink模块中,使算成为基于模型设计流程中的一部分。     

Big Data Based Self-Optimization Networking in Next Generation Mobile Networks

Using self-optimization techniques is the only viable solution for increasing the efficiency in next generation mobile networks. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to microcell and femto-cell users, considering the limited resources in radio access networks. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process. Based on the meaningful extracted information, the SON decision maker will be able to adjust network parameters and resource allocation factors in a more intelligent manner. The experimental results show that despite the tremendous volume of the analyzed data—which is hundreds of times bigger than usual methods—it is possible to improve the KPIs, such as throughput, up to 30% by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25% when the number of parameters is too high. Moreover, numerical results indicate the superiority of using support vector machine learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15% increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.     

Psychiatric manifestations of systemic lupus erythematosus

Low-intensive laser radiation used by topical, intravenous, and transendoscopic routes was employed in the complex antituberculosis therapy of 120 patients with pulmonary tuberculosis concurrent with chronic nonspecific respiratory diseases (n = 48), diabetes mellitus (n = 20 patients), gastrointestinal diseases (n = 45). The results of treatment were compared with the parameters of 62 patients with the similar clinical and X-ray characteristics of a tuberculosis process and concomitant diseases. It was ascertained that laser therapy might enhance the efficiency of treatment of patients with concurrent abnormality statistically significantly, by reducing the time of bacterial isolation, closure of decay cavities in the lung tissue, hospital treatment and by accelerating the onset of stable remission of concomitant diseases.     

A General Model of Unit Testing Efficacy

Much of software engineering is targeted towards identifying and removing existing defects while preventing the injection of new ones. Defect management is therefore one important software development process whose principal aim is to ensure that the software produced reaches the required quality standard before it is shipped into the market place. In this paper, we report on the results of research conducted to develop a predictive model of the efficacy of one important defect management technique, that of unit testing. We have taken an empirical approach. We commence with a number of assumptions that led to a theoretical model which describes the relationship between effort expended and the number of defects remaining in a software code module tested (the latter measure being termed correctness). This model is general enough to capture the possibility that debugging of a software defect is not perfect and could lead to new defects being injected. The Model is examined empirically against actual data and validated as a good predictive model under specific conditions. The work has been done in such a way that models are derived not only for the case of overall correctness but also for specific types of correctness such as correctness arising from the removal of defects contributing to shortcoming in reliability (R-type), functionality (F-type), usability (U-type) and maintainability (M-type) aspects of the program subject to defect management.     

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

Considering the importance of biosensors on the Internet of the patient body that collect vital signs and transmit them to the coordinator, energy consumption and network lifetime are essential challenges in these networks. This paper, it has been tried to present a method based on adapting sampling rate through patient’s risk and discovered pattern by employing an intelligence method based on adaptive neuro-fuzzy inference system, interpolation function, and a biosensor patron. It causes restricting sensed and transmitted data to the coordinator. In the proposed schema, three methods containing Grid partitioning, Subtractive Clustering and fuzzy c-means have been used in two modes, including hybrid and error backpropagation, to predict the individual’s behavioral pattern and determine the patient's risk, attentively. The simulation results in MATLAB R2018b show that the proposed method reduces the network communications. It has improved energy consumption by up to three times and also reduced traffic by more than 80% compared to similar methods.

     

Accuracy and availability modeling of social networks for Internet of Things event detection applications

Wireless Networks - As Social networks are widely used by the people around the world, if this infrastructure can be used for event detection systems like fire forest detection, the overall cost of...