Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems

This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crude oil systems using type-2 fuzzy logic systems. PVT properties are very important in the reservoir engineering computations, and its accurate determination is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially in uncertain situations coupled with their characteristics instability during predictions. In this work, a type-2 fuzzy logic based model is presented to improve PVT predictions. In the formulation used, the value of a membership function corresponding to a particular PVT properties value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty. In this way, the model will be able to adequately model PVT properties. Comparative studies have been carried out and empirical results show that Type-2 FLS approach outperforms others in general and particularly in the area of stability, consistency and the ability to adequately handle uncertainties. Another unique advantage of the newly proposed model is its ability to generate, in addition to the normal target forecast, prediction intervals without extra computational cost.     

Experimental investigation and finite element modelling of the effects of flow velocities on a skewed integral bridge

This paper presents finite element modelling of the effects of different flow velocities on the structural behaviour of a skewed integral bridge. Flow velocities affect the scour depths at the piles of a bridge and thus affect its structural behaviour. Laboratory tests on a scaled-down hydraulic model along with numerical modelling were performed to simulate the structural behaviour of the scoured integral bridge. A finite element package was used for the numerical modelling work, and the displacements and strains corresponding to the measured locations on the physical model were extracted. The experimental and numerical results for the case of maximum scour depths were compared.     

Power efficient scheduling heuristics for energy conservation in computational grids

Computation grids and computational clouds are becoming increasingly popular in the organizations which require massive computational capabilities. Building such infrastructures makes a lucrative business case, thanks to availability of cheap hardware components and affordable software. Maintaining computational grids or cloud, however, require careful planning as in such dedicated environments, round-the-clock availability of workstations is very crucial. Ensuring uninterrupted availability, not only demands mechanism for failover redundancy but also results in constant power drainage. The tradeoff between the cost and the performance is the constant dilemma that the operations of the data centers face today. In this paper, we propose various heuristics for power-aware scheduling algorithms for scheduling jobs with dependent tasks onto the computational grid. We formulate the problem as a multi-objective function which results in various cost-performance tradeoffs each lying within the solution boundary.     

Feasibility of Electrochemical Deposition of Nickel/Silicon Carbide Fibers Composites over Nickel Superalloys

Nickel superalloys are typical materials used for the hot parts of engines in aircraft and space vehicles. They are very important in this field as they offer high-temperature mechanical strength together with a good resistance to oxidation and corrosion. Due to high-temperature buckling phenomena, reinforcement of the nickel superalloy might be needed to increase stiffness. For this reason, it was thought to investigate the possibility of producing composite materials that might improve properties of the metal at high temperature. The composite material was produced by using electrochemical deposition method in which a composite with nickel matrix and long silicon carbide fibers was deposited over the nickel superalloy. The substrate was Inconel 718, and monofilament continuous silicon carbide fibers were chosen as reinforcement. Chemical compatibility was studied between Inconel 718 and the reinforcing fibers, with fibers both in an uncoated condition, and coated with carbon or carbon/titanium diboride. Both theoretical calculations and experiments were conducted, which suggested the use of a carbon coating over the fibers and a buffer layer of nickel to avoid unwanted reactions between the substrate and silicon carbide. Deposition was then performed, and this demonstrated the practical feasibility of the process. Yield strength was measured to detect the onset of interface debonding between the substrate and the composite layer.

     

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.     

Malware analysis performance enhancement using cloud computing

Nowadays, computer based technology has taken a central role in every person life. Hence, damage caused by malicious software (malware) can reach and effect many people globally as what could be in the early days of computer. A close look at the current approaches of malware analysis shows that the respond time of reported malware to public users is slow. Hence, the users are unable to get prompt feedback when reporting suspicious files. Therefore, this paper aims at introducing a new approach to enhance malware analyzer performance. This approach utilizes cloud computing features and integrates it with malware analyzer. To evaluate the proposed approach, two systems had been prepared carefully with the same malware analyzer, one of them utilizes cloud computing and the other left without change. The evaluation results showed that the proposed approach is faster by 23 % after processing 3,000 samples. Furthermore, utilizing cloud computing can open door to crowd-source this service hence encouraging malware reporting and accelerate malware detection by engaging the public users at large. Ultimately this proposed system hopefully can reduce the time taken to detect new malware in the wild.     

Unordered rule discovery using Ant Colony Optimization

In this article,a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization（ACO）. The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets,selected from miscellaneous domains,based on several performance measures. As opposed to its ancestors,our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.     

Multiclass Support Vector Machines for Classification of ECG Data with Missing Values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values.     

Use of high‐resolution satellite data for the structural and agricultural inventory of tank irrigation systems

Tanks are small storage reservoirs impounding the runoff from monsoon rains to regulate the supply of water mainly for irrigated command areas that are typically less than 200 ha. They account for one‐third of the irrigated areas in Tamil Nadu, Karnataka and Andhra Pradesh. Years of neglect and indifference in tank maintenance and management have eroded their functional efficiency and jeopardized their multifarious benefits. In Tamil Nadu, this has resulted in a decline in their contribution to irrigation from 40% in 1995 to 25% in 2000. The modernization of these tanks requires prioritization and investment. Remote sensing technology, with its unique advantages and the latest high‐resolution sensors, can provide the information on the agricultural, hydrological and structural conditions of the tank irrigation systems necessary for prioritization. The National Remote Sensing Centre (NRSC) has carried out a study of the Nanjur tank cascade in Tamil Nadu using high‐resolution data from the IKONOS satellite during the crop season of 2003–2004. This study demonstrated the use of high‐resolution satellite images to obtain an inventory of the different components of a tank irrigation system such as tank bunds, surplus weirs, supply channels and distribution networks. It was also found useful in updating the road–rail network at village level. The 1‐m merged satellite data were useful in mapping open wells and minor roads in a tank cascade. The cropping pattern in a tank system can be mapped at cadastral level using these images, which will be useful for micro‐level water and agricultural management. The 4‐m multispectral image was found to be sufficient for mapping different crops at field level. The high‐resolution image also provided information on intrafield variability in crop condition. The reliability and cost‐effectiveness of high‐resolution images from Indian satellites provide scope for the generation of information for tank system studies as well as for micro‐level natural resource management.