Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.     

Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods.     

基于特征贡献率的机械故障分类方法

为了使7075铝合金薄壁件表面喷丸加工应力得到松弛和均化，运用ANSYS建立平台式振动时效有限元模型，通过振动模态和谐响应分析，获得最佳激振频率和试样装夹位置。在此基础上，将薄壁框架件置于平台上进行振动时效处理，以验证仿真分析结果，评价其对试样表面应力作用效果。结果表明：在亚共振频率为112 Hz时，试样在平台零振幅和最大弯曲位置处分别对称布置，时效后表面均形成应力松弛，松弛率分别为29.5%和33.3%，不均匀波动分别为20.3%和20.7%，而沿振动方向松弛程度不均匀，说明激振力造成了材料表面组织屈服，且屈服程度与应力强度有关。提出再时效-位置交叠方法，使应力松弛不均匀度分别下降到9.9%和15.9%，应力均化得到改善，该研究工作为薄壁类零件表面应力均匀化提供了参考。     

Prediction of composite suitability index for physical habitat simulations using the ANFIS method

A physical habitat simulation is a useful tool for assessing the impact of river development or restoration on river ecosystem. Conventional methods of physical habitat simulation use the habitat suitability index models and their success depends largely on how well the model reflects monitoring data. One of preferred habitat suitability index models is habitat suitability curves, which are normally constructed based on monitoring data. However, these curves can easily be affected by the subjective opinion of the expert. This study introduces the ANFIS method for predicting the composite suitability index for use in physical habitat simulations. The ANFIS method is a hybrid type of artificial intelligence technique that combines the artificial neural network and fuzzy logic. The method is known to be a powerful approach especially for developing nonlinear relationships between input and output datasets.In this study, the ANFIS method was used to predict the composite suitability index for the physical habitat simulation of a 2.5 km long reach of the Dal River in Korea. Zacco platypus was chosen as the target fish of the study area. A 2D hydraulic simulation was performed, and the hydraulic model was validated by comparing the measured and predicted water surface elevations. The distribution of the composite suitability index predicted by the ANFIS model was compared with that using the habitat suitability curves. The comparisons reveal that the two distributions are similar for various flows. In addition, the distribution of the composite suitability index of the Dal River is computed by the ANFIS method using monitoring data for the other watersheds, namely the Hongcheon River, the Geum River, and the Chogang Stream. The monitoring data for the Chogang Stream, correlation pattern of which was the most similar to that of the Dal River, yielded the distribution of the composite suitability index, which was very close to that obtained using data for the Dal River. This is also supported by the mean absolute percentage error for the difference in the weighted usable areas.     

Linked hypernyms: Enriching DBpedia with Targeted Hypernym Discovery

The Linked Hypernyms Dataset (LHD) provides entities described by Dutch, English and German Wikipedia articles with types in the DBpedia namespace. The types are extracted from the first sentences of Wikipedia articles using Hearst pattern matching over part-of-speech annotated text and disambiguated to DBpedia concepts. The dataset covers 1.3 million RDF type triples from English Wikipedia, out of which 1 million RDF type triples were found not to overlap with DBpedia, and 0.4 million with YAGO2s. There are about 770 thousand German and 650 thousand Dutch Wikipedia entities assigned a novel type, which exceeds the number of entities in the localized DBpedia for the respective language. RDF type triples from the German dataset have been incorporated to the German DBpedia. Quality assessment was performed altogether based on 16.500 human ratings and annotations. For the English dataset, the average accuracy is 0.86, for German 0.77 and for Dutch 0.88. The accuracy of raw plain text hypernyms exceeds 0.90 for all languages. The LHD release described and evaluated in this article targets DBpedia 3.8, LHD version for the DBpedia 3.9 containing approximately 4.5 million RDF type triples is also available.     

Goal-oriented dynamic test generation

ContextMemory safety errors such as buffer overflow vulnerabilities are one of the most serious classes of security threats. Detecting and removing such security errors are important tasks of software testing for improving the quality and reliability of software in practice.ObjectiveThis paper presents a goal-oriented testing approach for effectively and efficiently exploring security vulnerability errors. A goal is a potential safety violation and the testing approach is to automatically generate test inputs to uncover the violation.MethodWe use type inference analysis to diagnose potential safety violations and dynamic symbolic execution to perform test input generation. A major challenge facing dynamic symbolic execution in such application is the combinatorial explosion of the path space. To address this fundamental scalability issue, we employ data dependence analysis to identify a root cause leading to the execution of the goal and propose a path exploration algorithm to guide dynamic symbolic execution for effectively discovering the goal.ResultsTo evaluate the effectiveness of our proposed approach, we conducted experiments against 23 buffer overflow vulnerabilities. We observed a significant improvement of our proposed algorithm over two widely adopted search algorithms. Specifically, our algorithm discovered security vulnerability errors within a matter of a few seconds, whereas the two baseline algorithms failed even after 30 min of testing on a number of test subjects.ConclusionThe experimental results highlight the potential of utilizing data dependence analysis to address the combinatorial path space explosion issue faced by dynamic symbolic execution for effective security testing.     

Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution

In the last 20 years the applicability of Bayesian inference to the system identification of structurally dynamical systems has been helped considerably by the emergence of Markov chain Monte Carlo (MCMC) algorithms – stochastic simulation methods which alleviate the need to evaluate the intractable integrals which often arise during Bayesian analysis. In this paper specific attention is given to the situation where, with the aim of performing Bayesian system identification, one is presented with very large sets of training data. Building on previous work by the author, an MCMC algorithm is presented which, through combing Data Annealing with the concept of ‘highly informative training data’, can be used to analyse large sets of data in a computationally cheap manner. The new algorithm is called Smooth Data Annealing.     

Acoustic emission analysis using Bayesian model selection for damage characterization in ceramic matrix composites

Acoustic emission (AE) during tensile testing of three-dimensional woven SiC/SiC composites was analyzed by a statistical modeling method based on a Bayesian approach to quantitatively evaluate the fracture process. Gaussian mixture models and Weibull mixture models were utilized as candidate models describing the AE time-series data. After fitting AE time-series data to these models with Markov Chain Monte Carlo (MCMC) methods, the model selection was conducted by stochastic complexity. Among the candidate models, the two-component Weibull mixture model was automatically selected. It was confirmed that the component distributions in the two-component Weibull mixture model were corresponding to the evolution of matrix cracking and fiber breakage, respectively. Since the proposed AE analysis method can determine the number of component distributions without the decision of researchers and inspectors, it is expected to be useful for an understanding of the fracture process in newly developed materials and the reliability assessment in service.     

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.