Digital analysis and simulation of nonstationary service loads

This study addresses the growing need for appropriate methods of analysis and simulation of service loads. Specifically, the study attempts to provide a method for analyzing nonstationary service loads that uses available tools of analysis, requires a modest computational facility, and reduces the results to a few parameters. The study also addresses the problem of finding a simulation method that uses these analysis parameters to provide a reproducable service load;A new presentation of the state of art of the methods of analysis and simulation of service loads is given;A nonstationary model is presented that represents the nonstationary process as a composition of two different stationary processes. These stationary processes are assembled according to a probabilistic model (generalized Poisson process) to form the nonstationary process. The idea of parameterizing the description of service loads is presented;In the analysis phase, the method of segmenting the nonstationary sequence and a statistic that estimates each segment population are used to obtain power spectrum estimates of the stationary populations which constitute the nonstationary signal. The method of smoothed periodograms was used as the computational technique of power spectrum estimation. Two different population estimators were used. A method for estimating the statistical parameters of the generalized Poisson process is given. Finally, the coefficients of two fourth order digital filters were used to describe the power spectra of the stationary processes;In the simulation phase of the proposed method, software and hardware methods are presented to generate a white random sequence of numbers, generate the generalized Poisson process, shape the white sequence into a sequence with the required power spectra and finally generate the nonstationary sequence;The proposed method of analysis and simulation of service loads was applied to a typical analog record and a typical digitized sequence of data;The proposed method is seen to be successful in providing a practical way of analyzing a nonstationary signal, presenting the analysis results in terms of few parameters, and generating a nonstationary sequence at a fast sampling rate that can be used by engineers for fatigue life prediction programs or fatigue life testing of components and structures

14-Bromo-12-chloro-2,16-dioxapentacyclohenicosa-3(8),10,12,14-tetraene-7,20-dione

In the title compound, C19H16BrClO4, both the fused xanthene rings and one of the cyclohexane rings adopt envelope conformations, while the other cyclohexane ring is in a chair conformation. In the crystal, molecules are linked by C-H...O hydrogen bonds, forming infinite chains running along [10-1] incorporating R22(16) ring motifs. In addition, C-H...[pi] interactions and weak [pi]-[pi] stacking interactions [centroid-centroid distance = 3.768 (3) Å] help to consolidate the packing

Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults

Dissertation submitted in partial fulfilment of the requirements for the degree of Statistics and Information Management specialized in Risk Analysis and ManagementTo effectively manage credit score analysis, financial institutions instigated techniques and models that are mainly designed for the purpose of improving the process assessing creditworthiness during the credit evaluation process. The foremost objective is to discriminate their clients – borrowers – to fall either in the non-defaulter group, that is more likely to pay their financial obligations, or the defaulter one which has a higher probability of failing to pay their debts. In this paper, we devote to use machine learning models in the prediction of mortgage defaults. This study employs various single classification machine learning methodologies including Logistic Regression, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. To further improve the predictive power, a meta-algorithm ensemble approach – stacking – will be introduced to combine the outputs – probabilities – of the afore mentioned methods. The sample for this study is solely based on the publicly provided dataset by Freddie Mac. By modelling this approach, we achieve an improvement in the model predictability performance. We then compare the performance of each model, and the meta-learner, by plotting the ROC Curve and computing the AUC rate. This study is an extension of various preceding studies that used different techniques to further enhance the model predictivity. Finally, our results are compared with work from different authors.Para gerir com eficácia a análise de risco de crédito, as instituições financeiras desenvolveram técnicas e modelos que foram projetados principalmente para melhorar o processo de avaliação da qualidade de crédito durante o processo de avaliação de crédito. O objetivo final é classifica os seus clientes - tomadores de empréstimos - entre aqueles que tem maior probabilidade de pagar suas obrigações financeiras, e os potenciais incumpridores que têm maior probabilidade de entrar em default. Neste artigo, nos dedicamos a usar modelos de aprendizado de máquina na previsão de defaults de hipoteca. Este estudo emprega várias metodologias de aprendizado de máquina de classificação única, incluindo Regressão Logística, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Para melhorar ainda mais o poder preditivo, a abordagem do conjunto de meta-algoritmos - stacking - será introduzida para combinar as saídas - probabilidades - dos métodos acima mencionados. A amostra deste estudo é baseada exclusivamente no conjunto de dados fornecido publicamente pela Freddie Mac. Ao modelar essa abordagem, alcançamos uma melhoria no desempenho do modelo de previsibilidade. Em seguida, comparamos o desempenho de cada modelo e o meta-aprendiz, plotando a Curva ROC e calculando a taxa de AUC. Este estudo é uma extensão de vários estudos anteriores que usaram diferentes técnicas para melhorar ainda mais o modelo preditivo. Finalmente, nossos resultados são comparados com trabalhos de diferentes autores

CASE STUDY ON DIFFERENT TYPES OF DRILLING BITS AND THE RATE OF PENETRATION

Enhancing the field of oil and gas production, safety and without damaging the reservoir, in today' s era is the top most priority of each and every operator. Accurate accounting of the field sustainable capacity, development plans, and strict compliance with good reservoir management guidelines with excellent planning of field activities including but not limited to; operations and maintenance, projects, right choice of drilling equipments will help minimizing the production losses. In other words, whenever it has been established that a petroleum reservoir probably exists, despite improvements in seismic techniques the only way of confirming the presence of hydrocarbons is to drill an exploration well. Drilling is very expensive, and if hydrocarbons are not found there is no return for the investment

Effective Mechanical Properties of 3D Structural Metamaterials

Recent advances in fabrication techniques have enabled the creation of novel materials with enhanced mechanical properties commonly known as metamaterials. They refer to materials consisting of a unit cell tessellated in three orthogonal directions with dimensions scaling down to the nanoscale. The objective of this research was to describe the effective properties of the octet-truss unit cell at different lattice angles and loading directions. The research is composed of three consecutive parts. The first part addressed the analytical derivation of the continuum-based model while including the lattice angle. The second part demonstrated the impact of the lattice angle on its effective properties, namely relative density, effective stiffness, and effective strength. Finally, potential in lattice structure optimization is demonstrated through an experimental study. This work addressed optimizing the structural configuration of the octet-truss, which when combined with favorable size effects, would unlock the full potential of mechanical metamaterials as load-bearing structures

Revisiting Non-Convexity in Topology Optimization of Compliance Minimization Problems

Purpose: This is an attempt to better bridge the gap between the mathematical and the engineering/physical aspects of the topic. We trace the different sources of non-convexification in the context of topology optimization problems starting from domain discretization, passing through penalization for discreteness and effects of filtering methods, and end with a note on continuation methods. Design/Methodology/Approach: Starting from the global optimum of the compliance minimization problem, we employ analytical tools to investigate how intermediate density penalization affects the convexity of the problem, the potential penalization-like effects of various filtering techniques, how continuation methods can be used to approach the global optimum, and how the initial guess has some weight in determining the final optimum. Findings: The non-convexification effects of the penalization of intermediate density elements simply overshadows any other type of non-convexification introduced into the problem, mainly due to its severity and locality. Continuation methods are strongly recommended to overcome the problem of local minima, albeit its step and convergence criteria are left to the user depending on the type of application. Originality/Value: In this article, we present a comprehensive treatment of the sources of non-convexity in density-based topology optimization problems, with a focus on linear elastic compliance minimization. We put special emphasis on the potential penalization-like effects of various filtering techniques through a detailed mathematical treatment

Sedimentology of the Swift Formation (Jurassic) in the Little Rocky Mountains of Montana

The marine strata of the Swift Formation (Upper Callovian-Oxfordian) are widely distributed and well exposed in the Little Rocky Mountains of north-central Montana. The contact between the Swift and the underlying marine Rierdon Formation is sharp, whereas the upper contact with the non-marine Morrison Formation is gradational. The Swift Formation is about 30 m to 50 m thick and is divided into two members: a lower shale and an upper sandstone. Detailed sedimentological analysis defined six facies; three in each member. The shale member contains a conglomerate facies (Facies A), a shale-siltstone facies (Facies B), and a bioclastic limestone facies (Facies C). The facies of the sandstone member comprise a sandstone­-siltstone-shale facies (Facies D), a cross-bedded sandstone facies (Facies E), and a limestone facies (Facies F). The Swift Formation forms a coarsening-upward sequence from mud to sand-silt-mud intercalations to sand, which has been interpreted by other people as a progradational sequence across a shelf. The Rierdon-Swift contact is a disconformity spanning three ammonite zones. The whole section of the Swift Formation is considered to be a shallow marine shelf deposit that formed in the course of a transgressive-regressive episode during Late Callovian-Oxfordian time. Facies A was produced by the reworking of sediment by waves in a near­shore setting during the early stage of the transgressive sea. Facies B was deposited from suspension in relatively deep, open, marine waters during the maximum expansion of the Oxfordian sea. Facies C was formed by the winnowing effect of frequent storm-generated waves, reworking the muddy platform deposits of Facies B. Facies D and E form a continuous regressive sequence that was deposited in a storm-dominated, lower shoreface environment. Facies F was deposited in a shallow, relatively protected setting. The depositional model proposed for the Swift Formation in the study area is one of a shifting pattern of sedimentation in a shallow marine setting, where inner shelf deposits passed transitionally into lower shoreface deposits; these, in turn, gave way to middle-to-upper shoreface sediments. The sea-level changes during the deposition of the Swift Formation were as a result of mainly local and regional tectonism; eustatic factors, if any, were minor