Academic literature on the topic 'Dynamic cumulative sum'

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection of process faults. Even though fault detection algorithms are designed and implemented for quickly detecting incidents, most these algorithms do not have an optimal property in terms of detection delay with respect to false alarm rate. Based on this optimization property, a real-time system for detecting changes in dynamic systems is designed in this paper. This work is motivated by combining two fault detection (FD) methods; a simplified procedure of the incident detection problem is formulated by using the combination of the Artificial Neural Networks (ANN) and the cumulative sum (CUSUM) or Page-Hinkley test. It is intended to reveal any drift from the normal behavior of the process. The process behavior under its normal operating conditions is established by a reliable model. In order to obtain this reliable model for the process dynamics, the black-box identification by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model is chosen in this study. The purpose is to develop and test the fault detection method on a real incident data, to detect the change presence, and pinpoint the moment it occurred. The experimental results demonstrate the robustness of the FD method.

The occurrence of one risk may exacerbate other risks or portfolios of risks due to their highly complex interrelated structures and existing interactions. Hence, the cumulative impact of a chain of risks may be greater than the sum of their individual impacts. Commonly practiced risk analysis approaches do not account for these interactions and face deficiency in providing reliable information regarding the actual impact of the identified risks. This paper presents a new approach to construction risk analysis where the interrelated structure of risks and their interactions have been modeled through the governing feedback loops. The proposed methodology is a system dynamics-based approach for risk analysis and assessment. The full impact of a risk or a set of risks may efficiently be modeled, simulated, and quantified in terms of time and cost by the proposed object-oriented simulation methodology. To evaluate the performance of the proposed methodology, it has been incorporated into a bridge construction project. The interrelated structures of the identified risks have been modeled and their cumulative consequences simulated and quantified as an illustrative example.

We analyze a game in which a group of agents exerts costly effort over time to make progress on a project. The project is completed once the cumulative effort reaches a prespecified threshold, at which point it generates a lump-sum payoff. We characterize a budget-balanced mechanism that induces each agent to exert the first-best effort level as the outcome of a Markov perfect equilibrium, thus eliminating the free-rider problem. We also show how our mechanism can be adapted to other dynamic games with externalities, such as strategic experimentation and the dynamic extraction of a common resource. (JEL C73, D62, D82, Q31)

A usual approach to monitoring an autocorrelated process is to apply a control chart to the process residuals. In this paper, we study the statistical behavior of the residuals of a controlled second-order autoregressive (AR(2)) cutting process when a special-cause shift occurs to the process mean. Shewhart, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are applied to the residuals to monitor the cutting process. Formulas, integral equations and recursive procedures for computing the average run lengths (ARLs) of the charts are derived. Numerical results are presented and the relative performance of the charts is investigated.

A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.

Remittances have become a major source of funding in developing countries. A time series analysis was conducted by taking the 25 years data of economic growth as a dependent variable while remittance, money supply and Inflation as independent variables form the annual report of Economic survey of Nepal (2020/21). The autoregressive distributive lag (ARDL) was used to study the relationship among the variables. The trend of economic growth and total percentage of economic growth is in increasing order. The relation between then was found as LN(Economic Growth) =8.0740 +0.220*LNREM + 0.3123 *LNM2-0.0802*LNINF. The stability test of long-run coefficient including short run was studied using ARDL model and are confirmed using Cumulative Sum chart (CUSUM) and cumulative sum of squares test (CUSUMSQ) test. Finding shows that there is long run positive relationship between remittance, money supply, inflation on economic growth of Nepal. From the analysis it is clear that coefficients of error correction are highly significant this indicate that adjustment to longterm equilibrium in the dynamic model and ARDL model showed all the independent variables had positive impact on economic growth of Nepal.

The aim of this study is to clarify the damage fracture process using acoustic emission technique and construct relationships between acoustic emission parameters and static, dynamic properties of reinforced concrete beam under four-point bending. Laboratory experiments were performed on two reinforced concrete beams with same length 250 cm. Static parameters including crack and displacement patterns with load level were observed and analyzed. Acoustic emission parameters of hit, count, amplitude, energy, duration, index of damage, and b-value with the increase in damage were demonstrated and corresponded to observed crack status of structure, which realized the damage process classification and mechanism characterization. Correlations of acoustic emission parameters and cumulative crack width with the first-order natural frequency were obtained to clarify their relevance. The results revealed that as the level of damage increased, the values of cumulative acoustic emission parameters increased, which could classify the damage fracture process into four stages. And the results coincided with the observed crack and displacement patterns of reinforced concrete beam. Sum of maximum width for observed cracks presented favorable quadric relations with acoustic emission parameters. Index of damage and b-value analyses (classical and Gaussian filtered ones) demonstrated the crack characteristic at each load step, which were in accordance with the observed crack patterns. Change rate of the first-order natural frequency presented favorable cubic relationships with acoustic emission energy and hit, while linear and negative relations with sum of crack widths. The results obtained in this study can be used for damage fracture characterization and health monitoring of reinforced concrete structures subjected to flexural load.

Purpose: The study examines how agricultural exports boost the economic growth of Pakistan in the long run and suggest policy implications during 1995-2018 using time series data. Methodology: Principal Component Analysis is used to construct an agricultural export index consisting of rice, raw cotton, fruits, and vegetables as variables. This quantitative study checked the structural stability of the model with cumulative-sum & cumulative-sum of the square. Rolling window analysis highlights the long-run yearly effect of the coefficient of the model. The result of variance decomposition method proof bidirectional causality where robust result proof using Fully modified ordinary least square and Dynamic ordinary least square techniques. Unit root at first difference proof stationery whereas cointegration has a long-run relationship between agricultural export and economic growth. Main Finding: The statistical estimation proofs the positive long-run association of agricultural exports with economic growth. Results explored a 26 percent increase in the economy of Pakistan by exporting agricultural goods. Application of this Study: This study helps to develop the economies if they face problems of low agricultural productivity. The agricultural export is sensitive to domestic indicators, and domestic policy can promote agricultural export, and create new potential markets. The originality of the Study: The study is suggested the agriculture techniques and their performance in developing economies.

In this paper, we propose a distinctive pilot power allocation algorithm to maximize the sum rate in a multi-cell multi-user massive multiple-input multiple-output (MIMO) system. The algorithm optimizes pilot powers by polarizing the corresponding SINR values. In order to polarize SINRs, the difference between average SINR per cell and individual SINR is calculated for each user of the whole cells. The exponential form of the difference is used in the calculations of the weights for power allocation. New power values are obtained in proportion to these weights. Therefore, the power budget is utilized more efficiently thanks to these optimized power values. The efficiency of the algorithm is measured using the cumulative average SINR of the simulation system. Furthermore, equal pilot power allocation (EPPA) and water-filling pilot power allocation (WF-PPA) schemes are also implemented to compare the performances under the same simulation environments. A vast number of simulations results prove that our proposed heuristic approach is more efficient than EPPA and WF-PPA methods.

Abstract. Mass change over Greenland can be caused by either changes in the glacial dynamic mass balance (DMB) or the surface mass balance (SMB). The GRACE satellite gravity mission cannot directly separate the two physical causes because it measures the sum of the entire mass column with limited spatial resolution. We demonstrate one theoretical way to indirectly separate cumulative SMB from DMB with GRACE, using a least squares inversion technique with knowledge of the location of the glaciers. However, we find that the limited 60 × 60 spherical harmonic representation of current GRACE data does not provide sufficient resolution to adequately accomplish the task. We determine that at a maximum degree/order of 90 × 90 or above, a noise-free gravity measurement could theoretically separate the SMB from DMB signals. However, current GRACE satellite errors are too large at present to separate the signals. A noise reduction of a factor of 10 at a resolution of 90 × 90 would provide the accuracy needed for the interannual cumulative SMB and DMB to be accurately separated.

The risk-adjusted Bernoulli cumulative sum (CUSUM) chart developed by Steiner et al. (2000) is an increasingly popular tool for monitoring clinical and surgical performance. In practice, however, use of a fixed control limit for the chart leads to quite variable in-control average run length (ARL) performance for patient populations with different risk score distributions. To overcome this problem, the simulation-based dynamic probability control limits (DPCLs) patient-by-patient for the risk-adjusted Bernoulli CUSUM charts is determined in this study. By maintaining the probability of a false alarm at a constant level conditional on no false alarm for previous observations, the risk-adjusted CUSUM charts with DPCLs have consistent in-control performance at the desired level with approximately geometrically distributed run lengths. Simulation results demonstrate that the proposed method does not rely on any information or assumptions about the patients' risk distributions. The use of DPCLs for risk-adjusted Bernoulli CUSUM charts allows each chart to be designed for the corresponding particular sequence of patients for a surgeon or hospital. The effect of estimation error on performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is also examined. Our simulation results show that the in-control performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is affected by the estimation error. The most influential factors are the specified desired in-control average run length, the Phase I sample size and the overall adverse event rate. However, the effect of estimation error is uniformly smaller for the risk-adjusted Bernoulli CUSUM chart with DPCLs than for the corresponding chart with a constant control limit under various realistic scenarios. In addition, there is a substantial reduction in the standard deviation of the in-control run length when DPCLs are used. Therefore, use of DPCLs has yet another advantage when designing a risk-adjusted Bernoulli CUSUM chart. These researches are results of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech). Moreover, DPCLs are adapted to design the risk-adjusted CUSUM charts for multiresponses developed by Tang et al. (2015). It is shown that the in-control performance of the charts with DPCLs can be controlled for different patient populations because these limits are determined for each specific sequence of patients. Thus, the risk-adjusted CUSUM chart for multiresponses with DPCLs is more practical and should be applied to effectively monitor surgical performance by hospitals and healthcare practitioners. This research is a result of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech) and Mr. Justin Loda (Department of Statistics, Virginia Tech).
Ph. D.

La perte de puissance d'un générateur photovoltaïque (GPV) est sans conteste due à l'apparition d'un certain nombre d'anomalies liées à la fabrication, à la production ou à l'environnement, engendrant des défaillances dans son bon fonctionnement. A partir d'un modèle réaliste, assez proche du fonctionnement réel et capable de prendre en compte l'effet d'avalanche d'une jonction PN transmise à l'ensemble du GPV, nous avons montré à suffisance, la perte de performances d'un générateur PV et la nécessité d'avoir une méthode de diagnostic pour l'aide à la maintenance afin de ne pas subir les effets des défauts.Deux méthodes de diagnostic ont été appliquées à ce GPV, l'une portant sur la détection et la localisation des défauts capteurs, et l'autre sur la détection et la localisation des défauts systèmes. Le choix particulier de ces deux techniques de diagnostic, qui ne ciblent pas les mêmes types de défauts, réside dans la nature complexe du modèle du processus industriel soumis à l'étude. Les performances obtenues avec la méthode des relations de redondance analytique (RRA) basée sur le principe de l'espace de parité appliqué au point de fonctionnement maximal, sont très pertinentes. Par la méthode de l'intelligence artificielle (IA) basée sur le principe des réseaux de neurones artificiels (RNA), nous avons expérimenté deux méthodes de classification pour la détection et le diagnostic des défauts systèmes. Si la détectabilité est prouvée avec nos différentes configurations sans possibilité de situer l'origine et la cause dans la première partie de la classification, nous arrivons grâce à un faisceau d'indices à situer l'origine ou la cause grâce à la classification pour le diagnostic.La réalisation de deux prototypes d'acquisition temps réel est faite sur le principe de l'Internet industriel des objets (IIoT). Le premier permet uniquement l'acquisition et la sauvegarde des données sur une carte SD. Le second prototype plus évolué, permet la transmission en temps réel par Wifi à un serveur web et vise la réalisation à long terme d'une plateforme de surveillance en temps réel. Les deux prototypes produisent des données qui sont utilisées pour alimenter les deux méthodes de diagnostic. Les résultats obtenus avec des données réelles sont compatibles avec ceux obtenus en phase de simulation. Les conclusions de ce diagnostic permettront une meilleure efficacité dans les opérations de maintenance proactive
The loss of power of a photovoltaic generator (PVG) is undoubtedly due to the appearance of a certain number of anomalies linked to manufacturing, production or the environment and causing failures in its proper functioning. From a realistic model, quite close to real operation and able to take into account the avalanche effect of a PN junction transmitted to the entire PVG, we have sufficiently shown the loss of performance of a PV generator and the need to have a diagnostic method for maintenance assistance in order not to suffer the effects of faults.Two diagnostic methods were applied to this PVG, one relating to the detection and localization of sensor faults, and the other to the detection and localization of system faults. The particular choice of these two diagnostic techniques, which do not target the same types of faults, lies in the complex nature of the model of the industrial process subjected to study. The performances obtained with the analytical redundancy relations (ARR), method based on the principle of parity space applied to the maximum operating point are very relevant. Using the artificial intelligence (AI), method based on the principle of artificial neural networks (ANN), we experimented with two classification methods for the detection and diagnosis of system faults. If detectability is proven with our different configurations without the possibility of locating the origin and the cause in the first part of the classification, we arrive thanks to a bundle of clues to locate the origin or the cause thanks to the classification for the diagnostic.The production of two real-time acquisition prototypes is based on the principle of the Industrial Internet of Things (IIoT). The first only allows the acquisition and saving of data on an SD card. The second, and more advanced prototype, allows real-time transmission via WiFi to a web server and aims to create a real-time monitoring platform in the long term. Both prototypes produce data that is used to power both diagnostic methods. The results obtained with real data are compatible with those obtained in the simulation phase. The conclusions of this diagnosis will enable greater efficiency in proactive maintenance operations

The origins and expression of addiction are best understood within the context of developmental processes and dynamic systems organization and change. For some individuals, these dynamic processes lead to risk cumulative or cascade effects that embody adverse childhood experiences that exacerbate risk; predict early onset of drinking, smoking, or other substance use; and often lead to a substance use disorder (SUD) during the transitions to adolescence and emergent adulthood. In other cases, protective factors within or outside of the individual’s immediate family enable embodiment of normative stress regulatory systems and neural networks that support resilience and prevention of SUDs. A case study is provided to illustrate these processes and principles of the organization of addictive behavior. Finally, a model of risk to resilience captures the flow of development and the extent to which individual-experience relationships contribute to risk and resilience.

This chapter focuses on the woman of Tekoa (2 Sam. 14) and argues that her speech points out a pattern of David’s previous misconducts (profiled in ANE literature as the royal deviance principle) that endangered his family and YHWH’s people. Using the ominous phraseology in v. 14 (we are like water spilled on the ground), and reinforcing its tie to ancient maledictions, the woman parades before David a horrid demise of a nation due to its monarch’s failure to rectify inner-dynastic feuds. By placing her curse-related imagery into a lament-based petition, the woman protests its fulfilment in the ensuing chapters in the Absalom saga. Since the entirety of 2 Sam. 14 is supplemented with grief-related artifices, the woman’s speech functions as an act of mourning for the cumulative death toll of God’s people under David’s kingship.

This book examines the theoretical and conceptual foundation of effective modern intelligence collection—the strategies required to support intelligence analysis of the modern, complex operational environments of today's military conflicts or competitive civilian situations such as business. Just as the old rules of conventional warfare and intelligence analysis do not apply fully in the 21st-century environment, neither does the traditional methodology of collecting intelligence on these elusive, adapting foes operating as complex adaptive systems (CAS)—adversaries that excel in today's complex contexts. Intelligence Collection: How To Plan and Execute Intelligence Collection In Complex Environments proposes substantive improvements in the way the U.S. national security system collects intelligence and supports intelligence analysis. The work draws on the groundbreaking work of a diverge group of theorists ranging from Carl von Clausewitz and Sun Tzu to M. Mitchell Waldrop, General David Petraeus, and Orson Scott Card, communicating a unifying theory and ontology of thought for how America's intelligence collection professionals must learn to collect data as our country faces elusive, determined, and smart adversaries in nonlinear, dynamic environments. The new ideas presented will help the nation's intelligence collection specialists to amass a formidable, cumulative intelligence power, regardless of the level of war or the type of operational environment.

Climate models report that the environmental changes resulting from excess CO₂ and heat absorption by the ocean already reach many deep-ocean margins, basins, and seas. Decadal monitoring programmes have confirmed significant warming and deoxygenation trends down to the abyss, which combine with CO₂-enriched, more corrosive conditions. Although the resolution of current models does not account for the typical mesoscale seafloor heterogeneity, cumulative impacts on biodiversity and productivity hotpots are anticipated. The growing interest in deep-sea resource exploitation has shed light on the lack of knowledge about current climate-driven disturbance and potential cumulative threats at great depth. Assessing the sensitivity of deep-sea ecosystems to temperature increase combined with oxygen and resource decline is emerging as a growing challenge. The natural patchiness of deep-seafloor habitats and associated deep-sea diversity patterns inform about environmental constraints over space, but the temporal dynamics of these systems is not well known. Experimental studies are required to assess the physiological limits and explore the adaptation and acclimation potential of foundation species exposed to various forms of abiotic stress. The case of cold-water corals is particularly illustrative of the potential synergistic effects of climate stressors, including warming, acidification, deoxygation, and reduced food availability. Addressing ecosystem vulnerability also requires dedicated monitoring efforts to identify the current and future drivers of climate-change impacts on deep-sea habitats. United Nations policy objectives for protected high-sea biodiversity and healthy oceans and seas drive the momentum towards better climate-change forecasting over the ocean-depth range and related integrated observing strategies.

Abstract Entrepreneurship policy is by nature multi-level and multi-dimensional. Contextual factors—geographical and institutional—influence the policy’s operations and cumulative impacts. This chapter seeks to better understand the interdependence and interplay of the policy across different levels of government, and its evolution over time, as an integral part of innovation and entrepreneurial ecosystems. We look at the case of Japan, examining small and medium-sized enterprise (SME) policies introduced at national and sub-national levels since the mid-1990s. Our examination reveals three related policy turns—entrepreneurial, networking, and local ecosystem—in the country’s SME policies, which constitute the evolutionary dynamics of its innovation and entrepreneurial ecosystems. The chapter also assesses the effects of those policy measures and programmes, showing varied contributions of subsidies, an overall shift of policy instruments from subsidies to networking supports and the latter’s greater effectiveness, and significant effects of a recent place-based start-up support programme. The chapter concludes by identifying opportunities and challenges, showing the diversity and complexity of recent place-based policymaking for the revitalization of local economies.

In this paper, the finite element analysis method is used to simulate the dynamic propagation process of Lamb waves in the aluminum alloy plate, and the modulation effect of damage on the received signal is analyzed. By analyzing the dispersion curve of Lamb waves propagating in the aluminum alloy plate, a narrow-band Hanning window modulated sine wave with the center frequency of 150 KHz and the period of 5 is selected as exciting signal, based on the delay-and-sum imaging algorithm, using a honeycomb sensor array composed of six piezoelectric sensors, the simulated damage of additional heavy weight in aluminum alloy plate with size of 600mm 600mm 2mm was identified. The positioning errors of the three experiments were 8.06 mm, 13.34 mm and 14 mm. The experimental results show that the delay-and-sum imaging algorithm can accurately locate the damage in the aluminum alloy plate.

Abstract The gas-liquid two-phase flow behaviors are always associated with its dynamic void fraction, such as flow resistance, heat transfer coefficient, phase distribution, critical heat flux etc. As regard to the commercial PWR and BWR, rod bundles are the typical geometry, which contains many sub-channels for coolant flowing. In present study, the sub-channel void fraction was measured in 5 × 5 rod bundles with the sub-channel impedance void meter consisting of 12 strip electrodes. Based on the measured void fraction in different sub-channels, the void fraction dynamics, PDF (probability distribution function) and CDF (cumulative distribution function) curves were analyzed to make clear the effect of superficial gas and liquid velocity, flow development and casing tube. The empirical correlation for PDF of dynamic sub-channel void fraction has been developed, which showed good fitness with PDF and CDF curves and satisfying accuracy of averaged void fraction.

Abstract This work aims to create an approximation of the reservoir numerical model using smart proxy modeling (SPM) to be used for production optimization. The constructed SPM in this work is further improved in different steps to increase its accuracy and efficiency compared to the existing literature. These steps include sequential sampling, average feature ranking, convolutional neural network (CNN) deep learning modeling, and feature engineering. SPM is a novel methodology that generates results faster than numerical simulators. SPM decouples the mathematical equations of the problem into a numeric dataset and trains a statistical/AI-driven model on the dataset. Major SPM construction steps are: objective, input, and output selection, sampling, running numerical model, extracting new static and dynamic parameters, forming a new dataset, performing feature selection, training and validating the underlying model, and employing the SPM. Unlike traditional proxy modeling, SPM implements feature engineering techniques that generate new static/dynamic parameters. The extracted parameters help to capture hidden patterns within the dataset, eventually increasing SPMs’ accuracy. SPM can either be constructed to predict the grids’ characteristics, called grid-based SPM, or to predict the wells' fluid rates, called well-based SPM. In this work, the well-based SPM is constructed to duplicate the Volve offshore field production results undergoing waterflooding. We used Latin hypercube sampling coupled with genetic algorithm (GA) in the sampling step. The designed parameters to perform sampling are the individual liquid rate of the producers, and the output is the individual well's cumulative oil production. In the formed dataset, various extracted parameters relating to the wells are prepared, such as well types, indexes, trajectories, and cumulative oil production. Furthermore, a grid-based SPM is constructed in parallel to the well-based SPM. At each timestep of the prediction, dynamic parameters relating to grids (in this case: grids’ pressure/saturations) are transferred to the existing well-based dataset. This technique helps the well-based SPM further increase in accuracy by finding new patterns within the dataset. We implement an average of 23 different models to rank, and perform the feature selection process. Finally, the CNN model is trained on the dataset, and is coupled with two derivative-free optimizers of GA and particle swarm optimizer to maximize the oil production over the selected time period. Sequential sampling used in this work is a novel technique to construct the SPM with the lowest number of numerical model executions. It provides an efficient workflow to perform sampling, thereby saving time instead of repeating the whole SPM construction steps. The average feature ranking implemented in this paper provides the best prioritization of input parameters. It provides a confident ranking for the feature selection step. Finally, the underlying CNN model is compared to the prediction accuracy of the ANN model.

Traditionally, the flow regime in two phase flow in rod bundles are considered in a global sense by the visualization. However, a sub-channel flow regime is required to understand and model the two phase flow structures in rod bundles. In this work, a sub-channel impedance meter was designed to get the dynamic feature in the sub-channels, which was applied to identify the sub-channel flow regime by the fast search and finding peaks of the cumulative probability distribution functions (CPDFs) objectively. In the present study, five flow regimes, namely bubbly flow, quasi-cap bubbly flow, quasi-slug flow, cap-turbulent flow and churn-turbulent flow were defined and recognized. The sub-channel flow regimes at the same cross section were compared to each other, which show similar feature with the local flow regimes in pipe. It is possible to identify nine different global flow regime configurations by combining the corner, side and inner sub-channel flow regime at the same cross section, which was drawn in the 2D sub-channel flow regime.

Time-dependent reliability is the probability that a system will perform its intended function successfully for a specified time. Unless many and often unrealistic assumptions are made, the accuracy and efficiency of time-dependent reliability estimation are major issues which may limit its practicality. Monte Carlo simulation (MCS) is accurate and easy to use but it is computationally prohibitive for high dimensional, long duration, time-dependent (dynamic) systems with a low failure probability. This work addresses systems with random parameters excited by stochastic processes. Their response is calculated by time integrating a set of differential equations at discrete times. The limit state functions are therefore, explicit in time and depend on time-invariant random variables and time-dependent stochastic processes. We present an improved subset simulation with splitting approach by partitioning the original high dimensional random process into a series of correlated, short duration, low dimensional random processes. Subset simulation reduces the computational cost by introducing appropriate intermediate failure sub-domains to express the low failure probability as a product of larger conditional failure probabilities. Splitting is an efficient sampling method to estimate the conditional probabilities. The proposed subset simulation with splitting not only estimates the time-dependent probability of failure at a given time but also estimates the cumulative distribution function up to that time with approximately the same cost. A vibration example involving a vehicle on a stochastic road demonstrates the advantages of the proposed approach.

Abstract Digital fluid power is a growing field which utilizes electronics and advanced controls to improve efficiencies, energy savings, and productivity in fluid power systems. Often relying on on/off high-speed switching techniques, digital hydraulics relies heavily on the performance of valves, where an error in the valve performance could lead to a major drop in the efficiency and performance of the entire system. Specifically, digital pump/motors are sensitive to valve delay and transition timing which negatively impacts their performance and condition with time. One approach to assessing the performance and efficiency of digital pump/motors is via monitoring its inlet (low) and outlet (high) pressure time-series. Real-time condition monitoring also supports preventive maintenance and provides a better understanding of the dynamics of pump/motors. For condition monitoring, Statistical Process Control (SPC) charts are often designed to detect shift changes in time-series. This paper proposes to construct two cumulative sum (CUSUM) control charts for fast real-time shift detection in the high and low pressure time-series of digital pump/motors. The proposed method will be able to actively detect common misbehaviors in the valves utilized in the digital pump/motor. The model have been successfully tested on a three-piston inline digital pump/motor, but this monitoring technique can be modified and implemented on other digital technology classes where valve performance is key in the success of the system.

Abstract Vehicle type approval drive cycles have become a mainstay for benchmarking performance of engines in the development cycle. However, they are typically long and costly to test, with questions of repeatability and real-world relevance. With the move to Real Driving Emission (RDE) style testing, complete foreknowledge of the cycle is no longer guaranteed. This paper presents a methodology for identifying key behaviours (or information rich regions) from the current worldwide harmonised light-duty test cycle (WLTC) type approval test using a moving 2-minute window approach. Three techniques for pattern recognition are presented and applied to data collected from a modern Gasoline Turbocharged Direct Injection (GTDI) engine, run through the WLTC. The techniques examine different points in the process, with the first examining response data, and working backwards to the original vehicle speed cycle specification. The first two techniques, Intensity Ratio (IR) for cumulative responses and Energy Residency (ER) for engine inputs, are newly developed in this paper. The final technique, Dynamic Time Warping (DTW) is a new application of an existing tool to the subject of vehicle drive cycles. The techniques are examined in isolation and discussed, before being brought together to identify commonly agreed information rich sub-cycle candidates that best represent the parent cycle. The techniques make use of a combination of time windowing, signal derivative and peak analysis methods. The two-minute window is chosen based on the length of an existing sub-cycle that was identified as part of earlier work, and this method is also described and validated in this paper. One sub-cycle identified by the approach represents a duration reduction of 93% to a containable 2-minute transient. This segment accounts for 15% of the overall WLTC fuel used. A discussion of the techniques and their applications is presented to motivate future work in this area.

Increasing complexity of aerospace structures facilitates a growing need for structural health monitoring (SHM) systems capable of real-time active damage detection. A variety of sensing approaches have been demonstrated using embedded ultrasonic sensors such as piezoelectric wafer active sensors (PWAS) and magneto-elastic active sensors (MEAS). Common methodologies consider wave propagation (pitch-catch or pulse-echo) and standing wave (vibration or impedance) techniques with damage detection capabilities dependent upon structural geometry, material characteristics, distance to damage and damage size/orientation. While recent studies have employed damage detection and classification approaches that are dependent on cumulative statistics, this study explores the contribution of sensor parameters and experimental setup variability on the damage detection scheme. The impact of variability in PWAS and MEAS are considered on sensor use in ultrasonic and magneto-mechanical impedance damage detection. In order to isolate sensor parameters, measurements were conducted with PWAS in free-free boundary conditions. Variability of PWAS parameters was evaluated by measuring the sensors impedance response. An analytical model of PWAS was used to estimate sensor parameters and to determine their variability. Additionally, experiments using MEAS were performed that demonstrate variation of magneto-mechanical impedance during structural dynamic tests. From these experiments the importance of sensor setup is discussed and its contribution into the overall detection scheme is explored.

3D physiologic geometry of St. Jude Medical (SJM) valve after implantation was simulated with non-Newtonian two-phase blood model. The simulation used the unsteady Reynolds averaged Navier-Stokes (URANS) approach and the Wilcox k-ω turbulent model. Platelet stress accumulation and the resulting platelet damage were calculated from the results. Thrombogenic potential of two bileaflet MHV geometries was conducted using fluid-structure interaction (FSI) computation. Two commercially available valve geometries, SJM and ATS, which differ mostly in their hinge design, were simulated in a straight geometry with sudden expansion downstream of the valve. The thrombogenic potential of the two valves was based on computed wall shear stresses on the leaflets and cumulative shear stress on multiple particles released during forward and reverse flow phases. Platelet stress accumulation along pertinent trajectories from the FSI studies indicated that the SJM valve has a higher thrombogenic potential then the ATS valve. Flow patterns generated by the valve are conducive to platelet activation provide optimal conditions for activated platelets to interact with each other and form aggregates are hypothesized to be the source of thromboemboli formation, increasing the risk for cardioembolic stroke. The new damage model developed was utilized to estimate the effects of repeated passages and platelet senescence on this thrombogenic potential. Flow and pressure effects on a cell like a platelet can be well represented by a continuum mechanics model down to the order of the μm level. However, the molecular effects of adhesion/aggregation bonds are on the order of nm. Thus we also adopt a discrete particles dynamics (DPD) approach in which the macroscopic model provides information about the flow induced stresses that may activate blood cellular constituents. This multiscale modeling approach concentrates on flow regions in prosthetic devices like MHVs and cardiovascular pathologies that have a high propensity to activate platelets and form aggregates. Preliminary simulations of blood flow in simple geometries using this approach, which widely departs from the traditional continuum approach, is successful in generating viscous blood flow velocity distributions in these geometries.

Abstract The potential for a gas-in-riser situation to become uncontrollable by the rapid displacement of mud out of the riser is extremely high if the riser-top is left open. The unloading can be catastrophic in synthetic-based mud (SBM) or oil-based mud (OBM) when the gas remains dissolved and undetected till pressure reduction causes sudden desorption of dissolved gas closer to the surface. This work demonstrates, investigates, and provides insights into the riser gas unloading phenomena with the help of full-scale gas migration experiments. A 5200 ft deep vertical test well (9 5/8" x 2 7/8" casing/drill-pipe) at LSU instrumented with 4 down-hole PT gauges was used for the tests. Tests were carried out in water, and SBM. Each test started by injecting a fixed volume of nitrogen gas (5 to 15 bbl) at a low (0.3 bbl/min) or high flow rate (4 bbl/min) from the bottom of the annulus while keeping the annulus open. After the influx, the annulus was either closed at the surface to study the effects of gas migration under shut-in conditions or left open to study the effects of gas migration under open-top annulus conditions. The rate of pit-gain reduced during the low-void-fraction gas tests in water, and SBM-filled-annulus when gas influx stopped (closing of subsea BOP). However, for the high-void-fraction test in SBM, the pit-gain stopped once influx stopped and remained negative from 6.5 minutes to 35.5 minutes due to a reduction in mud level caused by the dissolution of gas in SBM. The pit gain later resumed and continued to increase. Keeping the annulus open resulted in a rapid exponential increase in pit-gain as the gas-front neared the surface requiring an immediate shut-in of the annulus to avoid unsafe rapid discharge. The final estimated outflow rate based on cumulative pit-gain (Coriolis) was 160gpm for the high-void-fraction test in SBM. Pressure, and differential pressure data from pairs of gauges were used to make real-time decisions during the tests and to estimate the location and migration velocities of gas-front and tail. The model developed for analysis and comparison of test results in water is used here to explain the behavior of gas migration under open-top conditions. A thorough investigation with the help of gauge data and pit gain has explicated our understanding of gas migration behavior and its effect on the dynamics of gas-liquid equilibrium from influx to impending unloading situation. The interesting results from the tests are extremely useful in explaining the dangers of using open-top annulus on rigs.

1. Macroeconomic Summary In December, headline inflation (13.1%) and the average of the core inflation measures (10.3%) continued to trend upward, posting higher rates than those estimated by the Central Bank's technical staff and surpassing the market average. Inflation expectations for all terms exceeded the 3.0% target. In that month, every major group in the Consumer Price Index (CPI) registered higher-than-estimated increases, and the diffusion indicators continued to show generalized price hikes. Accumulated exchange rate pressures on prices, indexation to high inflation rates, and several food supply shocks would explain, in part, the acceleration in inflation. All of this is in a context of significant surplus demand, a tight labor market, and inflation expectations at different terms that exceed the 3.0% target. Compared to the October edition of the Monetary Policy Report, the forecast path for headline and core inflation (excluding food and regulated items: EFR) increased (Graphs 1.1 and 1.2), reflecting heightened accumulated exchange rate pressures, price indexation to a higher inflation rate (CPI and the producer price index: PPI), and the rise in labor costs attributed to a larger-than-estimated adjustment in the minimum wage. Nevertheless, headline inflation is expected to begin to ease by early 2023, although from a higher level than had been estimated in October. This would be supported initially by the slowdown forecast for the food CPI due to a high base of comparison, the end anticipated for the shocks that have affected the prices of these products, and the estimated improvement in external and domestic supply in this sector. In turn, the deterioration in real household income because of high inflation and the end of the effects of pent-up demand, plus tighter external and domestic financial conditions would contribute to diluting surplus demand in 2023 and reducing inflation. By the end of 2023, both headline and core (EFR) inflation would reach 8.7% and would be 3.5% and 3.8%, respectively, by December 2024. These forecasts are subject to a great deal of uncertainty, especially concerning the future behavior of international financial conditions, the evolution of the exchange rate, the pace of adjustment in domestic demand, the extent of indexation of nominal contracts, and the decisions taken regarding the domestic price of fuel and electricity. In the third quarter, economic activity surprised again on the upside and the growth projection for 2022 rose to 8.0% (previously 7.9%). However, it declined to 0.2% for 2023 (previously 0.5%). With this, surplus demand continues to be significant and is still expected to weaken during the current year. Annual economic growth in the third quarter (7.1 % SCA)1 was higher than estimated in October (6.4 % SCA), given stronger domestic demand specifically because of higher-than-expected investment. Private consumption fell from the high level witnessed a quarter earlier and net exports registered a more negative contribution than anticipated. For the fourth quarter, economic activity indicators suggest that gross domestic product (GDP) would have remained high and at a level similar to that observed in the third quarter, with an annual variation of 4.1%. Domestic demand would have slowed in annual terms, although at levels that would have remained above those for output, mainly because of considerable private consumption. Investment would have declined slightly to a value like the average observed in 2019. The real trade deficit would have decreased due to a drop in imports that was more pronounced than the estimated decline in exports. On the forecast horizon, consumption is expected to decline from current elevated levels, partly because of tighter domestic financial conditions and a deterioration in real income due to high inflation. Investment would also weaken and return to levels below those seen before the pandemic. In real terms, the trade deficit would narrow due to a lower momentum projection for domestic demand and higher cumulative real depreciation. In sum, economic growth for all of 2022, 2023, and 2024 would stand at 8.0%, 0.2% and 1.0%, respectively (Graph 1.3). Surplus demand remains high (as measured by the output gap) and is expected to decline in 2023 and could turn negative in 2024 (Graph 1.4). Although the macroeconomic forecast includes a marked slowdown in the economy, an even greater adjustment in domestic absorption cannot be ruled out due to the cumulative effects of tighter external and domestic financial conditions, among other reasons. These estimates continue to be subject to a high degree of uncertainty, which is associated with factors such as global political tensions, changes in international interest rates and their effects on external demand, global risk aversion, the effects of the approved tax reform, the possible impact of reforms announced for this year (pension, health, and labor reforms, among others), and future measures regarding hydrocarbon production. In 2022, the current account deficit would have been high (6.3 % of GDP), but it would be corrected significantly in 2023 (to 3.9 % of GDP) given the expected slowdown in domestic demand. Despite favorable terms of trade, the high external imbalance that would occur during 2022 would be largely due to domestic demand growth, cost pressures associated with high freight rates, higher external debt service payments, and good performance in terms of the profits of foreign companies.2 By 2023, the adjustment in domestic demand would be reflected in a smaller current account deficit especially due to fewer imports, a global moderation in prices and cost pressures, and a reduction in profits remitted abroad by companies with foreign direct investment (FDI) focused on the local market. Despite this anticipated correction in the external imbalance, its level as a percentage of GDP would remain high in the context of tight financial conditions. In the world's main economies, inflation forecasts and expectations point to a reduction by 2023, but at levels that still exceed their central banks' targets. The path anticipated for the Federal Reserve (Fed) interest rate increased and the forecast for global growth continues to be moderate. In the fourth quarter of 2022, logistics costs and international prices for some foods, oil and energy declined from elevated levels, bringing downward pressure to bear on global inflation. Meanwhile, the higher cost of financing, the loss of real income due to high levels of global inflation, and the persistence of the war in Ukraine, among other factors, have contributed to the reduction in global economic growth forecasts. In the United States, inflation turned out to be lower than estimated and the members of the Federal Open Market Committee (FOMC) reduced the growth forecast for 2023. Nevertheless, the actual level of inflation in that country, its forecasts, and expectations exceed the target. Also, the labor market remains tight, and fiscal policy is still expansionary. In this environment, the Fed raised the expected path for policy interest rates and, with this, the market average estimates higher levels for 2023 than those forecast in October. In the region's emerging economies, country risk premia declined during the quarter and the currencies of those countries appreciated against the US dollar. Considering all the above, for the current year, the Central Bank's technical staff increased the path estimated for the Fed's interest rate, reduced the forecast for growth in the country's external demand, lowered the expected path of oil prices, and kept the country’s risk premium assumption high, but at somewhat lower levels than those anticipated in the previous Monetary Policy Report. Moreover, accumulated inflationary pressures originating from the behavior of the exchange rate would continue to be important. External financial conditions facing the economy have improved recently and could be associated with a more favorable international context for the Colombian economy. So far this year, there has been a reduction in long-term bond interest rates in the markets of developed countries and an increase in the prices of risky assets, such as stocks. This would be associated with a faster-than-expected reduction in inflation in the United States and Europe, which would allow for a less restrictive course for monetary policy in those regions. In this context, the risks of a global recession have been reduced and the global appetite for risk has increased. Consequently, the risk premium continues to decline, the Colombian peso has appreciated significantly, and TES interest rates have decreased. Should this trend consolidate, exchange rate inflationary pressures could be less than what was incorporated into the macroeconomic forecast. Uncertainty about external forecasts and their impact on the country remains high, given the unpredictable course of the war in Ukraine, geopolitical tensions, local uncertainty, and the extensive financing needs of the Colombian government and the economy. High inflation with forecasts and expectations above 3.0%, coupled with surplus demand and a tight labor market are compatible with a contractionary stance on monetary policy that is conducive to the macroeconomic adjustment needed to mitigate the risk of de-anchoring inflation expectations and to ensure that inflation converges to the target. Compared to the forecasts in the October edition of the Monetary Policy Report, domestic demand has been more dynamic, with a higher observed level of output exceeding the productive capacity of the economy. In this context of surplus demand, headline and core inflation continued to trend upward and posted surprising increases. Observed and expected international interest rates increased, the country’s risk premia lessened (but remains at high levels), and accumulated exchange rate pressures are still significant. The technical staff's inflation forecast for 2023 increased and inflation expectations remain well above 3.0%. All in all, the risk of inflation expectations becoming unanchored persists, which would accentuate the generalized indexation process and push inflation even further away from the target. This macroeconomic context requires consolidating a contractionary monetary policy stance that aims to meet the inflation target within the forecast horizon and bring the economy's output to levels closer to its potential. 1.2 Monetary Policy Decision At its meetings in December 2022 and January 2023, Banco de la República’s Board of Directors (BDBR) agreed to continue the process of normalizing monetary policy. In December, the BDBR decided by a majority vote to increase the monetary policy interest rate by 100 basis points (bps) and in its January meeting by 75 bps, bringing it to 12.75% (Graph 1.5). 1/ Seasonally and calendar adjusted. 2/ In the current account aggregate, the pressures for a higher external deficit come from those companies with FDI that are focused on the domestic market. In contrast, profits in the mining and energy sectors are more than offset by the external revenue they generate through exports. Box 1 - Electricity Rates: Recent Developments and Indexation. Author: Édgar Caicedo García, Pablo Montealegre Moreno and Álex Fernando Pérez Libreros Box 2 - Indicators of Household Indebtedness. Author: Camilo Gómez y Juan Sebastián Mariño