Methodology For Adjustment Research Articles

A Practical Methodology for Real-Time Adjustment of Kalman Filter Process Noise for Lithium Battery State-of-Charge Estimation

A Practical Methodology for Real-Time Adjustment of Kalman Filter Process Noise for Lithium Battery State-of-Charge Estimation

A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processes

In the current paradigm of Zero Defect Manufacturing, it is essential to obtain mathematical models that express the propagation of manufacturing deviations along Multistage Manufacturing Processes (MMPs). Linear physical-based models such as the Stream of Variation (SoV) model are commonly used, but its accuracy may be limited when applied to MMPs with a large amount of stages, mainly because of the modeling errors at each stage that are accumulated downstream.In this paper we propose a methodology to calibrate the SoV model using data from the inspection stations and prior engineering-based knowledge. The data used for calibration does not contain information about the sources of variation, and they must be estimated as part of the model adjustment procedure. The proposed methodology consists of a recursive algorithm that minimizes the difference between the sample covariance of the measured Key Product Characteristic (KPC) deviations and its estimation, which is a function of a variation propagation matrix and the covariance of the deviation of the variation sources. To solve the problem with standard convex optimization tools, Schur complements and Taylor series linearizations are applied. The output of the algorithm is an adjusted model, which consists of a variation propagation matrix and an estimation of the aforementioned variation source covariance.In order to validate the performance of the algorithm, a simulated case study is analyzed. The results, based on Monte Carlo simulations, show that the estimation errors of the KPC deviation covariances are proportional to the measurement noise variance and inversely proportional to the number of processed parts that have been used to train the algorithm, similarly to other process estimators in the literature.

A novel re-entrant honeycomb metamaterial with tunable bandgap

An auxetic metamaterial consisting of a re-entrant honeycomb structure with hierarchical characteristics (RHS-H) is proposed. The new structure is constructed by attaching small re-entrant structural unit cells to the nodes of the traditional re-entrant structures. Not only can the overall stiffness and stability of the proposed structure be tuned during compression and tension, but a better acoustic performance is also obtained compared with traditional re-entrant honeycomb structures. Firstly, the deformation mechanism of the bandgap is numerically explored by analyzing the dispersion curve of the microstructure as well as the upper and lower bounds of the bandgap vibrational modes. Secondly, the bandgap tunability of the designed structure under uniaxial compression or tension is discussed. Finally, the transmittance of finite period size is calculated to verify the numerical results of the bandgap. Numerical simulation results show that the proposed novel RHS-H has attenuation characteristics of a tunable low-frequency plane wave through a reasonable selection of compressive strain, tensile strain and geometric parameters. The vibration damping strength of the bandgap increases under tensile strain. When the auxetic effect is enhanced, the first and second bandgaps become lower and wider. The novel metamaterial has potential applications in vibration and noise reduction and the design of acoustic devices in dynamic environments, while providing new ideas and a methodology for the real-time adjustment of bandgaps.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach.

Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.

Accounting mechanism for information signals about the imposition of sanctions in valuation of a company

Investors have to review regularly their forecasts and stock value estimates taking into consideration the imposition of sanctions against Russian companies. Due to the presence of behavioral effects, as well as to the incorrect accounting of incoming information, the risk of obtaining inaccurate stock value estimates increases. In this regard, a mechanism should be developed for adequate update of stock value estimates upon receipt of new information signals about the imposition of sanctions. The study is based on such methods as analysis, synthesis, the longitudinal method, as well as the method of discounted cash flows. The study is based on the dynamics of financial indicators of stock returns of the 40 most marketable Russian companies. A mechanism is proposed for accounting the informational signals about sanctions, which makes it possible to avoid behavioral effects and update adequately the intrinsic stock value estimates upon receipt of new information on the imposition of sanctions. This mechanism makes it possible to consider both changes in expectations regarding cash flows and changes in the cost of capital for companies. At the same time, the research in this field should be continued in order to develop a methodology for equity capital valuation adjustment that will be instrumental in consideration of variance in bias and emergence of the so-called “thick tails” in the distribution of stock returns under receipt of information signals on the imposition of sanctions.

Baseline adjustment methodology in a shared water savings contract under serious drought conditions

Baselines are often employed in shared water saving contracts for estimating water savings after some type of intervention by the water service company. An adjustment to the baseline may become necessary under certain conditions. Earlier work has described a number of relatively complex methods for baseline determination and adjustment, but application in regions faced with relatively limited data becomes problematic. If the adjustment were determined before finalising the contractual matters, it would be possible to gather the required data in order to determine the adjustment. However, in cases where no adjustment was fixed prior to the contract, a method is required to determine an adjustment mid-contract based on whatever data are available at the time. This paper presents a methodology for baseline adjustment in an existing shared water savings contract and explains how adjustment could be determined mid-contract, under conditions of limited data. The adjustment compensates for expected reduced water consumption due to external influences induced by serious water restrictions, typically introduced during periods of drought. The fundamental principle underpinning the baseline adjustment methodology presented in this paper involved segregating real water losses from the actual consumption of end-users, preferably by analysing the minimum night flow. In the absence of recorded night flows, an alternative procedure involving the minimum monthly consumption pre- and post-baseline was employed. The baseline adjustment method was subsequently applied in a South African case study, reported on separately. This technique is helpful because adjustments could be determined without adding unnecessary complexity or cost, and provides a means to resolve disputes in cases where unexpected savings occur mid-contract.

Baseline adjustment methodology in a shared water savings contract under serious drought conditions

Baselines are often employed in shared water saving contracts for estimating water savings after some type of intervention by the water service company. An adjustment to the baseline may become necessary under certain conditions. Earlier work has described a number of relatively complex methods for baseline determination and adjustment, but application in regions faced with relatively limited data becomes problematic. If the adjustment were determined before finalising the contractual matters, it would be possible to gather the required data in order to determine the adjustment. However, in cases where no adjustment was fixed prior to the contract, a method is required to determine an adjustment mid-contract based on whatever data are available at the time. This paper presents a methodology for baseline adjustment in an existing shared water savings contract and explains how adjustment could be determined mid-contract, under conditions of limited data. The adjustment compensates for expected reduced water consumption due to external influences induced by serious water restrictions, typically introduced during periods of drought. The fundamental principle underpinning the baseline adjustment methodology presented in this paper involved segregating real water losses from the actual consumption of end-users, preferably by analysing the minimum night flow. In the absence of recorded night flows, an alternative procedure involving the minimum monthly consumption pre- and post-baseline was employed. The baseline adjustment method was subsequently applied in a South African case study, reported on separately. This technique is helpful because adjustments could be determined without adding unnecessary complexity or cost, and provides a means to resolve disputes in cases where unexpected savings occur mid-contract.

Integrated multimodal photoacoustic microscopy with OCT- guided dynamic focusing.

Combining different contrast mechanisms to achieve simultaneous multimodal imaging is always desirable but is challenging due to the various optical and hardware requirements for different imaging systems. We developed a multimodal microscopic optical imaging system with the capability of providing comprehensive structural, functional and molecular information of living tissues. This imaging system integrated photoacoustic microscopy (PAM), optical coherence tomography (OCT), optical Doppler tomography (ODT) and confocal fluorescence microscopy in one platform. By taking advantage of the depth resolving capability of OCT, we developed a novel OCT-guided surface contour scanning methodology for dynamic focusing adjustment. We have conducted phantom, in vivo, and ex vivo tests to demonstrate the capability of the multimodal imaging system for providing comprehensive microscopic information of biological tissues. Integrating all the aforementioned imaging modalities with OCT-guided dynamic focusing for simultaneous multimodal imaging has promising potential for preclinical research and clinical practice in the future.

The Pennsylvania Trauma Outcomes Study Risk-Adjusted Mortality Model: Results of a Statewide Benchmarking Program

Trauma centers need objective feedback on performance to inform quality improvement efforts. The Trauma Quality Improvement Program recently published recommended methodology for case mix adjustment and benchmarking performance. We tested the feasibility of applying this methodology to develop risk-adjusted mortality models for a statewide trauma system. We performed a retrospective cohort study of patients ≥16 years old at Pennsylvania trauma centers from 2011 to 2013 (n = 100,278). Our main outcome measure was observed-to-expected mortality ratios (overall and within blunt, penetrating, multisystem, isolated head, and geriatric subgroups). Patient demographic variables, physiology, mechanism of injury, transfer status, injury severity, and pre-existing conditions were included as predictor variables. The statistical model had excellent discrimination (area under the curve = 0.94). Funnel plots of observed-to-expected identified five centers with lower than expected mortality and two centers with higher than expected mortality. No centers were outliers for management of penetrating trauma, but five centers had lower and three had higher than expected mortality for blunt trauma. It is feasible to use Trauma Quality Improvement Program methodology to develop risk-adjusted models for statewide trauma systems. Even with smaller numbers of trauma centers that are available in national datasets, it is possible to identify high and low outliers in performance.

Multivariate Seasonal Adjustment, Economic Identities, and Seasonal Taxonomy

This article extends the methodology for multivariate seasonal adjustment by exploring the statistical modeling of seasonality jointly across multiple time series, using latent dynamic factor models fitted using maximum likelihood estimation. Signal extraction methods for the series then allow us to calculate a model-based seasonal adjustment. We emphasize several facets of our analysis: (i) we quantify the efficiency gain in multivariate signal extraction versus univariate approaches; (ii) we address the problem of the preservation of economic identities; (iii) we describe a foray into seasonal taxonomy via the device of seasonal co-integration rank. These contributions are developed through two empirical studies of aggregate U.S. retail trade series and U.S. regional housing starts. Our analysis identifies different seasonal subcomponents that are able to capture the transition from prerecession to postrecession seasonal patterns. We also address the topic of indirect seasonal adjustment by analyzing the regional aggregate series. Supplementary materials for this article are available online.

A new analytical approach for simplified thermal modelling of buildings: Self-Adjusting RC-network model

A novel method for adjusting the parameters of a lumped parameter model for the transient thermal response of building constructions is presented. Previous analytical adjustment methods can be complex and inaccurate, while optimization algorithms, although more accurate, require prior simulation using a high-order reference model, and so provide no advantages to be integrated into simulation programs. This work presents a methodology for the analytical adjustment of a first-order model based on the hypothesis that the position of the capacitance varies in every time step in response to changes in the excitation value. By comparison with a reference model and using a wide range of constructions (420), the functional form of this dependence was determined in accordance with the value of time step and properties and thickness of the element. Using different typical constructions (41), the method was validated by comparison with the reference model in terms of surface heat fluxes, surface temperatures and indoor air temperature. The results showed an excellent agreement with the reference model for surface temperatures and indoor air temperature, and good agreement for surface heat fluxes. The method can be integrated into simulation programs with a low computational cost, sufficient accuracy, universality and adaptable time step.

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

The Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things.

Cognitive Methodology for Optical Amplifier Gain Adjustment in Dynamic DWDM Networks

Our recently proposed cognitive methodology for optical amplifier gain adjustment, that relies on case-based reasoning, showed optical signal-to-noise ratio improvements over time demonstrating the cognition process regardless the deployed amplifier type. In this paper, we extend our preliminary analysis exploring the cognitive methodology benefits for different and larger network topologies. The obtained results show agreement between networks, demonstrating the methodology suitability regardless the network scenario.

Automated visual position detection and adjustment for optical waveguide chips and optical fiber arrays

The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adjustments is one of the key steps of alignment coupling. A methodology for position detection, and angle and spacing adjustment was proposed for optical waveguide chips and optical fiber arrays based on machine vision. The experimental results show angle detection precision levels higher than 0.05°, line detection precision levels higher than 0.1 μm, and detection time less than 2 s. Therefore, the system developed herein meets the precise requirements necessary for position detection, and angle and spacing adjustments for optical waveguide chips and optical fiber arrays.

Future stability challenges for the UK network with high wind penetration levels

Offshore wind plant such as variable speed wind turbines (DFIG) will play an increasingly important role in future decades if ever-stringent requirements of energy security and low carbon emissions are to be met. Although some analysis of the impact of DFIG on system stability has previously been reported, none of it is based on a large network representing a real system, or the large network is simply not publicly available. This paper describes one such suitable equivalent dynamic network for stability studies based on the whole UK transmission system. The methodology for appropriate control system design and adjustment of the parameters under different dispatch conditions is presented. The network model is subsequently updated according to various National Grid future energy scenarios where DFIG models are appropriately added and distributed. Two important aspects contributing to future system stability are studied in detail, namely maximum value of the rate of change of frequency and transient stability. A number of detailed cases studies under varying wind penetration levels are presented which quantify the impact of key influencing factors such as the size of the largest generating unit for n-1 contingency, amount of primary system response, frequency dependency of load, and others. The study concludes that none of the individual factors can provide a complete solution and that careful cost benefit analysis is needed to determine the proper mix of services and reinforcements needed in the future.

Adjustment of Sensitive Parameters of a Mathematical Model of Glucose Metabolism Using an Evolutionary Algorithm

Currently, closed-loop therapy is a promising option for individuals with type 1 diabetes mellitus. It requires a glucose metabolism model and a control algorithm to reach a suitable automatic insulin delivery. Many mathematical models have been proposed considering the glucose-insulin dynamics and nominal parameters of a standard patient. Nevertheless, the open-loop therapy based on continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) provides sufficient data to seek patient-oriented models instead of the nominal ones. In consequence, patient-oriented models could open the possibility of having personalized closed-loop therapies. In this work, a methodology for automatic adjustment of sensitive parameters of a physiological mathematical model of glucose metabolism is presented. The methodology is based on an evolutionary algorithm and it is exemplified with a physiological model. This methodology provides a criterion to select the best model which can reproduce the glucose-insulin dynamics of a diabetic patient according to the data provided by the CGM and CSII systems.

INTER‐PROVIDER COMPARISON OF PATIENT‐REPORTED OUTCOMES: DEVELOPING AN ADJUSTMENT TO ACCOUNT FOR DIFFERENCES IN PATIENT CASE MIX

This paper describes the development of a methodology for the case-mix adjustment of patient-reported outcome measures (PROMs) data permitting the comparison of outcomes between providers on a like-for-like basis. Statistical models that take account of provider-specific effects form the basis of the proposed case-mix adjustment methodology. Indirect standardisation provides a transparent means of case mix adjusting the PROMs data, which are updated on a monthly basis. Recently published PROMs data for patients undergoing unilateral knee replacement are used to estimate empirical models and to demonstrate the application of the proposed case-mix adjustment methodology in practice. The results are illustrative and are used to highlight a number of theoretical and empirical issues that warrant further exploration. For example, because of differences between PROMs instruments, case-mix adjustment methodologies may require instrument-specific approaches. A number of key assumptions are made in estimating the empirical models, which could be open to challenge. The covariates of post-operative health status could be expanded, and alternative econometric methods could be employed.

Multi-step Functional Process Adjustments to Reduce No-fault-found Product Failures in Service Caused by In-tolerance Faults

This paper proposes a methodology to eliminate or reduce No-Fault-Found (NFF) Product failure in field by adjusting manufacturing processes. No-Fault-Found (NFF) failures can be frequently characterized by in-tolerance faults, i.e., faults occur when design parameters represented by Key Product Characteristics (KPCs) and process variables represented by Key Control Characteristics (KCCs) are within the tolerance limits (in-spec) and therefore they cannot be eliminated by standard process control and adjustment methods. Literature in the area of statistical process and control are unable to monitor and adjust the NFF failures as these methodologies are based on design template itself. Also, all the adjustments strategies consider in process adjustment domain are single-step or adjustments are performed instantaneously. However, the single-step adjustment from process nominal (ud) to functional nominal (uf) is not always possible due to presence of system or resource constraints limiting the amount of adjustment that can be made in a given time such scenario in this paper is refer as multi-step adjustment scenario. This paper proposes a methodology for the multi-step functional process adjustment by identifying the adjustment paths that will minimize the total cost associated with product failure and adjustment cost. The proposed methodology includes: (i) discretization of space from ud to uf, orthogonally, with defined resolution; (ii) identification of warranty cost at each discretised node based on overlap between manufacturing distribution and in-tolerance NFF regions considering node as production nominal; and (iii) determination of the multi-step function process adjustment between ud and uf using recursive dynamic programming formulation. The methodology is illustrated through numerical simulation.

Challenges of measuring progress in Afghanistan using violence trends: the effects of aggregation, military operations, seasonality, weather, and other causal factors

Measuring nationwide progress of counterinsurgency operations in Afghanistan using violence trends is difficult due to several factors: aggregation of data to the national level may obfuscate disparate local trends; the observed seasonality in violence makes comparisons difficult and may obscure progress; and short-term spikes or troughs – attributable to weather, military operations and tempo, or holiday periods – heavily influence simple averaging schemes. Despite these challenges, proper understanding of violence statistics is critical to estimating the effectiveness of military forces added during a surge or redeployed as part of transition. This article explores methods for analyzing observed violence trends to identify causal factors, to provide a comparable baseline, and to inform assessments at appropriate levels of aggregation. One methodology for seasonal adjustment of violence data is discussed and shown to provide a logical baseline for examining trends. An ordinary least squares regression model is developed and implemented using time-series violence data.

Robust extraction of covariate information to improve estimation efficiency in randomized trials.

In randomized trials, investigators typically rely upon an unadjusted estimate of the mean outcome within each treatment arm to draw causal inferences. Statisticians have underscored the gain in efficiency that can be achieved from covariate adjustment in randomized trials with a focus on problems involving linear models. Despite recent theoretical advances, there has been a reluctance to adjust for covariates based on two primary reasons: (i) covariate-adjusted estimates based on conditional logistic regression models have been shown to be less precise and (ii) concern over the opportunity to manipulate the model selection process for covariate adjustments to obtain favorable results. In this paper, we address these two issues and summarize recent theoretical results on which is based a proposed general methodology for covariate adjustment under the framework of targeted maximum likelihood estimation in trials with two arms where the probability of treatment is 50%. The proposed methodology provides an estimate of the true causal parameter of interest representing the population-level treatment effect. It is compared with the estimates based on conditional logistic modeling, which only provide estimates of subgroup-level treatment effects rather than marginal (unconditional) treatment effects. We provide a clear criterion for determining whether a gain in efficiency can be achieved with covariate adjustment over the unadjusted method. We illustrate our strategy using a resampled clinical trial dataset from a placebo controlled phase 4 study. Results demonstrate that gains in efficiency can be achieved even with binary outcomes through covariate adjustment leading to increased statistical power.