Imperfect Model Research Articles

Floodwater utilization potential assessment of China based on improved conceptual model and multi-reservoir basin assessment method

Floodwater resource utilization is an important way to alleviate the water resources shortage in China. The quantitative assessment of the floodwater utilization potential under the current engineering conditions is vital for implementation of floodwater resource utilization. However, the imperfect conceptual model of floodwater utilization potential, the limited availability of reservoir engineering data, and the lack of universal assessment method that directly considers the reservoirs regulation capacity pose a major challenge to assessment of floodwater utilization potential in China. This study improves the universality of the previous conceptual model of floodwater utilization potential by analyzing the differences and connections of floodwater resources potentials under different states of utilization. The floodwater utilization potential assessment method for river basin with multi-reservoir was also proposed based on our previous assessment method for single reservoir controlled basin. The assessment method was used to evaluate the floodwater utilization potential of the first- and second-level water resources regions in China based on the reanalyzed monthly runoff data (1902 ∼ 2014) and large and medium-sized reservoirs data (as of 2015). Results indicate that the annual average total floodwater utilization potential of China was calculated as 668.62 billion m3 under the current reservoir regulation capacity, accounting for 34.05% of the total floodwater water resources in China. The study demonstrated that the frequency curve of floodwater utilization potential can provide a better understanding of the states of floodwater utilization, and the assessment method. Strategies for long-term floodwater utilization and flood control planning in different regions of China were also provided to government in our study.

Optimal Scheme Models with Imperfect Checkpoint

We propose optimal checkpointing models for dual and majority decision structures with imperfect checkpoints. In systems with high reliability, the process returns to the previous checkpoint when errors occur. However, in real-time systems, if the process always returns to the previous checkpoint, its process might not end in time. In this paper, we propose extended checkpoint models in which when errors occur, the process makes forward and backward recoveries, i.e., the process makes the same one again or returns to the first one with some probability. Using dual and majority decision structures as processing modules, these models are applied to constant and random tasks for the processing of objective works. Furthermore, it is shown that processing models are given by the more general forms using a K-out-of-n system. We formulate simultaneous renewal equations for imperfect models and solve them skillfully, using mathematical methods. The mean execution times until the process succeeds are obtained, and optimal policies to minimize them are derived analytically. These results include the former ones and would be useful for some practical checkpoint models.

Process modelling to facilitate model-based decision-making for resource recovery from urban wastewater - A grey-box approach applied to nanofiltration

Currently, decision support tools (DSTs) for wastewater treatment and resource recovery from wastewater use oversimplified databases evaluate and design of treatment trains. The databases consist of only the average, minimum, and maximum process performances, whereas most processes perform differently depending on the process characteristics and operating conditions. To address this issue of oversimplification, this study demonstrates how a grey-box modelling approach for nanofiltration (NF) can serve as an alternative to extensive databases. The membrane model used in this study is a modified version of the solution-diffusion imperfection model proposed by Niewersch et al. (2020). This model was used to estimate water and solute permeabilities (chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP)) based on flux and solute removal literature data at various transmembrane pressures (TMPs; between 4 and 24 bar) for the two membranes, Dow NF90 and NF270. The estimated parameters were cross-validated to predict flux and solute removal. The validation mean absolute percentage errors were below 20 % in most cases, except for the TN rejection, which was 51 %. The applicability and relevance of the NF model were then evaluated using an optimisation model aimed at meeting recovery targets and simultaneously minimising costs (operational and capital expenditure defined by the membrane area and the pumping power, respectively). The optimisation results showed that the selection of an NF membrane (NF90 or NF270) and the operating condition (TMP) were sensitive to the resource recovery targets. In conclusion, a grey-box model can potentially improve the performance of DSTs for resource recovery from wastewater.

Smoothed Online Combinatorial Optimization Using Imperfect Predictions

Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds. We study smoothed online combinatorial optimization problems when an imperfect predictive model is available, where the model can forecast the future cost functions with uncertainty. We show that using predictions to plan for a finite time horizon leads to regret dependent on the total predictive uncertainty and an additional switching cost. This observation suggests choosing a suitable planning window to balance between uncertainty and switching cost, which leads to an online algorithm with guarantees on the upper and lower bounds of the cumulative regret. Empirically, our algorithm shows a significant improvement in cumulative regret compared to other baselines in synthetic online distributed streaming problems.

Learning-based state estimation and control using MHE and MPC schemes with imperfect models

This paper presents a reinforcement learning-based observer/controller using Moving Horizon Estimation (MHE) and Model Predictive Control (MPC) schemes where the models used in the MHE-MPC cannot accurately capture the dynamics of the real system. We first show how an MHE cost modification can improve the performance of the MHE scheme such that a true state estimation is delivered even if the underlying MHE model is imperfect. A compatible Deterministic Policy Gradient (DPG) algorithm is then proposed to directly tune the parameters of both the estimator (MHE) and controller (MPC) in order to achieve the best closed-loop performance based on inaccurate MHE-MPC models. To demonstrate the effectiveness of the proposed learning-based estimator-controller, three numerical examples are illustrated.

GNSS-based precise orbit determination for maneuvering LEO satellites

Maneuverability is essential for low Earth orbit (LEO) satellites to fulfill various operational objectives. However, the precise orbit determination (POD) process might deteriorate due to imperfect satellite orbital dynamics modeling. This article develops a generic POD strategy with maneuver handling for LEO satellites equipped with high-performance spaceborne Global Navigation Satellite System (GNSS) receivers. Given the time span of an executed maneuver, a set of constant thrust accelerations in the satellite body-fixed reference frame is estimated without using a-priori maneuver accelerations. In addition, different numbers of velocity pulses are estimated at predefined epochs determined by the duration of a maneuver. POD experiments are done for the GRACE-FO and Sentinel-3 satellites, for which the orbit maneuvers vary significantly. The orbits are assessed via internal consistency checks and external orbit validations. Internally, in each direction, the agreement between the reduced-dynamic and kinematic orbits reaches a level of 1 cm, which is comparable with the reference day without maneuvers. Externally, comparisons with the official GRACE-FO products and orbits from the Sentinel-3 Copernicus POD Quality Working Group confirm the reliability of the new orbits with maneuver handling. Finally, satellite laser ranging and K-band ranging measurements indicate a 1-cm accuracy of the absolute orbits and a 2-mm accuracy of the GRACE-FO relative orbits. The maneuver handling strategy is tested in the Bernese GNSS Software, consistently developed at the Astronomical Institute of the University of Bern.

Tau pathology in neurodegenerative disease: disease mechanisms and therapeutic avenues.

Tauopathies are disorders associated with tau protein dysfunction and insoluble tau accumulation in the brain at autopsy. Multiple lines of evidence from human disease, as well as nonclinical translational models, suggest that tau has a central pathologic role in these disorders, historically thought to be primarily related to tau gain of toxic function. However, a number of tau-targeting therapies with various mechanisms of action have shown little promise in clinical trials in different tauopathies. We review what is known about tau biology, genetics, and therapeutic mechanisms that have been tested in clinical trials to date. We discuss possible reasons for failures of these therapies, such as use of imperfect nonclinical models that do not predict human effects for drug development; heterogeneity of human tau pathologies which may lead to variable responses to therapy; and ineffective therapeutic mechanisms, such as targeting of the wrong tau species or protein epitope. Innovative approaches to human clinical trials can help address some of the difficulties that have plagued our field's development of tau-targeting therapies thus far. Despite limited clinical success to date, as we continue to refine our understanding of tau's pathogenic mechanism(s) in different neurodegenerative diseases, we remain optimistic that tau-targeting therapies will eventually play a central role in the treatment of tauopathies.

Do retail businesses have efficient incentives to invest in public charging stations for electric vehicles?

Many public charging stations for electric vehicles in the United States are chargers installed at the premises of pre-existing businesses such as grocery stores or restaurants. This paper investigates the incentives of these retail businesses to install and operate charging stations. First, we note that observed pricing schedules for charging stations significantly depart from both first-best and monopoly pricing. We argue that a possible explanation is that many hosting facilities may install a charging station primarily as a strategy to attract more customers to their core business. Second, we use an imperfect competition model to study retail businesses’ incentives to invest in charging stations. We show that hosting facilities may be trapped in a prisoner’s dilemma where investing in a charging station decreases their profit in the long run. However, the equilibrium level of investments in public charging stations need not differ from the socially optimal outcome.

Observationally-constrained projections of an ice-free Arctic even under a low emission scenario

The sixth assessment report of the IPCC assessed that the Arctic is projected to be on average practically ice-free in September near mid-century under intermediate and high greenhouse gas emissions scenarios, though not under low emissions scenarios, based on simulations from the latest generation Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Here we show, using an attribution analysis approach, that a dominant influence of greenhouse gas increases on Arctic sea ice area is detectable in three observational datasets in all months of the year, but is on average underestimated by CMIP6 models. By scaling models’ sea ice response to greenhouse gases to best match the observed trend in an approach validated in an imperfect model test, we project an ice-free Arctic in September under all scenarios considered. These results emphasize the profound impacts of greenhouse gas emissions on the Arctic, and demonstrate the importance of planning for and adapting to a seasonally ice-free Arctic in the near future.

Imperfect maintenance modeling and assessment of repairable multi-component systems

Industrial systems are subject to several maintenance tasks during their operational life. To ensure optimal performance of these systems, it is crucial to build maintenance models that describe the real behavior of the system. In this scope, the herein paper provides an original approach for maintenance efficiency modeling of multi-component systems. The proposed approach seeks to model the behavior of multi-component system after the replacement of one or more of its components and evaluate this effect. As a matter of fact, from maintenance efficiency point of view, the replacement of one component can be considered as perfect maintenance for the component itself but imperfect for the whole system. To this end, a hybrid model, which considers geometric adjustment of failure intensity, proportional adjustment of failure intensity and proportional adjustment of age is provided. Then, an efficient procedure based on Bayesian networks is provided for the assessment of the imperfect maintenance model parameters. Finally, the proposed model is compared to the most common imperfect maintenance models.

Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks

AbstractRegional climate models (RCMs) have a high computational cost due to their higher spatial resolution compared to global climate models (GCMs). Therefore, various downscaling approaches have been developed as a surrogate for the dynamical downscaling of GCMs. This study assesses the potential of using a cost‐efficient machine learning alternative to dynamical downscaling by using the example case study of emulating surface mass balance (SMB) over the Antarctic Peninsula. More specifically, we determine the impact of the training framework by comparing two training scenarios: (a) a perfect and (b) an imperfect model framework. In the perfect model framework, the RCM‐emulator learns only the downscaling function; therefore, it was trained with upscaled RCM (UPRCM) features at GCM resolution. This emulator accurately reproduced SMB when evaluated on UPRCM, but its predictions on GCM data conserved RCM‐GCM inconsistencies and led to underestimation. In the imperfect model framework, the RCM‐emulator was trained with GCM features and downscaled the GCM while exposed to RCM‐GCM inconsistencies. This emulator predicted SMB close to the truth, showing it learned the underlying inconsistencies and dynamics. Our results suggest that a deep learning RCM‐emulator can learn the proper GCM to RCM downscaling function while working directly with GCM data. Furthermore, the RCM‐emulator presents a significant computational gain compared to an RCM simulation. We conclude that machine learning emulators can be applied to produce fast and fine‐scaled predictions of RCM simulations from GCM data.

Disentangling the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union

In this work, we manage to disentangle the role of virus infectiousness and awareness-based human behavior in the COVID-19 pandemic. Using Bayesian inference, we quantify the uncertainty of a state-space model whose propagator is based on an unusual SEIR-type model since it incorporates the effective population fraction as a parameter. Within the Markov Chain Monte Carlo (MCMC) algorithm, Unscented Kalman Filter (UKF) may be used to evaluate the likelihood approximately. UKF is a suitable strategy in many cases, but it is not well-suited to deal with non-negativity restrictions on the state variables. To overcome this difficulty, we modify the UKF, conveniently truncating Gaussian distributions, which allows us to deal with such restrictions. We use official infection notification records to analyze the first 22 weeks of infection spread in each of the 27 countries of the European Union (EU). It is known that such records are the primary source of information to assess the early evolution of the pandemic and, at the same time, usually suffer underreporting and backlogs. Our model explicitly accounts for uncertainty in the dynamic model parameters, the dynamic model adequacy, and the infection observation process. We argue that this modeling paradigm allows us to disentangle the role of the contact rate, the effective population fraction, and the infection observation probability across time and space with an imperfect first principles model. Our findings agree with phylogenetic evidence showing little variability in the contact rate, or virus infectiousness, across EU countries during the early phase of the pandemic, highlighting the advantage of incorporating the effective population fraction into pandemic modeling for heterogeneity in both human behavior and reporting. Finally, to evaluate the consistency of our data assimilation method, we performed a forecast that adequately fits the actual data. Statement of significanceData-driven and model-based epidemiological studies aimed at learning the number of people infected early during a pandemic should explicitly consider the behavior-induced effective population effect. Indeed, the non-isolated, or effective, fraction of the population during the early phase of the pandemic is time-varying, and first-principles modeling with quantified uncertainty is imperative for an adequate analysis across time and space. We argue that, although good inference results may be obtained using the classical SEIR type model, the model posed in this work has allowed us to disentangle the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union from official infection notification records.

Experimental characterization and modelling of adhesive bonded joints under static and non-monotonic fracture loading in the mode II regime

Understanding and predicting the behaviour of bonded joints under different loading conditions is certainly an aspect of primary importance. Although numerous experimental studies have been carried out over the last few decades, the shear behaviour of bonded joints under cyclic loading has not been investigated in detail. To fill this gap, an extensive experimental and numerical program of double lap shear bonded joints has been carried out. The double lap shear joints were tested under static and cyclic loading to evaluate and compare the influence of external forces on the adhesive performance. The results are discussed in terms of the force-displacement relationship, shear stress, stiffness, residual displacement, and initial damage. Finally, a method is presented for predicting the actual behaviour of double lap shear bonded joints under different loading conditions using an imperfect interface model with damage evolution. The numerical results are in good agreement with the experimental results for all loading conditions. This work has provided and validated an interesting design tool for adhesive structures.

Optimal Linear Multilateration Combined With the Kalman Filter for Range-Only Tracking

This paper presents the optimal estimation for the extensively concerned linear multilateral positioning issues and further improves the accuracy of range-only tracking significantly. The traditional linear multilateration method has been used to achieve target positioning for range-only measurements, but the completeness of the theory and implementation effect are limited due to the imperfect model and the lack of proper statistical error analysis. Moreover, the performance of the representative nonlinear filters is not satisfactory because of the strong nonlinearity between the range observations and kinematic states of the moving target. This paper reveals the essence of the linear fusion of multiple distance sensors from a geometric perspective. The multi-sensor fusion model is reconstructed in the linear feature space by transforming the likelihood function into the probability function and separating the expectation of the compound random variable and residual. After that, a new linear multilateration method is developed based on the precise derivation of statistical characteristics corresponding to the linear fusion model, and also minimizing the squared Mahalanobis distance is introduced to replace minimizing the mean square error. The first-order moment, as a pseudo-measurement used to improve the positioning accuracy, and the second-order moment, as the covariance of the pseudo-measurement noise, are provided by this method, which has been proved to be best linear unbiased according to the Gauss–Markov theorem. Combined with the pseudo-measurement, a standard linear Kalman filter is capable of tracking the maneuvering target. Several simulations and experiments verify the superior performance of the proposed method.

Impact of emission and service constraint for an imperfect production system under two level Hamiltonian

AbstractConsidering the impact of emission on environment, over the last few decades, reduction of emission during production process (or emission free production process) appears to be one of the most growing preferences for every manufacturing firm. Again, manufacturing companies of different products are bounded to offer various facilities to their consumers in order to survive in the current highly competitive market situation. Primarily considering these two factors taken in to account, this study addresses an imperfect manufacturing inventory model in which customers' demand increases linearly w. r. to emission reduction level, service level from the manufacturer and decreases nonlinearly w. r. to price of the items. The analytical expressions of production rates, emission levels and service levels are derived by solving a number of optimum control problems using Hamiltonian maximum principle with a values for the business period and selling price. The basic goal is to maximize the average profit of the system. In addition, a number of numerical examples are considered to elaborate four distinct instances involved in the proposed model and these are solved numerically using teaching learning based optimization algorithm. Finally, sensitivity experiment is performed to investigate the influence of various system factors on the best‐found policies. The sensitivity analysis indicates that service rate provided by the manufacturer and selling price of the items impose a huge impact on the average profit of the system. Further, the carbon reduction affects the revenue of the system less significantly.

A Three-Layer Imperfect Production Supply Chain Model with Green Investment Under Trade Credit and Cap-Trade Policy

A three-layer supply chain model with a single supplier, manufacturer, and retailer is presented in this paper. This proposed model is analyzed under green investment along with trade credit and cap-trade policy. It is considered that the manufacturer’s production process is imperfect. Few inferior quality products are reworked, and the remaining defective items are discarded. The manufacturer makes investments in green technology to reduce carbon emissions. The product’s demand is influenced by selling price, advertisement, and green-technology level. The total average profit of the supply chain is constructed under three different scenarios of trade credit provided by the manufacturer to the retailer. The primary goal of this research is to obtain the optimal selling price, production rate, and green-technology level to maximize the system’s total average profit by using genetic algorithm. A numerical example is shown to demonstrate the model, and sensitivity analysis is carried out to analyze the impact of numerous input parameters. Managerial insights and conclusions are provided to make this model helpful for manufacturers.

Optimal Policies on Education Training for Employees Using Maintenance Modeling Aspects

Employee training is essential for corporate activities to improve their production efficiency and product quality. The most representative two types of employee training are known, i.e., on-the-job training and off-the-job training. Off-the-job training can be classified into two types, i.e., compulsory training and non-compulsory training. Safety training and compliance are known as compulsory training, and they are needed to ensure that daily work continues without serious problems. Compulsory education is undertaken in a classroom every year by all employees of a department or section. Daily e-learning is effective for complementing and enhancing mandatory education and it helps employees to remember what they have learned during their annual education. In this paper, we discuss optimal employee safety education models that are complemented and enhanced by e-learning. The expected cost rate of education is expressed using the imperfect maintenance model, and the optimal policies that minimize it are discussed.

Reliability modelling and evaluating of wind turbine considering imperfect repair

To model and evaluate the reliability of wind turbine (WT) under imperfect repair, an improved Log-linear Proportional Intensity Model (LPIM)-based method was proposed. Initially, using the three-parameter bounded intensity process (3-BIP) as the benchmark failure intensity function of LPIM, an imperfect repair effect-aware WT reliability description model was developed. Among them, the 3-BIP was used to describe the evolution process of the failure intensity in the stable operation stage with running time, while the LPIM reflected the repair effect. Second, the estimation problem for model parameters was transformed into a minimum solution problem for a nonlinear objective function, which was then solved using the Particle Swarm Optimization algorithm. The confidence interval of model parameters was finally estimated using the inverse Fisher information matrix method. Key reliability indices interval estimation based on the Delta method and point estimation was derived. The proposed method was applied to a wind farm’s WT failure truncation time. The proposed method has a higher goodness of fit based on verification and comparison. As a result, it can bring the evaluated reliability closer to engineering practice.

Pengembangan Sistem Pendeteksi Jenis Sayuran dengan Metode CNN Berbasis Android

Vegetables are foodstuffs of plant origin that can be consumed fresh and have various health benefits. However, not a few people do not know the types of vegetables and will find it difficult to find the vegetables they want. This research aims to make it easier for people to find vegetables by classifying them. Researchers developed a model using the Convolutional Neural Network (CNN) method with a total of 15 datasets with a total of 3000 image data. Researchers conducted training datasets with 3 types of epochs, including 20 epochs, 50 epochs and 100 epochs. The training produces accuracy and training loss, with the highest accuracy belonging to Epoch 50 and Epoch 100 and the lowest level of training loss is owned by Epoch 100 with a total of 0.609. However, after the model was deployed, the accuracy results obtained were not as high as the tests conducted on Google Colab. Tests were carried out on several objects, including carrots with an accuracy of 69%, cabbage with an accuracy of 53%, and papaya with an accuracy of 82%. The difference in accuracy results may be caused by objects that are less identical to the datasets or can also be caused by imperfect models. Even so, this application can already be used to classify types of vegetables.

A Real-Time Linear Prediction Algorithm for Detecting Abnormal BDS-2/BDS-3 Satellite Clock Offsets

Due to space environment interference, imperfect data processing model, and the performance of atomic clocks, real-time satellite clock products often contain outliers or irregular biases. We propose a real-time linear moving short-term prediction algorithm to predict clock offsets and detect abnormalities. The proposed algorithm mainly includes phase/frequency anomaly detection and real-time prediction part. Both the phase and frequency domains are used to detect abnormal clock offsets with previous epochs for building the clock prediction model accurately. The real-time moving prediction module utilizes the high short-term prediction performance to check the clock abnormality. The performance of the algorithm is then evaluated for all satellites with real-time estimated satellite clock offsets. To verify the feasibility and effectiveness of the proposed linear moving model and algorithm, the results of the grey model GM(1,1) and the ARIMA model are also compared. The experimental results indicated that the algorithm can detect clock outliers, frequency modulation, and phase jumps, and the linear model has a better clock performance improvement. After the abnormalities are removed with the proposed algorithm, the average STD accuracy of the real-time clock offsets for all satellites is improved by 15.5%, compared to an improvement of 11.4% by the GM(1,1) model and 11.5% by the ARIMA model. The PPP results demonstrate that the proposed clock prediction algorithm improves the positioning accuracy by 8.1%, 13.3%, and 16.9% in the east, north, and up components, respectively.