Continuous Modeling Research Articles

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented based on the Hydrological Simulation Program–Fortran (HSPF) model. Calibrated parameters related to base flow and moisture distribution showed marked differences between the continuous and event-based modeling. Results of the regionalized sensitivity analysis identified event-dependent parameters and showed that gravity drainage and storage outflow were the primary runoff generation processes for both scenarios. The overall performance of the event-based simulation was better than that of the daily simulation for streamflow based on the generalized likelihood uncertainty estimation (GLUE). The GLUE analysis also indicated that the performance of the continuous model was limited by several extreme events and low flows. In the event-based scenario, the HSPF model performances decreased as the precipitation became intense in the event-based modeling. The structure error of the HSFP model was recognized at the initial phase of the rainfall-event period. This study presents a valuable opportunity to understand dominant controls in different hydrologic scenario and guide the application of the HSPF model.

A COMBINATION OF TEMPORAL SEQUENCE LEARNING AND DATA DESCRIPTION FOR ANOMALYBASED NIDS

Through continuous observation and modelling of normal behavior in networks, Anomaly-based Network Intrusion Detection System (A-NIDS) offers a way to find possible threats via deviation from the normal model. The analysis of network traffic based on time series model has the advantage of exploiting the relationship between packages within network traffic and observing trends of behaviors over a period of time. It will generate new sequences with good features that support anomaly detection in network traffic and provide the ability to detect new attacks. Besides, an anomaly detection technique, which focuses on the normal data and aims to build a description of it, will be an effective technique for anomaly detection in imbalanced data. In this paper, we propose a combination model of Long Short Term Memory (LSTM) architecture for processing time series and a data description Support Vector Data Description (SVDD) for anomaly detection in A-NIDS to obtain the advantages of them. This model helps parameters in LSTM and SVDD are jointly trained with joint optimization method. Our experimental results with KDD99 dataset show that the proposed combined model obtains high performance in intrusion detection, especially DoS and Probe attacks with 98.0% and 99.8%, respectively.

Continuous Visual World Modeling for Autonomous Robot Manipulation

Service robots need to handle a variety of everyday manipulation tasks to accomplish household chores such as cooking and cleaning. Successful execution of these tasks is highly dependent on how reliable the robot perceives its environment through noisy sensing. We present a visual world modeling system for service robots to generate and maintain accurate models of their environments for continuous scenarios. This system is designed to provide a generic platform for both humanoid and ground manipulation robots using different types of vision sensors and algorithms. In our particular implementation, visual data processed by different perception algorithms are used for building and continuously updating a world model of the environment. We evaluate our system on a variety of object manipulation scenarios and show that the system produces consistent perception outcomes suitable for different manipulation tasks.

DIGITAL SOIL MAPPING IN A MOUNTAINOUS AREA WITH MIXED LAND USE (HUMOR CATCHMENT - EASTERN CARPATHIANS, ROMANIA) USING SOIL-LANDSCAPE SYSTEMS, FUZZY LOGIC AND ENVIRONMENTAL COVARIATES

The proper evaluation of soil resources, especially in mountainous areas, is very important for the suitable development of the local communities, which host traditional sustainable agriculture. Nonetheless, regarding the preservation of traditional sustainable agriculture and the introduction of a modern development plan to the area, an actualized and detailed distribution of soil cover is crucial. Soil legacy data is not always available at the right scale and spatial cover. To overcome such obstacles, the most suitable approach is the use of digital soil mapping for supplementing soil information. Sparse soil information is available for the Humor catchment, Eastern Carpathians, Romania, therefore we used a soil-landscape system approach, which when coupled with a fuzzy logic-based assignment of soil to landscape system and a raster GIS representation model of the landscape environmental layers (SoLIM model), allow the continuous spatial modelling of the soil classes. The result was validated against available soil maps and soil profiles and it was shown to better represent spatial distribution of the soil cover, although further work is needed to better sample soils representing local conditions which because weren t predictable in the applied model, were included as punctual occurrences.

Operational design for shuttle systems with modular vehicles under oversaturated traffic: Continuous modeling method

Abstract Existing peak/off-peak based schedules in urban mass transit systems feature two types of dispatch headways and a fixed vehicle capacity across the operational horizon, which lowers their service quality and causes significant energy waste. A promising cure to this challenge is to jointly design the dispatch headways and vehicle capacities in urban transit schedules. Chen et al. (2018) propose a continuous modeling method to solve the near-optimum solutions to and shed analytical insights into this joint design problem. Based on the theoretical properties discovered in the preceding paper, this paper formulates the joint design problem as a mixed integer linear programming model that can yield exact solutions to the optimal design with a discretized time representation. Further, a customized DP algorithm is proposed to solve this model. Similar to other problems that can be solved by DP algorithms, the “curse of dimensionality” also exists in the investigated problem since the queue length, as a state variable, may have a large set of possible values at each stage, which may lead to dramatically increasing computational time in solving the problem. To expedite the solution speed of the DP algorithm, we propose a set of valid inequalities based on the relationship between the queue length and vehicle capacity. These valid inequalities can reduce the unboundedly increasing state space into a narrow band and thus dramatically expedite the DP algorithm. With two sets of numerical experiments, we show that the discrete model can be solved by the customized DP algorithm to optimality with much less computation time compared with a state-of-the-art commercial solver, Gurobi. The analysis also reveals that the input parameters affect the effectiveness of dynamic capacity design under oversaturated and unsaturated traffic systems in a similar way despite some minor differences.

An Optimal Hybrid Control Method for Energy-Saving of Chilled Water System in Central Air Conditioning

Chilled water system of central air conditioning is a typical hybrid system; variable frequency behavior with amplitude limited of pumps reflects continuous and discrete dynamic characteristics. The way of energy-saving is variable water volume, via variable frequency behavior of pumps to gain adjustment of power consumption. Facing the situation of the variable frequency pumps with parallel operation, single continuous or discrete modeling cannot reflect the hybrid features. Thus, the control method will have some questions, such as bad energy-saving effect, difficult accurate adjustment of cold capacity, and low running energy efficiency. However, hybrid system modeling can reflect hybrid dynamic behavior of pumps, which is combining continuous and discrete features. The questions of nonlinear and multiparameters can be solved by control method based on hybrid system. Here, an optimum control method is proposed with the principle of the minimum, by setting the minimum power consumption as the performance function in fixed time, which realizes variable control of pumps and accurate adjustment of temperature inside room. At last, it shows the system characteristics and energy-saving affection by hybrid system modeling and the optimum control method.

Comparison of efficiency of techniques for delineating uni-element anomalies from stream sediment geochemical landscapes

This paper compares analyses of raw geochemical data and processed stream sediment geochemical data (i.e., background-corrected and dilution-corrected via the sample catchment basin approach (SCBA)). Both sets of analyses, based on multifractal approach, aimed to identify geochemical anomalies in three study regions in Iran with different geological characteristics and mineralization styles. The results of both analyses were validated for robustness by using the concept of normalized density (ND), which simultaneously considers both mineralized locations and area of anomalous zones. The comparison of the results emphasizes the efficiency of background and dilution correction procedures, which deal with stream sediment geochemical landscapes in the search for mineral deposits. This paper also presents results of discrete (catchment-based) and continuous (interpolation-based) field modelling of stream sediment geochemical dataset per region study. Although interpolation-based field modelling is rapid, time efficient and simple for delineation of geochemical anomalies, the results of discrete-based field modelling of geochemical anomalies are more superior. Therefore, regardless of the mineralization styles and geological settings, the SCBA to identify geochemical anomalies for mineral exploration is efficient.

Merged rainfall fields for continuous simulation modelling (CSM)

Long sequences of rainfall at fine spatial and temporal detail are increasingly required, not only for hydrological studies, but also to provide inputs for models of crop growth, landfills, tailings dams, liquid waste disposal on land and other environmentally sensitive projects. Rainfall information derived from rain gauges, radar or satellites may not individually be adequate to meet the detail required by hydrological models or other water resource studies. Therefore, a suitable technique is required to estimate rainfall at finer spatial and temporal resolutions. Different techniques have been developed to merge rainfall information from rain gauges, radar and satellites in order to obtain the 'best' estimate of the 'true' rainfall field. However, the length of the radar and satellite estimated rainfall records is currently limited. In this study, the mean areal merged rainfall, derived from rain gauges and radar, was estimated for 26 subcatchments in the Liebenbergsvlei catchment, which is a research catchment, in South Africa for the period when radar data were available. By using the relationships derived between the merged rainfall and rain-gauge data, improved subcatchment rainfall may be estimated using the historical data from rain gauges located in and around the subcatchments. In most of the subcatchments the relationship between the daily mean areal merged rainfall of the subcatchment and the daily rainfall data from rain gauges is strong (R2 > = 0.5). The relationship between the daily rain gauges and mean areal merged rainfall of the subcatchments is used to adjust the historical rainfall data from the daily rain gauges in order to estimate long sequences of subcatchment rainfall for input to continuous simulation models (CSMs).

Hierarchical Bayesian continuous time dynamic modeling.

Continuous time dynamic models are similar to popular discrete time models such as autoregressive cross-lagged models, but through use of stochastic differential equations can accurately account for differences in time intervals between measurements, and more parsimoniously specify complex dynamics. As such they offer powerful and flexible approaches to understand ongoing psychological processes and interventions, and allow for measurements to be taken a variable number of times, and at irregular intervals. However, limited developments have taken place regarding the use of continuous time models in a fully hierarchical context, in which all model parameters are allowed to vary over individuals. This has meant that questions regarding individual differences in parameters have had to rely on single-subject time series approaches, which require far more measurement occasions per individual. We present a hierarchical Bayesian approach to estimating continuous time dynamic models, allowing for individual variation in all model parameters. We also describe an extension to the ctsem package for R, which interfaces to the Stan software and allows simple specification and fitting of such models. To demonstrate the approach, we use a subsample from the German socioeconomic panel and relate overall life satisfaction and satisfaction with health. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Multiskilled workforce management by utilizing closed chains under uncertain demand: A retail industry case

A new methodology is presented for structuring the multiskilling characteristics of a set of initially single-skilled employees in the context of a multi-department service sector business, more specifically in the retail industry. Assuming stochastic demand under continuous modeling of the workforce, the methodology decomposes the multiskilling problem into three stages that are then solved sequentially. The first stage delivers a novel analytic expression for estimating the number of multiskilled employees required by each store department, the second stage applies constructive heuristics to these estimates to generate a feasible set of closed chaining structures, and the third stage uses Monte Carlo simulation and a linear programming model to evaluate the chains so generated. The results show that in unbalanced systems, the solutions involve closed long chains and closed short chains of different lengths, which are robust to demand variability, minimize the expected total cost of staff shortages, and exhibit the best cost-effective performance. The methodology provides a tool for company decision makers to address the three fundamental multiskilling issues in unbalanced systems, namely, where to add multiskilling, how much to add and how it should be added.

Continuous modeling of superheated steam drying of single rice grains

In this work, a transient macroscopic model is proposed for studying the conjugated heat and mass transfer phenomena that occur inside a single rice grain during the superheated steam drying process. The governing equations of this model are derived based on the volume-averaging approach. Since rice is a highly hygroscopic material, more than 80% of water is accumulated as bound water in rice grain. Both the bound water diffusive flow in the solid matrix and the convective flow of water vapor and liquid water in the void volume are taken into account in this model. Thermophysical properties of rice which serve as model inputs are measured, except the absolute permeability and the bound-water diffusivity coefficients. These two properties are determined by minimizing the square of residuals between simulation results and experimental data, which were obtained by means of a magnetic suspension balance system at a drying temperature of 160 °C. The validity of these two estimated parameters is reflected by a good agreement between the numerical and the experimental data at drying temperatures of 120 and 140 °C. Finally, a model-based sensitivity analysis is carried out, from which quantitative guidelines for the relationship between both the absolute permeability and the bound-water diffusivity and drying kinetics are proposed.

Dynamic Regulation Responding to an External Stimulus: A Differential Equation Model

This study examines the dynamic regulation process responding to an external stimulus. The damped oscillator model has been used to describe this process. However, the model does not allow a nonzero steady state, even though the oscillations may continue and do not necessarily damp toward zero. This study introduces the driven damped oscillator model which has an additional parameter to identify different patterns of the steady state. Three methods, generalized local linear approximation, continuous time structural equation modeling, and analytic solutions of differential equations are provided to estimate model parameters. A simulation study indicates that parameters in the driven damped oscillator model are well recovered. The model is then illustrated using a data set on the daily reports of sales after a sale promotion. Potential applications and possible expansions of this model are also discussed.

The Role of Time in the Quest for Understanding Psychological Mechanisms

The lead-lag structure of multivariate time-ordered observations and the possibility to disentangle between-person (BP) from within-person (WP) sources of variance are major assets of longitudinal (panel) data. Hence, psychologists are making increasing use of such data, often with the intent to delineate the dynamic properties of psychological mechanisms, understood as a sequence of causal effects that govern psychological functioning. However, even with longitudinal data, psychological mechanisms are not easily identified. In this article, we show how an adequate representation of time may enhance the tenability of causal interpretations in the context of multivariate longitudinal data analysis. We anchor our considerations with an example that illustrates some of the main problems and questions faced by applied researchers and practitioners. We distinguish between static versus dynamic and discrete versus continuous time modeling approaches and discuss their advantages and disadvantages. We place particular emphasis on different ways of addressing BP differences and stress their dual role as potential confounds versus valuable sources of information for improving estimation and aiding causal inference. We conclude by outlining an approach that offers the potential of better integration of information on BP differences and WP changes in the search for causal mechanisms along with a discussion of current problems and limitations.

Application of horizontal wells in three-dimensional shale reservoir modeling: A case study of Longmaxi–Wufeng shale in Fuling gas field, Sichuan Basin

3-D reservoir modeling is an important aspect to determine the heterogeneity of organic-rich shale reservoirs, an area of study that continues to be explored and refined. A large proportion of data acquired from horizontal wells causes issues in the structural and property modeling for shale reservoirs. Since horizontal wells are designed to drill into a specific, narrow zone, their horizontal section tends to parallel or nearly parallel formation surfaces. As a result, formation surfaces have a much more complex spatial location relationship with horizontal wellbores than with vertical wellbores. The existing algorithms are not good at addressing this issue during structural modeling. The major problem of using horizontal well data in property modeling is the biased dataset as their horizontal section tends to stay within a narrow zone. The property distribution feature estimated from this biased dataset, as a significant, default input of geostatistical simulation algorithms, causes the constructed property models to deviate away from the real case in the subsurface. A method to infer more formation tops in pseudo vertical wells according to a series of assumptions was developed to provide more constraint points for structural modeling within the areas of the horizontal well section. To use the biased database from horizontal wells, distribution function and trend model methods were developed for continuous property modeling, and a percentage and probability trend models were developed for discrete property modeling. The Longmaxi-Wufeng shale in the Fuling gas field of Sichuan Basin was used as an example to express and verify these methods.

Review: Continuous simulation modelling for design flood estimation – a South African perspective and recommendations

A number of severe flooding events have occurred both in South Africa and internationally in recent years. Furthermore, changes in both the intensity and frequency of extreme rainfall events have been documented, both locally and internationally, associated with climate change. The recent loss of life, destruction of infrastructure, and associated economic losses caused by flooding, compounded by the probability of increased rainfall variability in the future, highlight that design flood estimation (DFE) techniques within South Africa are outdated and in need of revision. A National Flood Studies Programme (NFSP) has recently been initiated to overhaul DFE procedures in South Africa. One of the recommendations in the NFSP is the further development of a continuous simulation modelling (CSM) system for DFE in South Africa. The focus of this paper is a review of CSM techniques for DFE, to guide further development for application in South Africa. An introduction to DFE, and particularly the CSM approach, is presented, followed by a brief overview of DFE techniques used in South Africa, leading into a more detailed summary of CSM for DFE within South Africa to date. This is followed by a review of the development and application of CSM methods for DFE internationally, with a focus on the United Kingdom and Australia, where methods have been developed with the intention of national scale implementation. It is important to highlight that there is a plethora of CSM methods available internationally and this review is not exhaustive; it focuses on and identifies some of the strengths and weaknesses of several popular methods, particularly those intended for national scale application, as the intended outcome from this review is to identify a path towards the development of a usable national scale CSM system for DFE in South Africa. Emphasis on a usable method is important, considering the reality that, despite promising results, numerous benefits, and national scale methods being developed, it appears that the CSM method for DFE is rarely used in practice.

On Inclusions Arising in Neural Field Modeling

We study existence of solutions to the Hammerstein integral inclusion with delay generated by a neural field equation with discontinuities and time lags. Particular attention is given to the problem of continuous dependence of the solution set on a parameter. The investigation of this issue has important applications in mathematical neuroscience allowing to establish relations between continuous and discontinuous modeling approaches that are both widely used in mathematical theory of neural fields. In order to obtain the main results for the Hammerstein integral inclusion we develop a framework of abstract Volterra operator inclusions and prove statements on local solvability, on extendability of the solutions, and on parametric dependence of the solution set.

Geometry and Mechanics of Microdomains in Growing Bacterial Colonies

Bacterial colonies are abundant on living and nonliving surfaces and are known to mediate a broad range of processes in ecology, medicine, and industry. Although extensively researched, from single cells to demographic scales, a comprehensive biomechanical picture, highlighting the cell-to-colony dynamics, is still lacking. Here, using molecular dynamics simulations and continuous modeling, we investigate the geometrical and mechanical properties of a bacterial colony growing on a substrate with a free boundary and demonstrate that such an expanding colony self-organizes into a "mosaic" of microdomains consisting of highly aligned cells. The emergence of microdomains is mediated by two competing forces: the steric forces between neighboring cells, which favor cell alignment, and the extensile stresses due to cell growth that tend to reduce the local orientational order and thereby distort the system. This interplay results in an exponential distribution of the domain areas and sets a characteristic length scale proportional to the square root of the ratio between the system orientational stiffness and the magnitude of the extensile active stress. Our theoretical predictions are finally compared with experiments with freely growing E. coli microcolonies, finding quantitative agreement.

Numerical analysis of coupled flows in porous and fluid domains by the Darcy-Brinkman equations

A numerical method to achieve the simultaneous computation of the seepage flows in porous media and the Navier-Stokes flows in the domain occupied purely by a fluid is presented in this paper. The method uses the Darcy-Brinkman equations with the continuous modelling of the interfacial flow velocity, and applies the finite volume method to the spatial discretization and the fractional step method to the numerical simulation of the incompressible fluid. The coupling of the two different flows is realized by the relevant interpolation of the pressure and the flow velocity onto the interface at which permeability and porosity are discontinuous. The interpolation schemes provide simple solutions to overcome the computational instability caused by the discontinuous change in permeability and to avoid having to resolve the considerably thin transition zone near the interface. Numerical analyses of the one-dimensional uniform flow, the lid-driven cavity flow, and the pipe-flow in a porous medium have been carried out. The results have shown that the proposed schemes enable a stable computation and fulfill the accurate and physically realistic numerical solutions.

Anaerobic Co-Digestion of Sludge and Organic Food Waste—Performance, Inhibition, and Impact on the Microbial Community

Anaerobic co-digestion allows for under-utilised digesters to increase biomethane production. The organic fraction of municipal solid waste (OFMSW), i.e., food waste, is an abundant substrate with high degradability and gas potential. This paper investigates the co-digestion of mixed sludge from wastewater treatment plants and OFMSW, through batch and continuous lab-scale experiments, modelling, and microbial population analysis. The results show a rapid adaptation of the process, and an increase of the biomethane production by 20% to 40%, when co-digesting mixed sludge with OFMSW at a ratio of 1:1, based on the volatile solids (VS) content. The introduction of OFMSW also has an impact on the microbial community. With 50% co-substrate and constant loading conditions (1 kg VS/m3/d) the methanogenic activity increases and adapts towards acetate degradation, while the community in the reference reactor, without a co-substrate, remains unaffected. An elevated load (2 kg VS/m3/d) increases the methanogenic activity in both reactors, but the composition of the methanogenic population remains constant for the reference reactor. The modelling shows that ammonium inhibition increases at elevated organic loads, and that intermittent feeding causes fluctuations in the digester performance, due to varying inhibition. The paper demonstrates how modelling can be used for designing feed strategies and experimental set-ups for anaerobic co-digestion.

A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function.

Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc. In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic image and a multispectral image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction method of multipliers which guarantees the convergence of the proposed method. Extensive experiments on many real datasets collected by different sensors demonstrate the effectiveness of the proposed technique as compared with several state-of-the-art pansharpening approaches.