Number Of Input Parameters Research Articles

Estimating Daily Dew Point Temperature Based on Local and Cross-Station Meteorological Data Using CatBoost Algorithm

Accurate estimation of dew point temperature (T_dew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local T_dew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily T_dew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (T_max, T_min and T_mean), maximum, minimum and average relative humidity (RH_max, RH_min and RH_mean), maximum, minimum and average global solar radiation (Rs_max, Rs_min and Rs_mean) from three weather stations in Hunan of China were used to evaluate the CatBoost and RF algorithms. The results showed that both algorithms achieved satisfactory estimation accuracy at the target stations (on average RMSE = 1.020°C, R²=0.969, MAE = 0.718°C and NRMSE = 0.087) in the absence of complete meteorological parameters (with only temperature data as input). The CatBoost algorithm (on average RMSE = 1.900°C and R²=0.835) was better than the RF algorithm (on average RMSE = 2.214°C and R²=0.828). The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters, and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter algorithms. The developed methodology is helpful to predict T_dew at regional scale.

Using the Kriging Response Surface Method for the Estimation of Failure Values of Carbon-Fibre-Epoxy Subsea Composite Flowlines under the Influence of Stochastic Processes

This paper investigates the use of the Kriging response surface method to estimate failure values in carbon-fibre-epoxy composite flow-lines under the influence of stochastic processes. A case study of a 125 mm flow-line was investigated. The maximum stress, Tsai-Wu and Hashin failure criteria was used to assess the burst design under combined loading with axial forces, torsion and bending moments. An extensive set of measured values was generated using Monte Carlo simulation and used as the base case population to which the results from the response surfaces was compared. The response surfaces were evaluated in detail in their ability to reproduce the statistical moments, probability and cumulative distributions and failure values at low probabilities of failure. In addition, the optimisation of the response surface calculation was investigated in terms of reducing the number of input parameters and size of the response surface. Finally, a decision chart that can be used to build a response surface to calculate failures in a carbon fibre-epoxy-composite (CFEC) flow-line was proposed based on the findings obtained. The results show that the response surface method is suitable and can calculate failure values close to that calculated using a large set of measured values. The results from this paper provide an analytical framework for identifying the principal design parameters, response surface generation, and failure prediction for CFEC flow-lines.

A Surrogate Model to Predict Production Performance in Digital Twin-Based Smart Manufacturing

Abstract With the dynamic arrival of production orders and unforeseen changes in shop-floor conditions within a production system, production scheduling presents a challenge for manufacturing firms to ensure production demands are met with high productivity and low operating cost. Before a production schedule is generated to process the incoming production orders, production planning is performed. Given the large number of input parameters involved in the production planning, it poses the challenge on how to systematically and accurately predict and evaluate production performance. Hence, it is important to understand the interactions of the input parameters between the production planning and the scheduling. This is to ensure that the production planning and the scheduling are coordinated and can be performed to achieve optimal production performance such as minimizing cost effectively and efficiently. Digital twin presents an opportunity to mirror the real-time production status and analyze the input parameters affecting the production performance in smart manufacturing. In this paper, we propose an approach to develop a surrogate model to predict the production performance using input parameters from a production plan using the capabilities of real-time synchronization of production data in digital twin. Multivariate adaptive regression spline (MARS) is applied to construct a surrogate model based on three categories of input parameters, i.e., current production system load, machine-based and product-based parameters. An industrial case study involving a wafer fabrication production is used to develop the surrogate model based on a random sampling of varying numbers of training data set. The proposed MARS model shows a high correlation coefficient and a large reduction in the number of input parameters for both linear and nonlinear cases with relation to three performances, namely flowtime, tardiness, and machine utilization.

Superiorization versus regularization: A comparison of algorithms for solving image reconstruction problems with applications in computed tomography.

A system matrix can be built in order to account for the refractions in an optical computed tomography (CT) system. In order to utilize this system matrix, iterative methods are employed to solve the image reconstruction problem. The purpose of this study is to compare potential iterative algorithms to solve this image reconstruction problem. Comparisons examine both solution time and the quality of the reconstructed image. While our work is motivated by optical CT, the results can be extended more generally to CT. A collection of 21 algorithms for solving the image reconstruction problem were evaluated. Specifically, algorithms using (i) superiorization techniques and (ii) regularization to avoid overfitting were compared. Multiple test problems are investigated using 18 different image phantoms, parallel-beam and fan-beam system matrices, and varying noise levels. Comparison of the algorithms is done using performance profiles on three different performance measures. The results for both the synthetic and clinical test problems show that there is not one single algorithm outperforming all others, but instead a set of top algorithms that give the best values on the performance profiles. When qualitative analyses such as reliance on stopping conditions, number of input parameters, and run time are also considered, FISTA-TV shows slight advantages over the other top algorithms. There is a set of top algorithms that all show good results in the performance profiles with a mix of superiorized and regularized model algorithms. As to which of these top algorithms outperforms the rest is undetermined and further research needs to beinvestigated.

A hybrid ant colony–particle swarm optimization method (ACOPSO) for aerospace propulsion systems

PurposeThe purpose of this paper is to introduce a hybrid, metaheuristic, multimodal and multi-objective optimization tool that is needed for aerospace propulsion engineering problems.Design/methodology/approachMulti-objective hybrid optimization code is integrated with various benchmark and test functions that are selected suitable to the difficulty level of the aero propulsion performance problems.FindingsAnt colony and particle swarm optimization (ACOPSO) has performed satisfactorily with benchmark problems.Research limitations/implicationsACOPSO is able to solve multi-objective and multimodal problems. Because every optimization problem has specific features, it is necessary to search their general behavior using other algorithms.Practical implicationsIn addition to the optimization solving, ACOPSO enables an alternative methodology for turbine engine performance calculations by using generic components maps. The user is flexible for searching various effects of component designs along with the compressor and turbine maps.Originality/valueA hybrid optimization code that has not been used before is introduced. It is targeted use is propulsion systems optimization and design such as Turboshaft or turbofan by preparing the necessary engine functions. A number of input parameters and objective functions can be modified accordingly.

A deep-learning-based prediction method of the estimated ultimate recovery (EUR) of shale gas wells

The estimated ultimate recovery (EUR) of shale gas wells is influenced by many factors, and the accurate prediction still faces certain challenges. As an artificial intelligence algorithm, deep learning yields notable advantages in nonlinear regression. Therefore, it is feasible to predict the EUR of shale gas wells based on a deep-learning algorithm. In this paper, according to geological evaluation data, hydraulic fracturing data, production data and EUR evaluation results of 282 wells in the WY shale gas field, a deep-learning-based algorithm for EUR evaluation of shale gas wells was designed and realized. First, the existing EUR evaluation methods of shale gas wells and the deep feedforward neural network algorithm was systematically analyzed. Second, the technical process of a deep-learning-based algorithm for EUR prediction of shale gas wells was designed. Finally, by means of real data obtained from the WY shale gas field, several different cases were applied to testify the validity and accuracy of the proposed approach. The results show that the EUR prediction with high accuracy. In addition, the results are affected by the variety and number of input parameters, the network structure and hyperparameters. The proposed approach can be extended to other shale fields using the similar technic process.

Sensitivity analysis of queueing models based on polynomial chaos approach

Queueing systems are modeled by equations which depend on a large number of input parameters. In practice, significant uncertainty is associated with estimates of these parameters, and this uncertainty must be considered in the analysis of the model. The objective of this paper is to propose a sensitivity analysis approach for a queueing model, presenting parameters that follow a Gaussian distribution. The approach consists in decomposing the output of the model (stationary distribution of the model) into a polynomial chaos. The sensitivity indices, allowing to quantify the contribution of each parameter to the variance of the output, are obtained directly from the coefficients of decomposition. The proposed approach is then applied to M/G/1/N queueing model. The most influential parameters are highlighted. Finally several numerical and data examples are sketched out to illustrate the accuracy of the proposed method and compare them with Monte Carlo simulation. The results of this work will be useful to practitioners in various fields of theoretical and applied sciences.

Is less more? Experimenting with visual stacking of coincident maps for spatial global sensitivity analysis in urban land-use change modeling

Visualization of the outputs from sensitivity analyses of spatially explicit models can potentially enhance the perception of the uncertainty in model outputs. However, spatial global sensitivity analysis (GSA) produces multiple output maps, twice as many as the number of input parameters under investigation, which can result in an overwhelming visual load to interpret. In this research, we tested the visual stacking of coincident maps to handle the visual complexity for an application of an urban land-use change model. The coincident maps were compared with the adjacent representation. Both types of maps were tested using a web-based survey to understand the efficacy in supporting the comprehension of the GSA results. The majority of the questions showed no statistically significant difference between visualization techniques; however, the difficulty of interpreting the adjacent maps was observed among novice users which suggesting the need for an update for the future representations using coincident maps.

Comparison of different machine learning algorithms for predicting air-conditioning operating behavior in open-plan offices

Air conditioning (AC) operating behavior has a significant influence on the energy consumption of buildings. Machine learning (ML) methods have recently been developed and have shown a high performance in predicting occupant behaviors. However, owing to a lack of comparison of different ML algorithms based on the same database and evaluation indicators, it is difficult to evaluate the positives and disadvantages of different ML algorithms in this research field. To address this gap, in this study, five ML algorithms (extreme gradient boosting, logistic regression, random forest, support vector classification, and k-nearest neighbor) were applied to predict the AC operating behavior under the same standard. The exhaustive method was applied to the five algorithms to detect the application of ML algorithms in different offices. The coupling relationship between the importance ranking method and ML algorithms was also discussed. The predicted results were evaluated using two indices, the open rate (OR) and F1 score. The results revealed that for all five algorithms in two different offices, a ΔOR within ± 5% and an F1 score of larger than 0.8 were achieved. Therefore, from the perspective of the two indices, the five algorithms showed similar performances, although they required different input numbers and parameter combinations. The results also showed that to reach the minimum abs(ΔOR) and the maximum F1 score, the corresponding ML algorithm and input parameters were inconsistent. When selecting the appropriate ML algorithm, determining the indicators that the simulation focuses on might be a key prerequisite. Influenced by the combination of parameters and algorithm chosen, the number of input parameters and model accuracy were not positively correlated. Meanwhile, for predicting the AC operating behaviors, it was difficult to find an important ranking method suitable for all algorithms. In addition, some frequent start-and-stop ACs were detected in the simulated AC on–off profiles, which indicated certain limitations of the ML algorithms.

Alternative Initial Probability Tables for Elicitation of Bayesian Belief Networks

Bayesian Belief Networks are used in many fields of application. Defining the conditional dependencies via conditional probability tables requires the elicitation of expert belief to fill these tables, which grow very large quickly. In this work, we propose two methods to prepare these tables based on a low number of input parameters using specific structures and one method to generate the table using probability tables of each relation of a child node with a certain parent. These tables can be used further as a starting point for elicitation.

Model parameter prediction of lumped plasticity model for nonlinear simulation of circular reinforced concrete columns

This paper presents the model parameter estimation of a lumped plasticity model to realistically simulate the nonlinear load-deformation response of circular reinforced concrete columns under cyclic lateral loading. The model parameter calibration is based on an experimental database consisting of 210 circular columns with various input parameters, such as material strength, reinforcement layout, specimen geometry, and test configuration. The model parameter values for the initial stiffness, plastic rotation capacity, moment strength, and cyclic damage parameters are calibrated to the first-cycle envelope of each set of test data. Empirical predictive equations are developed to estimate the model parameters as functions of the input parameters using four different regression techniques: stepwise, ridge, lasso, and elastic net regression. The equations developed with lasso regression provided a reasonable trade-off between the prediction error and the number of input parameters. Important input parameters that affect the model parameters are the volumetric transverse reinforcement ratio, spacing of confinement steel, and axial load. As an application of the proposed column model, seismic fragilities of bridge columns in two bridge classes with different interior bent type are compared based on an existing distributed plasticity model and the proposed lumped plasticity model. The seismic vulnerability prediction discrepancy between the proposed and existing models tend to increase as the level of ground motion intensity increases. The prediction trend between the two models may differ by bent configuration and deck mass. The proposed column model reduces the seismic vulnerability of one-column bent bridge class, but increases the vulnerability of two-column bent bridge class.

Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization.

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

Development of a Daylight Simulation Software for Early Design Stage: A Case Study of a Container House

Daylight simulation software play a significant role in decision-making during the architectural design process. These software enable architects to make informed decisions about daylight performance in the early phases of the project. Radiance as a simulation engine provides a physically accurate model of the lighting conditions in the scene, thus capable of achieving a high degree of accuracy and validity. However, with a command line interface, it requires a large number of input parameters, which complicates modelling and restricts flexibility. When integrated in the early design process, this approach becomes less efficient, or requires enormous computation time. This paper introduces a daylight simulation software named DaylightX that uses the backward raytracing algorithm implemented on the GPU to calculate the spatial distribution of daylight across the space. The tool aims to enhance the capabilities of the simulation environments through a user-friendly interface and thus allow for flexibility in the design process. An individual residential unit - a shipping container - was used as a base case for evaluation. The modular design of shipping containers allows for flexibility in window placement and orientation, but requires careful design and implementation from daylight performance perspective. The results of the study can serve to improve the daylight performance of container housing units.

Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin

This study compares LSTM neural network and wavelet neural network (WNN) for spatio-temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic basins. Using long-term in situ observed data for 30 years (1980–2009) from ten rain gauge stations and three discharge measurement stations, the rainfall and runoff trends in the Nzoia River basin are predicted through satellite-based meteorological data comprising of: precipitation, mean temperature, relative humidity, wind speed and solar radiation. The prediction modelling was carried out in three sub-basins corresponding to the three discharge stations. LSTM and WNN were implemented with the same deep learning topological structure consisting of 4 hidden layers, each with 30 neurons. In the prediction of the basin runoff with the five meteorological parameters using LSTM and WNN, both models performed well with respective R2 values of 0.8967 and 0.8820. The MAE and RMSE measures for LSTM and WNN predictions ranged between 11–13 m3/s for the mean monthly runoff prediction. With the satellite-based meteorological data, LSTM predicted the mean monthly rainfall within the basin with R2 = 0.8610 as compared to R2 = 0.7825 using WNN. The MAE for mean monthly rainfall trend prediction was between 9 and 11 mm, while the RMSE varied between 15 and 21 mm. The performance of the models improved with increase in the number of input parameters, which corresponded to the size of the sub-basin. In terms of the computational time, both models converged at the lowest RMSE at nearly the same number of epochs, with WNN taking slightly longer to attain the minimum RMSE. The study shows that in hydrologic basins with scarce meteorological and hydrological monitoring networks, the use satellite-based meteorological data in deep learning neural network models are suitable for spatial and temporal analysis of rainfall and runoff trends.

Parameter Anomaly Identification Model About Supervisory Control And Data Acquisition

With the rapid progress of sensor technology, state monitoring of wind turbines is more comprehensive, the amount and type of the sensor used will be more, the state parameters of wind turbines will be more. According to the status indicators to parameter index of target parameters selector model is established in this paper, and the number of input parameters are reduced on the basis of the guarantee accuracy. The two sub-models constitute the abnormal identification model of the working condition index. Finally, the model is analyzed by an example to verify the accuracy and effectiveness of the model. The research in this paper on abnormal discrimination of working condition parameters and evaluation methods of wind turbines is an important premise and basis for making scientific and reasonable operation and maintenance decisions of wind farms, and it has important academic significance.

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical model for planning the deployment of SDN smart backup controllers (SBCs) to preserve service in the presence of DDoS attacks. Given a number of input parameters, our model has two distinct capabilities. First, it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions. Second, it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks. The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters. Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

A METHOD FOR STUDY OF NUCLEAR FUEL CYCLE SCENARIOS WITH AN INTENSIVE ELECTRICITY PRODUCTION DECLINE IN A SHORT PERIOD

Nuclear fuel cycle scenario study can be of benefit for decision-making in nuclear industry development. However, due to the lack of knowledge about the future, many scenario studies are subject to uncertainties. As a result, some parameters may be disrupted. Energy production of an entire nuclear fleet of interest is such a parameter. Its disruptive decline has a strong impact on the results of scenario studies. Resilience study against disruption of decline in energy production is required to anticipate possible failures of scenario studies. In such a resilience study, a large number of scenario trajectory simulations with different modes of decline are expected. It is too difficult to set the values of all scenario parameters in each trajectory manually. As a solution, a method is proposed in this paper to reduce the number of input parameters. A set of rules has been implemented as an intermediate layer between the decision-makers and the scenario code to drive the evolution of a nuclear fleet. In this case, a trajectory can be fully characterized by a few parameters. This method has been applied to a simplified academic nuclear fleet with two different modes of decline in energy production. The simulation results showed that the developed method is feasible. One should note that the scenario model in this paper is only used for academic purposes and does not correspond to any industrial strategy or policy.

ТЕХНІЧНА ДІАГНОСТИКА БЕЗПРОВІДНИХ КАНАЛІВ СТАНДАРТУ 802.11

The main task when designing 802.11 wireless networks is to create access with the highest possible channel bandwidth for an individual subscriber. This problem is solved in two ways: calculation and test diagnostics of networks at the design stage; optimization of networks during operation based on technical diagnostics and monitoring. This paper investigates and clarifies the existing terminology for technical diagnostics and control for 802.11 wireless channels. As a result, it is established that radio monitoring is the first stage of technical diagnostics on the basis of which it is possible to obtain statistical characteristics about the change of channel parameters over time. The second stage includes models and methods of analysis and evaluation of channel parameters, considering the influence of various destabilizing factors with control operations. Based on the obtained terminology, a structural-consequential diagnostic model was proposed for the 802.11 wireless channel, which provides a formalized description of the object needed to solve the problems of diagnosis and control. The model uses the general structure of the channel and physical layer of the 802.11 standard and energy and information diagnostic parameters, which is valid for any wireless channel. The level of reliability of technical diagnostics of wireless channels is provided due to the built-in means of monitoring of subscriber devices, the number of input parameters and the observation period. In addition, an assessment of the suitability of the channel for traffic transmission is provided, which involves comparing the bit rate with the obtained technical diagnostic data. This is relevant in the current trends of improving the quality of modern infocommunication services, which significantly increases the bandwidth requirements of channels. The proposed model of technical diagnostics is a concept of methodology. It includes the use of: improved and new developed by the author's own methodological approaches to conducting research on wireless channels; acquired new knowledge about the operation of these channels under the influence of phenomena existing in the transmission medium; new models and methods for estimating energy and information parameters and the relationships between them; methods of evaluation and statistical processing of theoretical and empirical research.

A Novel Equivalent Continuum Approach for Modelling Hydraulic Fractures

Hydraulic fracturing has transitioned into widespread use over the last few decades. There are a variety of numerical methods available to simulate hydraulic fracturing. However, most current methods require a large number of input parameters, of which the values of some parameters are poorly constrained. This paper proposes a new method of modelling the hydraulically fractured region using void-ratio dependent relation to define the permeability of the fractured region. This approach is computationally efficient and reduces the number of input parameters. By implementing this method with an equivalent continuum representation, uncertainties are reduced arising from heterogeneity and anisotropy of earth materials. The computational efficiency improves modelling performance in stress sensitive zones such as in the vicinity of the injection well or near faults.

Sensitivity analysis of the 1-D SFR thermal stratification model via discrete adjoint sensitivity method

This paper presents a parameter sensitivity analysis on a thermal stratification (TS) model by using the discrete sensitivity method. The TS model was recently developed in our research group to efficiently predict the TS phenomenon in pool-type Sodium-cooled Fast Reactors. The fluid temperature gradient was considered as the figure of merit in the sensitivity analysis because it best characterizes the thermal stratification phenomenon. The sensitivities of the fluid temperature gradient with respect to four different parameters were investigated, including jet volumetric flow rate Qjet, jet temperature Tjet, heat capacity of the ambient fluid Cp,amb, and static thermal conductivity of the ambient fluid kc,amb. The sensitivity analysis was conducted through both the conventional forward sensitivity method and the advanced adjoint sensitivity method, which is more effective in cases where the number of outputs is small and the number of input parameters is large.The sensitivities obtained in this study suggested that perturbations in Qjet, Cp,amb, and kc,amb could introduce either positive or negative changes to the temperature gradient, depending on the axial location and the elapsed time of the experiment. However, an increase in Tjet always decreased the temperature gradient. Moreover, the impact of Tjet on the maximum temperature gradient was several times higher than that of the other three parameters, which indicated that additional attention may need to be paid to the occurrence of thermal stratification in the sodium pool when the impinging jet has a large temperature change. This study also provides a step-by-step example for the application of the discrete adjoint sensitivity method to the time-dependent nonlinear systems.