Model Combination Method Research Articles

Construction of Solid-Liquid Two-Phase Flow and Wear Rate Prediction Model in Multiphase Pump Based on Mixture Model-Discrete Phase Model Combination Method

Blade wear is the critical problem in the operation of multiphase pump. This paper presents a numerical study of the multiphase flow of multiphase pump. The trajectory of particles in the pump is calculated by the discrete phase model. Then, the simulation results are compared with the model test results of the pump to verify the correctness of the simulation method. The results show that the particles in the impeller domain are mainly near the hub, and the particles in the diffuser domain form a agglomerated area in the middle of the flow channel. The average wear rate of the impeller is more affected by the particle size than that of the diffuser. The maximum wear rate of blade surface increases first and then decreases with the increase of particle size. According to the wear data under different particle sizes, the regression model between particle size and wear rate is fitted to predict the wear of mixed transport pump in actual operation. The research results have important reference value for the prediction of the wear performance of the multiphase pump.

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions.

Propagating uncertainty about molecular evolution models and prior distributions to phylogenetic trees

Phylogenetic trees constructed from molecular sequence data rely on largely arbitrary assumptions about the substitution model, the distribution of substitution rates across sites, the version of the molecular clock, and, in the case of Bayesian inference, the prior distribution. Those assumptions affect results reported in the form of clade probabilities and error bars on divergence times and substitution rates. Overlooking the uncertainty in the assumptions leads to overly confident conclusions in the form of inflated clade probabilities and short confidence intervals or credible intervals.This paper demonstrates how to propagate that uncertainty by combining the models considered along with all of their assumptions, including their prior distributions. The combined models incorporate much more of the uncertainty than Bayesian model averages since the latter tend to settle on a single model due to the higher-level assumption that one of the models is true. Nucleotide sequence data illustrates the proposed model combination method.

Large Uncertainties Are Pervasive in Long-Term Frequency-Magnitude Relationships for Volcanoes in Southeast Asia

Frequency-magnitude relationships are a fundamental aspect of volcanic hazard and risk analysis. Typically, frequencies of previously recorded eruptions are used to identify such relationships. This works well for volcanoes that are well-studied, but it can take a long, sustained, and resource intensive research effort to compile complete eruption records. Further, the level of completeness that can be achieved will vary around the world as a function of climatic conditions, eruption style, and duration of written records. Given the importance of understanding hazard and risk for disaster risk reduction, how can frequency-magnitude relationships be determined for volcanoes with little or no eruption records? Analogue models have been used to supplement the eruption records of volcanoes with limited or no recorded eruptions. However, there has been little effort undertaken to compare the agreeability of different approaches to estimating frequency-magnitude relationships using these analogue models. This has implications for volcanic hazard and risk assessment, if different approaches are considered credible, yet yield vastly different estimates. In this work we compare frequency-magnitude relationships for volcanoes in Southeast Asia, a region where eruption records are known to be very incomplete. We do this by first reviewing published frequency-magnitude relationships to evaluate the agreeability between different approaches, and then develop a top-down multi-model Bayesian updating approach to deriving frequency-magnitude relationships for a wide variety of volcanoes in Southeast Asia (n = 176). Our review of published estimates found that there is considerable variability between published eruption probabilities for volcanoes in Southeast Asia. We also found that using different analogue models in the Bayesian analysis can lead to considerably different frequency-magnitude relationships (over an order of magnitude in some cases), highlighting the importance of using multiple models to ensure robust probability estimations are obtained. Sensitivity analysis demonstrated that the choice of model averaging or model combination method can influence the resulting frequency-magnitude estimations, whilst the choice to incorporate or remove uncertain eruption records had modest or no effect. Our findings provide important considerations for estimating frequency-magnitude relationships in volcanic hazard and risk assessments, and a method to combine multiple frequency-magnitude models whilst accounting for uncertainty in our estimations.

Combining Predictions of Auto Insurance Claims

This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study the effects of combining different methods using five measures of prediction accuracy. The results show the following. First, when there is an outstanding (in terms of Gini Index) prediction among the candidates, the “forecast combination puzzle” phenomenon disappears. The simple average method performs much worse than the more sophisticated model combination methods, indicating that combining different methods could help us avoid performance degradation. Second, the choice of the prediction accuracy measure is crucial in defining the best candidate prediction for “low frequency and high severity” (LFHS) data. For example, mean square error (MSE) does not distinguish well between model combination methods, as the values are close. Third, the performances of different model combination methods can differ drastically. We propose using a new model combination method, named ARM-Tweedie, for such LFHS data; it benefits from an optimal rate of convergence and exhibits a desirable performance in several measures for the Kangaroo data. Fourth, overall, model combination methods improve the prediction accuracy for auto insurance claim costs. In particular, Adaptive Regression by Mixing (ARM), ARM-Tweedie, and constrained Linear Regression can improve forecast performance when there are only weak learners or when no dominant learner exists.

Comprehensive evaluation of ecosystem health in pastoral areas of Qinghai–Tibet Plateau based on multi model

In order to solve the problem that the existing ecosystem health assessment methods are affected by many uncertain factors, resulting in inaccurate assessment results, a comprehensive assessment method of ecosystem health in pastoral areas of Qinghai–Tibet Plateau was proposed based on multi model combination method, taking pastoral areas of Qinghai–Tibet Plateau as an example. Remote sensing technology is introduced to obtain the relevant information of the study area and analyze the ecological and environmental problems of the Qinghai–Tibet Plateau. The remote sensing image of plateau pastoral area is corrected and fused, and the multi model is established by using the common Kriging interpolation method, and the response evaluation index system is obtained. Analysis of forest vegetation coverage, temperature vegetation drought index, leaf area index, vegetation primary productivity, surface temperature and precipitation, calculation of index weight, complete the design of evaluation method. The experimental results show that the designed evaluation method is closer to the actual situation, which verifies the effectiveness of the evaluation method. • Image preprocessing of Qinghai Tibet Plateau Using Remote Sensing Technology. • The common Kriging interpolation method is used to establish the multi index model. • The weight of ecosystem health evaluation index was calculated. • The results show that the design method is consistent with the actual situation.

Estimation of Suspended Sediment Load Using Artificial Intelligence‐Based Ensemble Model

Suspended sediment modeling is an important subject for decision‐makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence‐ (AI‐) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI‐based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed‐forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two‐year (2016‐2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI‐based ensemble in particular provide excellent performance in SSL estimation.

A new model for evaluating sustainable utilization of coastline integrating economic output and ecological impact: A case study of coastal areas in Beibu Gulf, China

Evaluation on sustainability of coastline utilization was one of the hot topics in marine environmental science. Based on high-resolution remote sensing images, coastlines can be divided into natural coastlines and artificial coastlines. Natural coastlines are mainly converted to harbor coastline and mariculture coastline. In this study, a new comprehensive model was proposed to assess the sustainability on utilization of coastline and applied to a typical case of Beibu Gulf, China. Comprehensive economic output efficiency (EOE) of artificial coastlines was obtained by integrating the contribution weight and applying linear combination model. In addition, the P–S-R framework integrated with fuzzy mathematics model and linear combination method was constructed to evaluate the disturbance intensity of human activities on the natural coastlines (DI) by taking petrochemical plants, ports, sewage outlets and aquaculture intensity as pressure indicators, seawater quality status near the coastline as state indicators, and whether the coastline was within nature reserve boundaries as the response indicator. Rely on the EOE and DI, the cities involved in the evaluation were finally categorized into four groups: group 1 with high economic output efficiency and low ecological impacts, group 2 with high economic output efficiency and high ecological impacts, group 3 with low economic output efficiency and low ecological impacts, group 4 with low economic output efficiency and high ecological impacts. The results showed that: (1) natural coastlines retention ratios were 52.45%, 57.97% and 76.15% in Guangdong, Guangxi and Hainan Province respectively. In addition, 8.66%, 10.47% and 5.17% of the coastlines with high disturbance from human activities in Guangdong, Guangxi and Hainan Province respectively were identified; (2) only 4 of 29 cities were categorized into the group with high economic output and low ecological impacts, indicating that most of the coastal cities in Beibu Gulf of China was at a relatively low level of sustainability. The proposed indices system and the integrated analysis framework were proved to be feasible for evaluating sustainable utilization of coastlines, which can be applied to other case studies. The results of evaluation provided valuable information for decision-makers to optimize coastal land use policy targeting at the balance of development and conservation.

Pemanfaatan Teknologi Unmanned Aerial Vehicle (UAV) untuk Pemetaan Kadaster

Abstract: Geospatial data (on horizontal and vertical positions) play an important role in decision making.Regarding that, the issue of funds, diversity of areas, human resources, and non-updated data become great obstacles. This study aims to examine the technological capabilities of Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft in acquiring and updating geospatial data, evaluating and inspecting changes in the conversion of agricultural land and the environment, as well as describing orthophoto extraction opportunities in digital services based on multipurpose cadastre. This research used a model combination method (concurrent triangulation). Several alternative requirements were derived to design methods of mapping, processing and controlling the quality of application to the concept of Fit For Purpose Land Administration (FFP-LA), and layering with Geographic Information Systems (GIS). The final product of this system is effective to produce and update land base map, evaluate and inspect changes in the conversion of land functions as well as facilitate digital services. The availability of open source software efficiently extracts and combines terrestrial and photogrammetric mapping products in real time and has the opportunity to realize multipurpose cadastre. The involvement of local human resources is applied to ensure legal certainty in the land registration system as well as to improve and strengthen the management of agrarian resourcesAbstract: Geospatial data (on horizontal and vertical positions) play an important role in decision making.Regarding that, the issue of funds, diversity of areas, human resources, and non-updated data become great obstacles. This study aims to examine the technological capabilities of Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft in acquiring and updating geospatial data, evaluating and inspecting changes in the conversion of agricultural land and the environment, as well as describing orthophoto extraction opportunities in digital services based on multipurpose cadastre. This research used a model combination method (concurrent triangulation). Several alternative requirements were derived to design methods of mapping, processing and controlling the quality of application to the concept of Fit For Purpose Land Administration (FFP-LA), and layering with Geographic Information Systems (GIS). The final product of this system is effective to produce and update land base map, evaluate and inspect changes in the conversion of land functions as well as facilitate digital services. The availability of open source software efficiently extracts and combines terrestrial and photogrammetric mapping products in real time and has the opportunity to realize multipurpose cadastre. The involvement of local human resources is applied to ensure legal certainty in the land registration system as well as to improve and strengthen the management of agrarian resources

The effect of Cikarang Bekasi Laut Project to fisherman livelihood

The purpose of this research is to evaluate the main effect of inland waterways, Cikarang Bekasi Laut (CBL) and Boro Boro project at Huripjaya estuaries, Bekasi. The purpose of this study, is to see the relationships between coastal fishermen and local investors and their workers as a migraine in managing marine resources, thus resulting in a decrease in fish catches by fishermen. This study uses a concurrent embedded model combination method (unbalanced mixture) where qualitative research methods as the primary method and quantitative methods as a secondary method. The purpose of using this combination method is that the data will be more complete, meaningful and accurate. The results of the study, based on the results of in-depth interviews and focused discussions, concluded that there was no agreement between the interests of traditional fishermen and project managers. It was shown that he did not pay attention to the fate of fishermen, but lately, information from the Head of the Kadus Dua had begun to record about 100 fishermen as prospective workers in one of the Cikarang Listriklindo projects. However, for other traditional fishermen do not know their fate, so the involvement of the local government.

The Optimization Management of Combination Research Methods in Improving the Quality of Writing Research Reports

Management in writing research reports is a crucial activity for a researcher who wants to publish his research results. The research method is a method used to get results and discussion of research. In this study, the research method was used to explain the characteristics of quantitative, combination and qualitative methods with as many as 12 (twelve) points, and literature studies on the definition of 4 (four). The core of this study discusses management of the philosophical foundation of combination research, the characteristics of quantitative, qualitative and combination methods. Then discussed also the definition of combination methods, the nature of the combination method, variations of combination methods, combination research methods models: combination model or sequential explanatory design, combination model or sequential exploratory design, combination model or concurrent triangulation design and model combination method or concurrent embedded design. By doing this research, it is hoped that it can add insight to the authors of research reports regarding the management of combination research methods.

Bone- and air-conduction speech combination method for speaker recognition

In this paper, first, we report speaker recognition performance using bone-conduction speech based on an i-vector-based speaker recognition system, which is the current state-of-the-art method. In addition, we propose three speaker recognition methods combining bone-conduction speech and air-conduction speech: a feature combination method, a speaker model combination method, and a similarity score combination method. To evaluate the proposed methods, we conducted speaker recognition experiments using a part of a large speech corpus constructed by the National Research Institute of Police Science, Japan. Experimental results show the bone-conduction speech performs almost the same as the air-conduction speech when the enrolment data and evaluation data are collected in the same session. In addition, all proposed methods improved the speaker recognition performance of air- and bone-conduction speech in the experiments. From these results, we conclude that fusing air- and bone-conduction speech improves the speaker recognition performance.

The effect of auditor quality to professional skepticsm and its relationship to audit quality

PurposeThe purpose of this paper is to investigate the relationships between auditor quality to professional skepticism and between auditor quality and professional skepticism to audit quality.Design/methodology/approachThe analysis method to test the causal effect of auditor quality to profesional skepticism and audit quality. The respondent in this research is the auditor in the Audit Board of the Republic of Indonesia in South Sulawesi province using questionnaire. The analysis tool used in this research is partial least square.FindingsThe auditor quality has direct effect on the professional skepticism. Professional skepticism has direct effect on the audit quality. The auditor quality has no direct effect on audit quality, but auditor quality has indirect effect on audit quality with mediation of professional skepticism.Originality/valueThis paper shows a research was conducted about professional skepticism public sector in governmental sector for producing the audit quality, especially in South Sulawesi province in Indonesia. This research retests the research result from Aranya and Amernic (1981); Carcello et al. (1992); Behn et al. (1997); Copley (1998); Brown and Raghunandan (1995); Beasley et al. (2001); Chiu (2003); Suraida (2005); Lewinsohn et al. (1997); Novianti (2008); Varelius (2009). The researcher uses model combination (design) method of sequential explanatory based on evidentiary sequence from Creswell 2009, quantitative research (by instrument using questionnaire).

Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model

ABSTRACTTropical cyclone (TC) disaster loss assessment is an important and difficult problem in TC prevention and disaster mitigation. Few studies have focused on combined model in this area. This study introduced a new combination model method to predict TC disaster loss, taking Guangdong province as an example. We analysed and collected 67 TC data from 1993 to 2009, which had impact on Guangdong province, in which 60 were randomly for training data and another 7 were for testing data. We conducted three models – GA–Elman neural networks, support vector regression (SVR) and generalized regression neural networks (GRNN), and the root mean square error (RMSE) value we got are 5.05, 7.85 and 3.82, respectively. Then the three models are combined into a comprehensive evaluation model by model combination method. The RMSE of the test results is 3.30. The results show that the combined model is superior to one individual model and it is a more accurate and stable method.

A Novel Approach Research on Chinese Language Model Fusion Based on RNN

In the light of the poor ability to describe the long-distance information of a sentence and the serious data sparse phenomenon of the mainstream n-gram language model, the RNN modeling method which can capture the inherent rules of natural language better and overcome the inadequacy of n-gram model was firstly used for Chinese language. To further improve the model performance, a model combination method was introduced so that RNN and the n-gram model can be merged together respectively. This algorithm can take full advantage of each model combining the language score information of the confused network marked by different models. The experiment results show that the RNN language model has a great superiority with better modeling performance. Meanwhile, with the combination of the models, the system recognition rate increases effectively on the task of Chinese telephone speech recognition.

A Complex Event Model Combination Method Based on Multiple Tuples

The combination problem of complex event model is an important problem in complex event study. Aiming to solve the problem of no well reflecting the spatial-temporal characteristic in current comeplex even models, which are combined by atomic events based on time or space or time and space, a multi-tuple combination method of complex event model based on object, time, space, events and properties is proposed in this paper based on the analysis in existing complex event model.The basic idea of the method is to make use of intrinsic relation existing in multi tuple of event model to combine and express complex event. At a result, our supposed model not only can more comprehensively reflect the spatial-temporal characteristic of event, but also can reflect some dynamic change conditions occurred in the event. Smart home temperature and humidity detection as a case and experimental simulation are used to illustrate and prove the correctness and feasibility of the proposed complex event method based on multiple tuple.

Research on Service Model Combination Method Based on Colored Petri Net

Model-driven architecture and service-oriented architecture are popular software development methods at the present, these two methods have their own advantages and disadvantages, the proposed SOA-based Model-driven Rapid Development Architecture by combining these two methods, Using colored Petri nets’ tools to study service composition methods specifically, and described five basic logical structure of service composition through formal methods, then gave out the construction method of composite services, finally, verified the effectiveness and safety of the service composition method through the modeling.

Three-dimensional fluorescence spectra model optimisation for water quality analysis based on particle swarm optimisation

In this paper, a model combination method is proposed to improve the model precision of water quality analysis with three-dimensional 3D fluorescence spectra. The key to successful model combination is the selection of sub-models, which also means selection of excitation wavelength for 3D fluorescence instrument miniaturisation. A particle swarm optimisation PSO algorithm is designed to select effective sub-models, in which the combinational model is built. Field samples from surface water and urban wastewater are used as research objects. Following the proposed PSO method, three excitation wavelengths were selected, and the corresponding sub-models were linearly combined to an optimised combinational model. The experimental results showed that the root mean square errors of prediction of the combinational model decreased significantly, whether compared with the sub-models having the best prediction precision or the combinational models without sub-models selection.

Availability Optimization for Repairable n-Stage Standby System by Applying Tabu-GA Combination Method

This study aims at the availability optimization problem for n-stage standby system under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related costs during system planned life cycle. In general, system availability can be improved by increasing component reliability level, component repair rate or the number of components in each subsystem. This proposed model combines Tabu method with Genetic Algorithm to solve system availability optimization problem. Through our method applied in the initial phase of system design and development, we can find the optimal allocation of component redundancy number, reliability and maintainability levels to minimize the total system cost under different configuration constraints such as weight, volume, and system availability requirements. The proposed numerical examples are demonstrated based on different system constraint requirements and parameter values. Through a simulation study of 30 times of calculations by applying this proposed combination method, stable results are clearly showed. Consequently, all numerical results and simulation studies clearly show that the proposed availability optimization model and Tabu-GA combination method developed in this paper can help solving system availability optimization problem in the initial system design and development period.

A Novel Mask Estimation Method Employing Posterior-Based Representative Mean Estimate for Missing-Feature Speech Recognition

This paper proposes a novel mask estimation method for missing-feature reconstruction to improve speech recognition performance in various types of background noise conditions. A conventional mask estimation method based on spectral subtraction degrades performance, due to incorrect estimation of the noise signal which fails to accurately represent the variations of background noise during the incoming speech utterance. The proposed mask estimation method utilizes a Posterior-based Representative Mean (PRM) estimate for determining the reliability of the input speech spectral components, which is obtained as a weighted sum of the mean parameters of the speech model using the posterior probability. To obtain the noise-corrupted speech model, a model combination method is employed, which was proposed in our previous study for a feature compensation method. Experimental results demonstrate that the proposed mask estimation method provides more separable distributions for the reliable/unreliable component classifier compared to the conventional mask estimation method. The recognition performance is evaluated using the Aurora 2.0 framework over various types of background noise conditions and the CU-Move real-life in-vehicle corpus. The performance evaluation shows that the proposed mask estimation method is considerably more effective at increasing speech recognition performance in various types of background noise conditions, compared to the conventional mask estimation method which is based on spectral subtraction. By employing the proposed PRM-based mask estimation for missing-feature reconstruction, we obtain +23.41% and +9.45% average relative improvements in word error rate for all four types of noise conditions and CU-Move corpus, respectively, compared to conventional mask estimation methods.