Estimation Of Distribution Research Articles

Prediction of the efficient heat flux to get an accurate thermal and clad geometry characteristic during laser cladding

The prediction of the accurate thermal and clad geometry characteristics plays a significant role in obtaining a better metallurgical bond between the cladding material and the substrate. The estimation of temperature distribution is difficult experimentally within the melt pool. A suitable and accurate heat flux is needed to apply during the numerical modeling to get clear and accurate process parameters before conducting the actual experiments. Therefore, different heat flux is used to get the best-suited heat flux which gives an accurate response during the laser cladding. The same processing parameters were considered during the simulation for the different heat sources. In all the cases, the maximum temperature was obtained at the center of the melt pool and decreased when moving away from the center of the melt pool in-depth and across the scanning speed direction. A similar shape of the melt pool is formed when three-dimensional volumetric Gaussian heat flux and conical heat flux are used, which gave best suited and efficient heat flux for the numerical simulation before actually conducting experiments. When egg-shaped heat flux is applied, an insufficient temperature was reached at the interface of the powder and substrate, whereas more dilution appeared for parabolic heat flux.

EEG based emotion recognition by hierarchical bayesian spectral regression framework

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition.

An efficient mixed-variable generation operator for integrated energy system configuration optimization

Energy consumption has skyrocketed as society has progressed, resulting in energy crises and environmental issues. New energy technologies are being developed to address these challenges, and technologies such as the cogeneration of electricity, heating, and cooling are being used to improve energy utilization. The configuration of an energy system has a critical impact on the economics of the system, its ability to supply energy, etc. This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system. The model considers equipment types (categorical variables), equipment quantities (integer variables), and critical parameters (real variables) as optimization variables, where economic performance, the proportion of renewable energy, supply shortage, and dependence on the external power grid are considered unoptimized objectives. This is a multiobjective and mixed-variable problem, and the combination of the two properties poses an excellent challenge for evolutionary algorithms in optimizing the model. Therefore, we propose an efficient generating operator based on a fully connected weighted neural network, called the FCWN operator. This operator overcomes the challenge of the discrete variable space and broken neighborhood relationships in mixed-variable problems. During the algorithm search process, search history information is used to update the fully connected weight network for distribution estimation of the variable space. Offspring are generated based on the fully connected network to improve the algorithm’s search efficiency. In the experimental section, we construct a model with 20 variables and conduct simulation-based configuration optimization for scenarios with 3, 5, and 8 available equipment types. The final obtained Pareto frontier solution set is evaluated using the hypervolume metric, a widely used multiobjective evaluation metric. The Wilcoxon rank sum test on the experimental results shows that the proposed algorithm has better results than other state-of-the-art algorithms at a 95% confidence level.

Overall particle size distribution estimation method based on kinetic modeling and transformer prediction

Estimating the overall particle size distribution (PSD) on the conveyor is one of the most important means of monitoring blast furnace production. Due to the harsh production environment and limited detection conditions, existing online detection methods can only estimate part of the PSD, but it is difficult to predict the overall PSD. Unlike segmentation methods that can only obtain surface PSD, we propose an overall PSD estimation method based on mechanistic modeling and local–global fused prediction networks. First, we constructed a kinetic mechanism model of particle accumulation, from which global field distribution features such as coordination number, moment of inertia, and spatial distribution were extracted. Then, a particle segmentation model is trained using surface images collected by detection means such as cameras to obtain surface PSDs as local surface features. Next, to predict the overall PSD, we propose a Local surface and Global field distribution feature Fusion-based Transformer network (LGFT). Among them, in order to better aggregate local and global features, we introduce orthogonal fusion and point cloud extractors in the encoder. Finally, we verify the accuracy and efficiency of our method through extensive ablation experiments.

Effects of variations in atmospheric temperature and humidity on the estimation of exclusive breastfeeding status using the deuterium oxide dose-to-mother technique.

The deuterium dose-to-mother (DTM) method measures the human milk intake of breastfed children. Recently, the use of this method has been expanded to classify babies objectively as exclusively breast fed (EBF) or not (non-EBF) based on quantification of non-milk oral water intake (NMOI). However, the calculation of NMOI estimates involves atmospheric temperature and humidity. To evaluate the effects of atmospheric temperature and humidity on NMOI calculation and the classification of exclusive breastfeeding. The effect of indoor temperature and relative humidity on NMOI and the estimated prevalence of non-EBF were examined in two existing data sets of DTM in children by varying temperature in the range of 15 to 35°C and relative humidity in the range of 20 to 80% representing the maximum span of indoor conditions expected. Population-level estimates of NMOI distributions were derived using the rstan package for R v2.21.2. The NMOI decreased at a rate of -1.15 g/day per °C increase and at a rate of -1.01 g/day per percent increase in relative humidity; this was due to variations in non-oral water intake from the atmosphere, a component of the calculation of NMOI, which is dependent on temperature and humidity. For the various locations considered, the mean calculated NMOI varied between 24.6 and 53.3 g/day using the same input data. In the mixed-fed sample of babies, the prevalence of non-EBF based on the earlier defined NMOI cut-off of 86.6 g/day was reduced by 19% when relative humidity was increased by 60%. Atmospheric conditions are essential factors in the computation of NMOI, used in the objective classification of babies as exclusively breast fed or not, and should be considered when the DTM method is used to classify exclusive breastfeeding.

The Synergistic Effect of Na2O on Hot Metal Desulfurization Kinetics in CaO-Na2O-SiO2-Al2O3-MgO Slag System

An experimental and modelling study on hot metal desulfurization is considered. The study identifies the thermodynamic and kinetic factors that define the rate of permanent contact reaction in steady flow conditions. The study proceeds from defining the expressions for overall mass transfer rate of desulfurization and uses experimental data to identify the mass transfer coefficients. The study draws an estimate of the model parameter distributions using a real–coded genetic algorithm and a Metropolis Hastings’ algorithm. The results of this study and the literature data indicate that the Na2O content (5–20 wt–%) of the slag has a great potential for improving the desulfurization efficiency via the increase of the rate of the permanent contact reaction. The desulfurization efficiency increased 5 unit–% per unit–% increase in slags Na2O content (below 10 wt–%). This can be implied to potentially decrease the desulfurization reagent consumption in powder injection.

Semi-supervised diagnosis method for coupling faults of key rotating components based on EEMD-KPCA under cross working conditions

To address the challenges of extracting coupled fault features from key rotating components and classifying them under changeable operating conditions, a semi-supervised fault diagnosis method is proposed. First, Ensemble Empirical Mode Decomposition and Kernel Principal Component Analysis are employed to decompose the original coupled fault signals and reduce feature dimensionality. Experiments are conducted on labeled datasets, yielding an average classification accuracy of 92.43%. To further classify unlabeled datasets under various working conditions, a probability distribution estimation function is incorporated and a confidence threshold is set. For unlabeled data with probabilities greater than the confidence threshold, a pseudo-label is added to increase the labeled data quantity. Thus, it makes learning from these unlabeled data possible. A comparison with the other three methods under cross working conditions showcases the superiority of the proposed approach.

Estimation of Continuous Distribution of Iterated Fission Probability Using an Artificial Neural Network with Monte Carlo-Based Training Data

The Monte Carlo neutron transport method is used to accurately estimate various quantities, such as k-eigenvalue and integral neutron flux. However, in the case of estimating a distribution of a desired quantity, the Monte Carlo method does not typically provide continuous distribution. Recently, the functional expansion tally (FET) and kernel density estimation (KDE) methods have been developed to provide a continuous distribution of a Monte Carlo tally. In this paper, we propose a method to estimate a continuous distribution of a quantity in all phase-space variables using a fully connected feedforward artificial neural network (ANN) model with Monte Carlo-based training data. As a proof of concept, a continuous distribution of iterated fission probability (IFP) was estimated by ANN models in two distinct fissile systems. The ANN models were trained on the training data created using the Monte Carlo IFP method. The estimated IFP distributions by the ANN models were compared with the Monte Carlo-based data that include the training data. Additionally, the IFP distributions by the ANN models were also compared with the adjoint angular neutron flux distributions obtained with the deterministic neutron transport code PARTISN. The comparisons showed varying degrees of agreement or discrepancy; however, it was observed that the ANN models learned the general trend of the IFP distributions from the Monte Carlo-based training data.

Morphological analysis and grain size distribution of SnO2 nanoparticles via digital image processing across diverse calcination temperatures.

This study presents a comprehensive image analysis of the SnO2 nanoparticles synthesised through calcination at diverse temperatures, which enables an estimation of grain size distribution (GSD) from field-emission scanning electron microscopy (FE-SEM) images. Even though FE-SEM images could provide us with a lot of information about sample differences, we can learn more and perform a more accurate analysis of them by using quantitative data obtained by our image processing application. The digital image processing techniques used in this research provide a detailed analysis of the nanoparticles' size and shape, enabling a deeper understanding of their unique characteristics. The results reveal the significant impact of calcination temperature on the morphology of the nanoparticles, with changes in grain size and grain size distribution observed at varying temperatures.

A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework.

A multi-objective evolutionary algorithm based on decomposition (MOEA/D) serves as a robust framework for addressing multi-objective optimization problems (MOPs). However, it is widely recognized that the applicability of a fixed offspring-generating strategy in MOEA/D can be limited, despite its foundation in the MOEA/D methodology. Consequently, hybrid algorithms have gained popularity in recent years. This study proposes a novel hyper-heuristic approach that integrates the estimation of distribution (ED) and crossover (CX) strategies into the MOEA/D framework based on the view of successful replacement rate (SSR) and attempts to explain the potential reasons for the advantages of hybrid algorithms. The proposed approach dynamically switches from the differential evolution (DE) operator to the covariance matrix adaptation evolution strategy (CMA-ES) operator. Simultaneously, certain subproblems in the neighbourhood denoted as B(i) employ the Improved Differential Evolution (IDE) operator to generate new individuals for balancing the high evaluation costs associated with CMA-ES. Numerical experiments unequivocally demonstrate that the suggested approach offers distinct advantages when applied to a three-objective test suite. These experiments also validate a significant enhancement in the efficiency (SRR) of the DE operator within this context. The perspectives and experimental findings, with a focus on the Success Rate Ratio (SRR), have the potential to provide valuable insights and inspire further research in related domains.

AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images

The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.

Estimation of Spatial Distribution of Potential Sources of Carbonaceous Aerosol from Local Measurements Near St. Petersburg

The results of back-trajectory analysis of nine-year (2013–2021) measurements of organic (OC) and elemental (EC) aerosol carbon concentrations made at the atmospheric monitoring station near St. Petersburg (Peterhof, 59.88° N, 29.83° E) are presented. The spatial location of sources was estimated by the concentration weighted trajectory method (CWT) in the geographic area 16°–44° E × 48°–68° N. The obtained data allow us to identify the territories with the strongest organic and elemental carbon emissions and to estimate the seasonal variability of these emissions. In particular, the obtained estimates show that the most intense sources of organic and elemental aerosol carbon in the studied region are located in the Volga-Oka interfluve and on the adjacent territories. It is demonstrated that linear regression coefficients between CWT function values for organic and elemental carbon differ for different regions and seasons and may indicate the prevailing type of sources of carbon-containing aerosol particles.

Fatigue and marginal adaptation of bulk fill restoratives: Effect of the layering technique and cavity dimension of extensively damaged teeth.

To evaluate the effect of layering technique and cavity dimension on the fatigue behavior and marginal adaptation of bulk fill (BF) restorations in extensively damaged teeth. Seventy-two premolars received class II cavities (MOD) followed by endodontic treatment. Half sample had 1/3 of their palatal cusp removed. Teeth were restored using three techniques: (I) incremental, with conventional resin composite (RC); (C) combined, using BF flow and RC, (B) bulk fill, with regular BF. Specimens were subjected to fatigue (80N, 2Hz, 37° C water) for 1 million cycles (n=12). The test was interrupted every 250,000 cycles to evaluate tooth integrity, restoration fracture and adaptation using FDI criteria. Images of the proximal surfaces were obtained before and after the cycling to measure the gap. Restoration fatigue survival and success were analyzed using Weibull distribution and Maximum Likelihood Estimation. Gap thickness was analyzed with Kruskal-Wallis and Student-Newman-Keuls tests (α=0.05). For the survival analysis, Weibull modulus (β) and characteristic lifetime (η) were similar among groups. Yet, for the success analysis, in which only restorations that were free of technical complications were ranked as success, the bulk-fill technique resulted in higher β, while the combined technique produced restorations with higher η, for teeth that had their cusp removed. C-technique also resulted in smaller gaps than I and B. The effect of the layering technique on the success of restorations was dependent on the cavity extension. The combined technique favors the adaptation and the longevity of extensively damaged teeth.

Estimation of the potential geographical distribution of invasive peach fruit fly under climate change by integrated ecological niche models

Climate change and biological invasions of insect pests are interlinked global concerns that drive shifts in the distribution of invasive insects. The peach fruit fly, Bactrocera zonata Saunders, is one of the most economically important Tephritidae species that attack several host plants and causes serious damage in Asia and Africa. Currently, B. zonata is absent from many countries and regions but has a risk of invasion. Therefore, it is crucial to investigate the impact of climate change on the global potential distribution of B. zonata. In this study, we used MaxEnt and CLIMEX models to estimate the risk area for B. zonata under near current and future climate conditions. The MaxEnt and CLIMEX results showed that the south of North and Central America was suitable for B. zonata. The European countries were slightly suitable for B. zonata. In Asia, the highly suitable regions of B. zonata included Saudi Arabia, United Arab Emirates, Oman, Iran, Pakistan, India, Nepal, Bangladesh, Bhutan, Myanmar, Thailand, Vietnam, and Laos. Moreover, China, Philippines, Indonesia, and Japan showed highly climate suitability for B. zonata. The climate suitability of B. zonata was increasingly high in the projection under climate change. The result of the two models showed that the climatic suitability for B. zonata will increase under climate change in China. Taken together, these predictive results support the quarantine of B. zonata for high-risk countries and provide in-depth information on how climatic changes may affect its possible geographic range.

Survival Analysis and Hazard of Log Logistic Distribution on Type I Censored Data Parametrically

Survival Analysis is a research method that examines the survival time of individuals or experimental units in relation to events such as death, disease, recovery, or other experiences. This study utilizes a parametric survival analysis model with a 2 parameter log logistic distribution and Maximum Likelihood Estimation (MLE) method to analyze the survival of students during their study period. The log logistic distribution is chosen due to its ability to capture early or late failure patterns. The objective of this research is to analyze type I censored survival data using the log logistic distribution applied to secondary data on student study duration. The dataset consists of 98 observations. The calculated values for the β and γ parameters of the 2 parameters log logistic distribution are 2.12831 and 0.0918891, respectively. The probability of students completing their studies by semester 8 (hazard function h(8)) is 0.370102, while the probability of students continuing their studies in semester 9 (survival function s(9)) is 0.320817.

Aerial Imaging-Based Fuel Information Acquisition for Wildfire Research in Northeastern South Korea

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires.

Simulation of noble metal particle growth and removal in the molten salt fast reactor

In the Molten Salt Fast Reactor (MSFR), fuel is dissolved in a liquid salt, causing the formation and decay of fission products, such as noble metals, to be inside the salt. Noble metals have very low solubility and do not dissolve in the salt. They are known to agglomorate into particles which, in turn, diffuse, drift or sediment to interfaces such as walls, free surfaces and bubbles, where they deposit. Leveraging this in the MSFR, online or offline helium bubbling is foreseen to act as a means to extract noble metal particles from the salt. This can potentially limit strong deposition of particles onto walls (i.e., plating), preventing dangerous decay heat hotspots in the heat exchangers or other primary circuit components. In this paper, we investigate the efficiency of particle removal by helium bubbles in the MSFR using Computational Fluid Dynamics (CFD). Theoretical estimates of equilibrium particle size distributions are used as initial condition, based on previous work. CFD calculations are performed in a simplified MSFR geometry. Bubble expansion, turbulent bubble coalescence and turbulent break-up are accounted for using the poly-disperse Log-normal Method of Moments (LogMoM) model, embedded in the two-fluid framework in OpenFOAM. The CFD analyses presented in this paper, in conjunction with the developed theory of noble metal particle formation, growth and capture, will help in the understanding and optimization of the fuel cycle in the MSFR or any other molten salt reactor design. The precise knowledge of how noble metal particle populations in the salt can be controlled, and how plating of noble metal particles onto primary circuit reactor components can be reduced, contributes to safe molten salt reactor operation.

Insights about Modelling Environmental Spatiotemporal Actions in Thermal Analysis of Concrete Dams: A Case Study

In order to conduct thermal analysis of concrete dams, it is necessary to assess and validate the spatiotemporal representations used for modeling the solar radiation and the water temperature boundary conditions. To illustrate this procedure, the thermal analysis of a concrete multiple-arch dam is presented. The article starts by providing an overview of the problem before focusing explicitly on the estimation of solar radiation distribution. Within this section, a comparison between the solar irradiance computed on the downstream face of the dam with or without considering the beam radiation shading at different times of the year is presented. This is followed by an analysis of the seasonal behavior of the water temperature of the dam’s reservoir based on measured data. After calibrating an empirical/statistical law based on temperatures measured at different depths, it is compared with the values estimated by a hydrodynamic model and some temperature profiles measured upstream of the dam. Finally, the article compares the results obtained with the thermal analysis versus the temperature measured by thermometers installed in the concrete.

Structure-Driven Representation Learning for Deep Clustering

As an important branch of unsupervised learning methods, clustering makes a wide contribution in the area of data mining. It is well known that capturing the group-discriminative properties of each sample for clustering is crucial. Among them, deep clustering delivers promising results due to the strong representational power of neural networks. However, most of them adopt sample-level learning strategies, and the standalone data point barely captures its holistic cluster’s context and may undergo sub-optimal cluster assignment. To tackle this issue, we propose a Structure-driven Representation Learning (SRL) method by introducing latent structure information into the representation learning process at both the local and global levels. Specifically, a local-structure-driven sample representation strategy is proposed to approximate the estimation of data distribution, which models the neighborhood distribution of samples with potential structure information and exploits statistical dependencies between them to improve cluster consistency. A global-structure-driven cluster representation strategy is designed, where the context of each cluster is sufficiently encoded according to its samples (exemplar-theory) and corresponding prototype (prototype-theory). In this case, each cluster can only be related to its most similar samples, and different clusters are separated as much as possible. These two models are seamlessly combined into a joint optimization problem, which can be efficiently solved. Experiments on six widely-used datasets demonstrate the superiority of SRL over state-of-the-art clustering methods.

A note on computing maximum likelihood estimates for the three-parameter asymmetric Laplace distribution

A finite-step procedure is proposed for maximum likelihood estimation of the three-parameter asymmetric Laplace distribution. Its performance is compared with the iterative method most commonly used for this distribution. The new procedure is much faster and reliably identifies samples for which maximum likelihood estimates lie on the boundary of the parameter space, making it a good choice for simulation studies and simulation-based methodologies.