Parameter Selection Criteria Research Articles

Analytical Overview of the Methods of Controlling the Parameters of Vibratory Compaction of the Concrete Mixes

Introduction. A historical (retrospective) overview of the evolution and application of the methods of concrete mixture compaction by vibration, as well as an overview of the breakthrough scientific+ advancements in the studied field have been presented. The use of the dry concrete mixes that reduce cement consumption, accelerate the strength gain of concrete, reduce the shrinkage strain and heat emission and increase the strength and durability of products has been proposed. The theoretical and practical issues of vibratory compaction of the concrete mixes, the research on the effect reached by the various vibration modes of the vibrating tables, the issues of choosing an efficient mode of vibration impact during compacting the concrete mixes and optimisation of their parameters have been studied. The vibrating table design, the installation layout of the vibrator’s eccentric weights to obtain a given value of the exciting force and the layout of a vibration module with the directed vibrations have been studied in detail. The criteria of duration of the concrete mixture compaction have been defined, changes of the impact parameters during compacting the concrete mixes have been characterised and technical specifications of the vibrating tables have been defined. In the article considerable attention has been paid to the main parameters of the vibrocompacting machines: frequency, amplitude and acceleration of the working member. Most often, the parameter of intensity is used to assess the efficiency of the vibration impact received by the mixture during the compaction process.Materials and Methods. A mixture that turns into concrete after compaction, setting and hardening was selected for the research. The various methods and parameters of concrete mixture compaction to obtain the high-quality concrete were studied. The impact of the various vibration modes on the process of compaction of concrete mixes was investigated, the criteria for the efficient selection and optimisation of the vibration impact parameters were revealed. The paper presents the criteria for assessing duration of the concrete mixture compaction and describes the changes in the parameters during compaction of the concrete mixes. The technical specifications of the vibrating tables of different types and sizes were also presented. The choice of the vibration compaction mode for the concrete mixture is a complicated task, which includes many parameters.Results. Upon the research, the data on the process of concrete mixture compaction under the impact of vibration forces has been obtained. The process results in the destruction of the structural bonds and weakening the bonds between the particles, which leads to the emergence of a variable stress-strain condition. It has also been found that under such settings, the mineral particles get transferred and a denser packing is formed.Discussion and Conclusion. The resulting denser packing of the concrete mixture particles ensures the undoubted economic advantage due to reduced consumption of a binder without loss of the strength properties of concrete.

Research on parameter selection criteria and design method for leachate pumping using vertical shafts

High leachate levels in landfills not only threaten the environment but also seriously affect the stability of piles. Therefore, it is crucial to pump leachate from piles, and vertical pumping wells have demonstrated their effectiveness in practical engineering. This paper establishes a model of vertical pumping wells, taking into account the non-homogeneous distribution of geotechnical parameters of the pile. The accuracy of this model is then validated through existing test results and numerical models. The main findings of the study are as follows: the radius of influence R is primarily influenced by the pumping time T, pumping pressure P, and anisotropy coefficient A. On the other hand, the depth of influence h is mainly determined by the burial depth h1 of the pumping section. The optimal pumping time Tb coincides with the time at which the well reaches its maximum radius of influence Rm. Similarly, the optimal pumping pressure Pb corresponds to the inflection point in the relationship between the radius of influence R and the pumping pressure P. The optimal pumping section burial depth is h1b = 20 m, and the optimal setback distance db is equal to the maximum setback distance dm. Additionally, this paper provides the criteria for parameter selection under each working condition at the optimal burial depth of the pumping section, as well as the design process for the vertical pumping system. The parameter design diagram and design method presented in this paper serve as valuable references for the design of real guide drainage projects.

Improving electrocardiographic imaging solutions: A comprehensive study on regularization parameter selection in L-curve optimization in the Atria

BackgroundIn electrocardiographic imaging (ECGI), selecting an optimal regularization parameter (λ) is crucial for obtaining accurate inverse electrograms. The effects of signal and geometry uncertainties on the inverse problem regularization have not been thoroughly quantified, and there is no established methodology to identify when λ is sub-optimal due to these uncertainties. This study introduces a novel approach to λ selection using Tikhonov regularization and L-curve optimization, specifically addressing the impact of electrical noise in body surface potential map (BSPM) signals and geometrical inaccuracies in the cardiac mesh. MethodsNineteen atrial simulations (5 of regular rhythms and 14 of atrial fibrillation) ensuring variability in substrate complexity and activation patterns were used for computing the ECGI with added white Gaussian noise from 40 dB to -3dB. Cardiac mesh displacements (1–3 cm) were applied to simulate the uncertainty of atrial positioning and study its impact on the L-curve shape. The regularization parameter, the maximum curvature, and the most horizontal angle of the L-curve (β) were quantified. In addition, BSPM signals from real patients were used to validate our findings. ResultsThe maximum curvature of the L-curve was found to be inversely related to signal-to-noise ratio and atrial positioning errors. In contrast, the β angle is directly related to electrical noise and remains unaffected by geometrical errors. Our proposed adjustment of λ, based on the β angle, provides a more reliable ECGI solution than traditional corner-based methods. Our findings have been validated with simulations and real patient data, demonstrating practical applicability. ConclusionAdjusting λ based on the amount of noise in the data (or on the β angle) allows finding optimal ECGI solutions than a λ purely found at the corner of the L-curve. It was observed that the relevant information in ECGI activation maps is preserved even under the presence of uncertainties when the regularization parameter is correctly selected. The proposed criteria for regularization parameter selection have the potential to enhance the accuracy and reliability of ECGI solutions.

Self-organizing hierarchical incremental learning framework and universal approximation analysis based on stochastic configuration mechanism

Conventional machine learning algorithms face significant limitations when dealing with high-dimensional data. Besides, deep learning models often require substantial computational resources and have a high processing time despite their excellent performance. Hence, this paper proposes an expanded stochastic configuration network and a self-organizing hierarchical incremental learning (SHIL) framework to overcome these challenges. Specifically, this study introduces a novel supervised hierarchical clustering tree based on the minimum redundancy maximum correlation algorithm, which mines internal data structures to construct diverse hierarchies. Subsequently, by exploiting the parent-child node relationships in the tree structure, SHIL defines the maximum number of nodes as the switching condition between levels uses the supervisory mechanism as the parameter selection criterion, and adopts the tolerance error as the termination criterion for the training. Furthermore, the universal approximation property of the SHIL framework is provided. The proposed SHIL framework is validated on several benchmark datasets, image datasets, and industrial robot cases, with the corresponding experimental results demonstrating that SHIL significantly improves computational efficiency and ensures high accuracy.

Separation and Evaluation of the Gradient Relaxation in the Atomic Comagnetometer

AbstractIn the atomic comagnetometer, nuclear spin relaxation is a core parameter that affects the magnetic field suppression ability and stability, and is influenced by various gradient fields. It is necessary to measure and separate the effects of different gradients on nuclear spin, which helps to effectively suppress them separately. This article proposes a periodic optical pumping method for separating and measuring polarization gradient relaxation and magnetic field gradient relaxation, considering the effects of pump light and applied magnetic field. The comprehensive influence model for pump light on transverse nuclear spin relaxation is established, and the measurement steps and parameter selection criteria for the proposed method considering signal decay characteristics are provided. Furthermore, the proportion of various gradient relaxations is quantified. This work provides an evaluation method for gradient relaxation suppression, supporting the improvement of the measurement sensitivity and stability of the atomic comagnetometer.

Thermodynamic performance evaluation and optimization of a hybrid system integrating vacuum graphene-anode thermionic converters with direct carbon fuel cells

An efficient hybrid system integrating a direct carbon fuel cell (DCFC) with a vacuum graphene-anode thermionic converter (VGTC) is proposed for cogeneration. The VGTC efficiently recycles waste heat released from the DCFC and generate additional electricity, significantly improving energy utilization efficiency and economic performance. A comprehensive mathematical model is developed to quantitatively assess the thermodynamic performance of the hybrid system, taking into account the overpotential losses of the DCFC and the irreversible energy losses occurring within the system. The results show that at an operating temperature of 923 K, the hybrid system can achieve a maximum power density of 466 W/m2, which is approximately 1.34 times that of a single DCFC, indicating a significant improvement in output performance. Besides, the optimal operating region and parameter selection criteria for the hybrid system are determined using finite-time thermodynamic optimization theory. Furthermore, the effects of essential parameters on the output performance of the hybrid system, such as the operating temperature of the DCFC, the heat transfer coefficient, the Fermi level of graphene, the work function of the cathode, the thermal emissivity, and the reflectivity of the back mirror, are investigated. Finally, the comparative study shows that the proposed hybrid system outperforms other previously reported hybrid systems regarding output electric power and conversion efficiency due to the efficient high-grade waste heat recovery and energy conversion by the VGTC.

Spectral analysis of seismic data based on a combined generalized S transform

The S transform is widely used to delineate seismic spectral anomalies associated with hydrocarbons during reservoir characterization. To overcome the relatively poor time-frequency resolution due to the fixed varying pattern of the window used in the S transform, we present a combined generalized S transform (CGST) for the spectral analysis of seismic data. First, the CGST decomposes the signal into a series of intrinsic mode functions (IMFs) using a complete ensemble empirical mode decomposition (CEEMD). An objective function is defined to quantitatively determine the spectral energy concentration criteria for optimal parameter selection of the improved Gaussian windows. Then, the time-frequency spectra for all the IMFs within narrower frequency bands are calculated using the respective Gaussian windows with normalized energy and individual optimal controlling parameters. We applied the CGST to spectral analyses of both synthetic signals and seismic field data. The results indicate that the CGST exhibits good performance with a higher time-frequency resolution. Field examples illustrate that the CGST can be used to characterize the spectral behavior of thin beds and indicate low-frequency shadows associated with hydrocarbon reservoirs.

Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer’s Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer’s Disease, which is crucial for early disease detection and personalized treatment.

Tuning parameter selection for nonparametric derivative estimation in random design

Estimation of a function, or its derivatives via nonparametric regression requires selection of one or more tuning parameters. In the present work, we propose a tuning parameter selection criterion called DCp for nonparametric derivative estimation in random design. Our criterion is general in that it can be applied with any nonparametric estimation method which is linear in the observed outcomes. Charnigo et al. [A generalized C p criterion for derivative estimation. Technometrics. 2011;53(3):238–253] had proposed a GCp criterion for a similar purpose, assuming values of the covariate to be fixed and constant error variance. Here we consider the setting with random design and non-constant error variance since the covariate values will not generally be fixed and equally spaced in real data applications. We justify DCp in this setting both theoretically and by simulation. We also illustrate use of DCp with two economics data sets.

Adaptive Neural Coordinated Control for Multiple Euler-Lagrange Systems With Periodic Event-Triggered Sampling.

This article addresses the event-triggered coordinated control problem for multiple Euler-Lagrange systems subject to parameter uncertainties and external disturbances. Based on the event-triggered technique, a distributed coordinated control scheme is first proposed, where the neural network-based estimation method is incorporated to compensate for parameter uncertainties. Then, an input-based continuous event-triggered (CET) mechanism is developed to schedule the triggering instants, which ensures that the control command is activated only when some specific events occur. After that, by analyzing the possible finite-time escape behavior of the triggering function, the real-time data sampling and event monitoring requirement in the CET strategy is tactfully ruled out, and the CET policy is further transformed into a periodic event-triggered (PET) one. In doing so, each agent only needs to monitor the triggering function at the preset periodic sampling instants, and accordingly, frequent control updating is further relieved. Besides, a parameter selection criterion is provided to specify the relationship between the control performance and the sampling period. Finally, a numerical example of attitude synchronization for multiple satellites is performed to show the effectiveness and superiority of the proposed coordinated control scheme.

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

Numerical investigation of the effect of thermal expansion coefficient and mushy zone constant on modelling of the phase change process to provide reliable selection criteria of modelling parameters

Phase change materials (PCMs) have been involved in many critical applications due to their ability to store thermal energy and stabilize the temperature. The accuracy of PCMs numerical modelling is affected by the software input assumptions including the thermal expansion coefficient and the mushy zone parameter. The thermal expansion coefficient is used to calculate the density value, to be applied in the modelling equations, but volume expansion is generally not considered by PCM modelling softwares. Due to this fact, the assumed value for the thermal expansion coefficient has to be selected properly, before conducting numerical tests. Another influential input is the mushy zone constant that has a significant effect on the output results, and it has to be selected from a wide range of values between 103 and 1010.In this study a numerical investigation for the influence of both parameters was conducted to provide valid selection criteria. It was found that changing the expansion coefficient controlled the melting process time without affecting the melting profiles, where increasing the expansion coefficient accelerated the melting process, reducing the melting time and vice versa. The melting interface profiles were found directly related to the mushy zone parameter. Increasing the mushy zone parameter value decreased heat transfer rate through the mushy region, which significantly changed the melting profiles. These tests were repeated using different orientations to confirm these findings. Finally, reliable selection criteria were introduced to select the proper values for numerical modelling of phase change process. This selection process was tested using Lauric acid material at different orientations.

A new type of generalized information criterion for regularization parameter selection in penalized regression with application to treatment process data

ABSTRACT We propose a new approach to select the regularization parameter using a new version of the generalized information criterion ( GIC ) in the subject of penalized regression. We prove the identifiability of bridge regression model as a prerequisite of statistical modeling. Then, we propose asymptotically efficient generalized information criterion ( AGIC ) and prove that it has asymptotic loss efficiency. Also, we verified the better performance of AGIC in comparison to the older versions of GIC . Furthermore, we propose MSE search paths to order the selected features by lasso regression based on numerical studies. The MSE search paths provide a way to cover the lack of feature ordering in lasso regression model. The performance of AGIC with other types of GIC is compared using MSE and model utility in simulation study. We exert AGIC and other criteria to analyze breast and prostate cancer and Parkinson disease datasets. The results confirm the superiority of AGIC in almost all situations.

Learning automaton-based energy-efficient and fault-tolerant topology evolution algorithm for underwater acoustic sensor network

With the development of Marine Internet of Things, underwater acoustic sensor network (UASN) has become a research focus. However, the energy of UASN nodes is limited, and the complex underwater environment is easy to cause the failure of UASN nodes, which will affect the normal operation of UASN. To address this issue, in this paper, an UASN energy-efficient and fault-tolerant topology is generated to prolong the node lifetime and improve the fault tolerance for node failure. Initially, the learning automaton is introduced and improved to optimize the transmission power of each node. Based on the optimized power of nodes, the node lifetime considering node load is analyzed. Then the preferential growth mechanism is improved to present the energy-efficient and fault-tolerant topology evolution algorithm. Consequently, the energy-efficient and fault-tolerant UASN topology is generated. In the simulation experiments, the selection criteria of parameters about transmission power and node load under different actual requirements is obtained, and the performance of generated topology is validated. Simulation results show that the fault tolerance and energy efficiency of generated topology in this paper outperforms the TCEB and PG-OSTCG topologies.

A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks

The COVID-19 pandemic has hit the airline industry hard, leading to heterogeneous epidemiological situations across markets, irregular flight bans, and increasing operational hurdles. Such a melange of irregularities has presented significant challenges to the airline industry, which typically relies on long-term planning. Given the growing risk of disruptions during epidemic and pandemic outbreaks, the role of airline recovery is becoming increasingly crucial for the aviation industry. This study proposes a novel model for airline integrated recovery problem under the risk of in-flight epidemic transmission risks. This model recovers the schedules of aircraft, crew, and passengers to eliminate possible epidemic dissemination while reducing airline operating costs. To account for the high uncertainty with respect to in-flight transmission rates and to prevent overfitting of the empirical distribution, a Wasserstein distance-based ambiguity set is utilized to formulate a distributionally robust optimization model. Aimed at tackling computation difficulties, a branch-and-cut solution method and a large neighborhood search heuristic are proposed in this study based on an epidemic propagation network. The computation results for real-world flight schedules and a probabilistic infection model suggest that the proposed model is capable of reducing the expected number of infected crew members and passengers by 45% with less than 4% increase in flight cancellation/delay rates. Furthermore, practical insights into the selection of critical parameters as well as their relationship with other common disruptions are provided. The integrated model is expected to enhance airline disruption management against major public health events while minimizing economic loss.

Straightening methods for RCA and RAC—a review

Based on 170 references, this study summarizes the processes, materials, improvement mechanisms, and factors affecting the improvement for two methods of recycled aggregate concrete (RAC): pretreatment of recycled concrete aggregate (RCA) and RAC modification. The improvement effects were analyzed according to the improvement methods and research directions were proposed. The results indicated that RCA pretreatment could improve its physical properties by removing or strengthening the RCA surface of the old mortar. RAC modification optimizes the internal structure of concrete by modifying its materials. Both can optimize the interface transition zone of the RAC and compact the concrete. However, to remove old mortar, appropriate conditions must be maintained to avoid secondary damage to the original aggregate. Future research should emphasize establishing a multifactor RCA grading system, forming optimal parameter selection criteria for pretreatments, improving the RAC theoretical system, exploring the combined effects of multiple methods, and verifying the long-term performance.

Performance enhancement and parametric optimization of a sky radiative and thermoelectric hybrid cooling system for water harvesting from humid air

The aim of this study is to deepen into the study and analysis of energetic performance of a novel RSC-TEC hybrid cooling system for water harvesting by combining thermoelectric and radiative sky cooling mechanisms. The main contents of this study are to propose the thermodynamic model of the novel hybrid system considering main losses and steady-state assumption, solve the operating temperatures of the thermoelectric cooler by energy balance equations, discuss the influences of multiple system variables including air flow rate, electric current of the thermoelectric cooler, ambient relative humidity, and ambient temperature on effectiveness and water production rate, and, evaluate and compare the performances of the whole system and the thermoelectric cooler. The main novelties are to provide the parametric optimum selection criteria of some key parameters of the overall system operating at optimum states. Results show that the maximum effectiveness and water production rate of the hybrid system can reach 1.93×10- 6 kg J−1 (amount to 6.95×103 ml of water by consuming 1 kWh of electrical energy) and 1.51×10- 5 kg s−1 (amount to 54.4 ml of water within 1 h) at 0.75 relative humidity and the ambient temperature of 298 K. In addition, the system is capable of producing about 140 ml of water within 1 h from the air with high relative humidity, which is more efficient than other different existing air water generators. The optimized effectiveness (coefficient of performance) of the hybrid system is much better than that of a separate (subsystem) thermoelectric cooler operating at the same ambient condition. The results obtained reveal the advantages of the present hybrid system. Complementary perspectives on compromise performance regimes are also displayed from Pareto front yielded from numerical multi-parametric and multi-objective methods.

A robust bathtub-shaped failure time model for a two-component system with applications to complete and censored reliability data

ABSTRACT This article proposes a flexible additive model that adequately describes complex reliability and survival data. The proposed methodology, referred to as the flexible exponential power-Gompertz (FEPG4) distribution, is able to characterize the behavior of a complex system whose failure times have bathtub-shaped, with clear burn-in and wear-out change points and a low, yet lengthy, flat middle segment as its underlying failure rate distribution. We discuss some properties of the model. Parameter inferences are proposed under maximum likelihood and Bayesian techniques. We determine the Bayes estimators of the FEPG4 parameters and used Hamiltonian Monte Carlo for posterior simulations. Extensive simulation experiments are performed to validate the proposed estimators. For assessing the potential of the FEPG4, the model is compared with other recent bathtub distributions constructed via the same approach on devices’ failure and running times (censored and non-censored case) and failure times of some devices, each with the bathtub failure rate. Seven parametric and non-parametric selection criteria and other supporting plots are utilized for comparison purposes. Findings indicate that the FEPG4 model might be the best alternative for representing device failure times, particularly when the bathtub-shaped failure rate of the available data clearly illustrates its three phases.

The reverse flow and amplification of heat in quantum-dot systems

For classical systems, heat is impossible to be transferred from a cool bath to a hot bath without the existence of any external agency. Here, we show that such a phenomenon can occur in quantum systems through a simple device that a quantum dot is embedded between the two reservoirs described by different statistical distribution functions. The reverse flow and amplification of heat in the device can be realized by regulating the energy levels of the quantum dot and the chemical potentials of reservoirs. It is of particular interest to discover that because of the existence of the quantum tunneling effect in the device, this new quantum phenomenon can be observed even when the chemical potential gradient and the temperature gradient are in the same direction. Moreover, the reverse heat flow and amplification coefficient of the quantum device are calculated. The novelty of this device is that the reverse flow of heat does not need externally driving force and this seemingly paradoxical phenomenon does not violate the laws of thermodynamics. It is further expounded that the quantum device has some practical applications. For example, the device can work as a micro/nano cooler. The performance characteristics of the cooler are revealed for different distribution functions. The coefficients of performance of the cooler operated at different conditions are calculated and the optimum selection criteria of key parameters are supplied.

Optimization of drilling parameters based on Copula function method for the Chepai area

In areas with complex stratigraphic lithology, the relationship between the penetration rate and drilling parameters should be fully considered to optimize the drilling process and improve drilling efficiency. The most frequently utilized methods for performing parameter optimization through correlation analysis are the correlation coefficient, principal component analysis, and grey correlation. The correlation coefficient method solely evaluates the extent of linear correlation between two variables, it cannot be applied to the non-linear connection between penetration rate and drilling parameters. The application of principal component analysis may produce inaccurate experimental findings due to the intricate and poorly co-varying nature of drilling parameters. The grey correlation method can lead to the substantial bias in the results because of the vast quantity of data analysed. Based on the vast quantity of data, using the copula function, the big data analysis method analyses the nonlinear relationship between penetration rate and drilling parameters. It constructs a united distribution function expression to determine the optimal parameter selection criteria. The in-situ drilling data from dozens of wells in the Chepaizi area are collected and optimized six types of parameters. The optimal parameter combination is determined. Following field investigation, there was a noteworthy increase of 34.83% in the average penetration rate.