Estimation Performance Research Articles

Process integration and electrification through multiple heat pumps using a Lorenz efficiency approach

This paper introduces a novel method for targeting the minimum shaft work required for process electrification employing principles of Pinch Analysis and detailed heat pump performance estimations. The method deviates from conventional Pinch Analysis by dividing the grand composite curve (GCC) into net heat sink and source profiles to enable the placement of heat pumps exploiting the heat pockets of the GCC. Additionally, it employs Lorenz efficiency over exergy efficiency, offering an accurate description of heat pump performance and highlighting the importance of integration between processes. The method is applied to two case studies. The first case, milk evaporator, focused on the placement of heat pumps in the heat pockets of the GCC. The results showed that while the maximum direct heat recovery was 20,447 kW, the optimal configuration limited the heat recovery to 12,199 kW, reducing the shaft work from 1,930 kW to 1,010 kW. The second, a spray dryer case, focused on the integration of electric boilers and the partial process electrification when available excess heat is limited. In this case, 1,520 kW out of 4,640 kW were covered by an electric boiler, with a biomass boiler replacing the electric boiler to cover 3,570 kW if available.

Channel Estimation for Deep Space Communications Under the Effect of Solar Scintillation

AbstractDuring probe‐to‐Earth superior conjunction, deep space communication channels will suffer from solar scintillation, leading to amplitude attenuation of received signals. This study aims to obtain the channel state information (CSI) on deep space channels affected by solar scintillation. Classical least squares (LS) and minimum mean squared error (MMSE) methods are adopted to perform channel estimation and compensate for the channel fading. Simulation results indicate that under the effect of solar scintillation, performing channel estimation technology can significantly improve bit error rate (BER) performance compared to systems without CSI, and the MMSE algorithm outperforms the LS for both BER and normalized mean squared error (NMSE). In addition, we also find that pilot density, geometric parameters, and the outer scale of solar wind turbulence has great influence on the estimation performance.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1hour data to cover time frequencies ranging from 1hour to 1month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Prospects for a survey of the galactic plane with the Cherenkov Telescope Array

Approximately one hundred sources of very-high-energy (VHE) gamma rays are known in the Milky Way, detected with a combination of targeted observations and surveys. A survey of the entire Galactic Plane in the energy range from a few tens of GeV to a few hundred TeV has been proposed as a Key Science Project for the upcoming Cherenkov Telescope Array Observatory (CTAO). This article presents the status of the studies towards the Galactic Plane Survey (GPS). We build and make publicly available a sky model that combines data from recent observations of known gamma-ray emitters with state-of-the-art physically-driven models of synthetic populations of the three main classes of established Galactic VHE sources (pulsar wind nebulae, young and interacting supernova remnants, and compact binary systems), as well as of interstellar emission from cosmic-ray interactions in the Milky Way. We also perform an optimisation of the observation strategy (pointing pattern and scheduling) based on recent estimations of the instrument performance. We use the improved sky model and observation strategy to simulate GPS data corresponding to a total observation time of 1620 hours spread over ten years. Data are then analysed using the methods and software tools under development for real data. Under our model assumptions and for the realisation considered, we show that the GPS has the potential to increase the number of known Galactic VHE emitters by almost a factor of five. This corresponds to the detection of more than two hundred pulsar wind nebulae and a few tens of supernova remnants at average integral fluxes one order of magnitude lower than in the existing sample above 1 TeV, therefore opening the possibility to perform unprecedented population studies. The GPS also has the potential to provide new VHE detections of binary systems and pulsars, to confirm the existence of a hypothetical population of gamma-ray pulsars with an additional TeV emission component, and to detect bright sources capable of accelerating particles to PeV energies (PeVatrons). Furthermore, the GPS will constitute a pathfinder for deeper follow-up observations of these source classes. Finally, we show that we can extract from GPS data an estimate of the contribution to diffuse emission from unresolved sources, and that there are good prospects of detecting interstellar emission and statistically distinguishing different scenarios.Thus, a survey of the entire Galactic plane carried out from both hemispheres with CTAO will ensure a transformational advance in our knowledge of Galactic VHE source populations and interstellar emission.

EMG and SSVEP-based bimodal estimation of elbow angle trajectory

Detecting intentions and estimating movement trajectories in a human–machine interface (HMI) using electromyogram (EMG) signals is particularly challenging, especially for individuals with movement impairments. Therefore, incorporating additional information from other biological sources, potential discrete information in the movement, and the EMG signal can be practical. This study combined EMG and target information to enhance estimation performance during reaching movements. EMG activity of the shoulder and arm muscles, elbow angle, and the electroencephalogram signals of ten healthy subjects were recorded while they reached blinking targets. The reaching target was recognized by steady-state visual evoked potential (SSVEP). The selected target’s final angle and EMG were then mapped to the elbow angle trajectory. The proposed bimodal structure, which integrates EMG and final elbow angle information, outperformed the EMG-based decoder. Even under conditions of higher fatigue, the proposed structure provided better performance than the EMG decoder. Including additional information about the recognized reaching target in the trajectory model improved the estimation of the reaching profile. Consequently, this study’s findings suggest that bimodal decoders are highly beneficial for enhancing assistive robotic devices and prostheses, especially for real-time upper limb rehabilitation.

High efficiency deep image compression via channel-wise scale adaptive latent representation learning

Recent learning based neural image compression methods have achieved impressive rate–distortion (RD) performance via the sophisticated context entropy model, which performs well in capturing the spatial correlations of latent features. However, due to the dependency on the adjacent or distant decoded features, existing methods require an inefficient serial processing structure, which significantly limits its practicability. Instead of pursuing computationally expensive entropy estimation, we propose to reduce the spatial redundancy via the channel-wise scale adaptive latent representation learning, whose entropy coding is spatially context-free and parallelizable. Specifically, the proposed encoder adaptively determines the scale of the latent features via a learnable binary mask, which is optimized with the RD cost. In this way, lower-scale latent representation will be allocated to the channels with higher spatial redundancy, which consumes fewer bits and vice versa. The downscaled latent features could be well recovered with a lightweight inter-channel upconversion module in the decoder. To compensate for the entropy estimation performance degradation, we further develop an inter-scale hyperprior entropy model, which supports the high efficiency parallel encoding/decoding within each scale of the latent features. Extensive experiments are conducted to illustrate the efficacy of the proposed method. Our method achieves bitrate savings of 18.23%, 19.36%, and 27.04% over HEVC Intra, along with decoding speeds that are 46 times, 48 times, and 51 times faster than the baseline method on the Kodak, Tecnick, and CLIC datasets, respectively.

Real-Time Aerodynamic Load Estimation for Hypersonics via Strain-Based Inverse Maps

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Precomputation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle.

Performance Estimation of Self-Circulating Fluidized Bed Gasification with mixtures of Biomass

Performance Estimation of Self-Circulating Fluidized Bed Gasification with mixtures of Biomass

Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.

On the Topp-Leone Generalized Power Weibull Distribution: Properties, Applications and Regression

Abstract The Topp-Leone generalized power Weibull distribution, which is an extension of generalized power Weibull, is proposed and its properties explored. The failure rate of the proposed distribution exhibits increasing, reversed J, upside-down bathtub, and bathtub shapes. Some statistical properties are obtained: quantile function, moments, moment generating function, incomplete moment, mean and median deviations, mean residual life function, and Lorenz as well as Bonferroni curves. The maximum likelihood estimation approach is deployed to estimate the model parameters. Simulation studies are conducted to evaluate the performance and accuracy of the maximum likelihood estimates of the model parameters. Applications of the model to real datasets are presented. A location-scale regression model is also developed for the proposed model and its application has been demonstrated with a real dataset. Keywords: Bonferroni, Deviation, Mimicked, Simulation, and Upside-down Bathtub.

On modeling added mass and circulation terms with fast potential-flow methods for oscillating-foil propulsors

The design of cycloidal thrusters and other oscillating foil propulsors requires fast tools that provide accurate estimates of propulsor hydrodynamic performance and mechanical loads. Potential-flow theory coupled with empirical formulae that account for viscous effects is the best choice for fast multi-parameter optimization. Within this context, oscillating foils are usually approximated as thin wings with flow solutions derived from small-disturbance theory. Added mass (non-circulatory) effects may be relevant even under such assumptions. They can be easily included in the flow equations assuming that the foil is a flat plate heaving and pitching. However, for blade motions involving complex trajectories like those in Voith Schneider propellers, the foils describe cycloidal loops, and the assumption of small pitch angles is no longer valid. Therefore, a more accurate modeling of added mass and circulatory terms is required.This paper presents extensions to a previous model, which allows for a more precise representation of the added mass terms in the potential flow equations, combining low computational cost and accurate modeling. The results show clear improvement in the evolution foil forces in time when the new approach is used. The extensions are especially needed for the simulations of foils describing complex paths with large variations of pitch angle. Applications to transverse-axis cycloidal propulsors are presented as well as to unconventional counter-flapping propulsors that have recently appeared in the scientific literature. Validation of the method with RANS computations is provided.

Организация работы терминала при хранении контейнеров на полуприцепах

Purpose: development of a method for determining the configuration of a container terminal that will help in the terminal design process to determine the number of gantry cranes, terminal trucks, and the number and location of storage areas. Methods: materials from open sources are used in the work. A systematic approach to the design of a container terminal is applied. Results: a method for determining the configuration of a container terminal is developed, the difference being the storage of a container on a semi-trailer. Practical importance: the developed method will allow estimation of container terminal performance and capital costs before design.

Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests

The combination of machine learning models with satellite imagery is becoming a popular data-modeling tool for biomass prediction, supporting land cover management. This study aims to select the most suitable model to estimate tropical forest aboveground biomass in Vietnam, helping to manage and monitor changes in biomass at regional and local scales. The study identified the optimal model for estimating forest aboveground biomass and minimizing the number of input variables while achieving satisfactory model performance. A total of 59 input variables, including topography, texture features, and vegetation indices, from satellite data were used in four non-parametric algorithms and a conventional parametric model, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) to predict biomass and evaluate changes aboveground biomass over 10 years in two tropical forests in Vietnam. The results indicated that all models had good estimation performance with R2 ranging from 0.615 to 0.754. For RF, MLR, and XGBoost, vegetation indices contributed the highest model weights, occupying 77.71% – 92.48%. For ANN and SVM, textural and topographic features were the majority of the model weights (73.74 – 96.36%). The RF model performed the best using 59 variables (R2 = 0.754, MAE = 78.5 Mg·ha−1, and %RMSE = 13.57%) and ten variables (R2 = 0.745, MAE = 85.8 Mg·ha−1, and %RMSE = 16.17%). The biomass map using the RF and ten variables achieved a good degree of fitting of 0.76, so it was suitable for managing and monitoring forest biomass in Vietnam. The results indicated a sharp decrease in the areas of dense and very dense forests from 2013 to 2021 and a gradual increase in 2023.

A method to balancing robotic mixed-model assembly lines: Practical constraints, computational challenges, and performance estimation

This paper considers a robotic assembly line with various practical constraints. These have been previously modeled mathematically, but extending it to consider mixed-model production presents two challenges: computational costs increase substantially with problem size, and devising a theoretical model for practical performance is difficult within mathematical programming. This paper seeks to bridge those gaps,by proposing a method based on fixing, constraining, and optimizing mixed-integer programming instances. Simulations and linear relaxations are used to measure performance and estimate room for improvement. The resulting solution for the large-size industrial case study reached approximately 5% better throughput than a mixed-model benchmark approach, which represents around 85% of the estimated improvement potential for starting from that initial solution. Furthermore, an exhaustive set of tests was performed to demonstrate the proposed method’s efficacy. Hence, the method managed to optimize a rather challenging computational problem that combines the complexity of relevant practical constraints with theoretical difficulties in estimating the performance of solutions.

Direction of Arrival Estimation Based on DNN and CNN

The accuracy of Direction of Arrival (DOA) estimation primarily depends on the precision of the data. When the receiver uses a low-precision analog-to-digital converter (ADC), traditional DOA estimation algorithms exhibit poor accuracy. To face the challenge of multi-target DOA estimation in scenarios with low-precision ADC quantized sampling, this paper proposes a novel DOA estimation algorithm for quantized signals based on classification problems. A deep learning network was constructed using Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), divided into the quantized signal recovery framework and the DOA estimation framework. The DNN network is utilized to recover signals that have undergone low-precision quantization, while the CNN network addresses the classification problem to estimate the DOA from received data with an unknown number of signal sources. A comprehensive analysis of the impact of signal-to-noise ratio (SNR), the number of array elements, and the number of quantization bits on the proposed algorithm was conducted. Simulation results indicate that the proposed algorithm exhibits superior DOA estimation performance in low-precision scenarios, characterized by reduced computational complexity, thereby facilitating real-time DOA estimation.

ROS Gateway: Enhancing ROS Availability across Multiple Network Environments.

As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments.

Modeling and performance estimation for L-shaped OWC wave energy converters with a theoretical correction for spring-like air compressibility

The investigation of Oscillating Water Columns (OWC) has gained significant attention in recent years thanks to their resilient structural design, allowing them to withstand harsh environmental conditions. This study focuses on the L-shaped OWC (L-OWC) due to its excellent energy capture efficiency. It should be noted that air compressibility plays a significant role in the plenum chamber of the OWC, especially at full scale. The present paper proposes a scaling-rematched approach, facilitating the evaluation of hydrodynamic coefficients and the performance estimation with a theoretical correction for the unmatched scaling problem of spring-like air compressibility. The methodology is applied successfully to a model-scale L-OWC design, employing three-dimensional, incompressible-flow simulations combined with an impeller model. The present study reveals the hydrodynamic characteristics (advantages) of the L-OWC: small fluid damping coefficient and large added mass, and hence the possibility that the spring-like air compressibility can be used to raise the efficiency of power capturing. Furthermore, there exists an interval where the air compressibility has a positive effect on the performance, not only having a significantly longer span than that of the conventional OWC but also much more directly matching the period range of high energy potential found in the wave climate of Northeastern Taiwan waters.

A review of Bayes filters with machine learning techniques and their applications

A Bayes filter is a widely used estimation algorithm, but it has inherent limitations. Performance can degrade when the dynamics are highly nonlinear or when the probability distribution of the state is unknown. To mitigate these issues, machine learning (ML) techniques have been incorporated into many Bayes filters, due to their advantage of being able to map between the input and the output without explicit instructions. In this review, we reviewed 90 papers that proposed the use of ML techniques with Bayes filters to improve estimation performance. This review provides an overview of Bayes filters with ML techniques, categorised according to the role of ML, remaining challenges and research gaps. In the concluding section of this review, we point out directions for future research.

A new class of Lindley distribution: System reliability, simulation and applications

This paper presents a new probability distribution called the DUS Lindley distribution, created by applying the DUS transformation to the traditional Lindley distribution. The study provides an in-depth analysis of the distribution's statistical properties. These properties include a variety of statistical measures such as the probability density function, cumulative distribution function, failure rate, survival function, reverse hazard function, Mills ratio, mean residual life, mean past life, moments, conditional moments, characteristic function, order statistics, entropy measures, likelihood ratio test and Lorenz and Bonferroni curves. Parameter estimation is performed using several methods including weighted least squares, maximum likelihood estimation, Cramer-Von Mises estimation, least squares and Anderson-Darling estimation. The paper also explores the estimation of system reliability and evaluates the performance of maximum likelihood estimators through simulation studies across different sample sizes. Finally, the DUS Lindley distribution is applied to two real-world datasets, demonstrating a better fit than other well-known distributions.

Evaluating sampling techniques for quantifying Asiatic garden beetle (Maladera formosae) (Coleoptera: Scarabaeidae) infestations in commercial mint.

The Asiatic garden beetle, Maladera formosae Brenske (AGB), has become a significant pest of commercial mint fields in northern Indiana. Larval feeding on mint roots can cause stunted growth and plant death when densities are high. Sampling approaches that provide reliable estimates of larval densities in mint have not been established, leaving farmers without the knowledge necessary to implement integrated pest management (IPM) strategies. To address this knowledge gap, we evaluated strategies for estimating AGB larval densities and plant performance in commercial mint systems. We used 2 sampling methods to collect larval density and plant performance data from 3 mint fields and conducted simulations to optimize sampling intensity (accuracy and precision) and sampling scheme (random vs. systematic) using these data. Additionally, we examined the sensitivity and efficiency of each sampling method. Compared to the cup-cutter method, the quadrat method provided the most accurate and precise estimates of larval density and plant performance, with ≤ 7 samples required per 0.2 ha. Quadrat excavation was also more sensitive, increasing the probability of detecting AGB larvae within a 32 m2 plot by 76.7%, and requiring significantly less time to survey an equivalent volume of soil for AGB larvae. When the quadrat method was employed, random sampling schemes provided below-ground biomass estimates that were significantly closer to the true mean of the sampling area. The results of this research will facilitate the development of IPM decision-making tools for farmers and support future research for AGB and other soil insect pests affecting mint production.