Estimate System Performance Research Articles

Strata Design for Variance Reduction in Stochastic Simulation

Stratified sampling is one of the powerful variance reduction methods for analyzing system performance, such as reliability, with stochastic simulation. It divides the input space into disjoint subsets, called strata, to draw samples from each stratum. Partitioning the input space properly and allocating greater computational effort to crucial strata can help accurately estimate system performance with a limited computational budget. How to create strata, however, has yet to be thoroughly examined. Strata design faces the curse of dimensionality and data scarcity as the input dimension increases. We analytically derive the optimal stratification structure that minimizes the estimation variance for univariate problems. Further, reconciling the optimal stratification into decision trees, we devise a robust algorithm for multi-dimensional problems. Numerical experiments and a wind turbine case study demonstrate the superiority of the proposed method in terms of variance reduction, leading to computational efficiency and scalability.

Operation optimization in large-scale heat pump systems: A scheduling framework integrating digital twin modelling, demand forecasting, and MILP

The integration of large-scale heat pumps and thermal energy storage can facilitate sector coupling, potentially lowering heating and cooling costs in industries and buildings. This cost reduction can be extended by optimizing the utilization of the available thermal energy storage capacity in accordance to fluctuating electricity prices. Although the literature offers methods for optimizing the operation of these integrated systems, they often overlook the impact of heat pump performance degradation over time, such as from fouling. This oversight can lead to suboptimal system performance and inaccurate operational cost estimates. The present study addresses this gap by introducing a novel operational scheduling framework that aimed to reduce the operational costs of a commercial large-scale heat pump system. The system comprised an open cooling tower, a thermal storage tank and two heat pumps affected by fouling. The framework incorporated a mixed-integer linear programming (MILP) model, thermal demand forecasting, and heat pump performance maps that account for varying fouling levels. These maps were obtained from online calibrated simulation models used as digital twins of the heat pumps. The results demonstrated that the proposed framework enhanced the thermal energy storage utilization in response to variable electricity prices and adjusted the heat pump operation based on the influence of fouling. This resulted in a reduction of operational costs of up to 5% compared to the conventional operation of the system. These savings were observed to vary depending on the forecasting accuracy and the prevailing fouling levels. Overall, this study demonstrates the potential of using the proposed framework for cost reduction in large-scale heat pump systems.

AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency

This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

Selective under-representation of Pacific peoples in population estimates for health indicator measurements in Aotearoa New Zealand misinforms policy making

BackgroundThe Census of Populations and Dwellings’ is the five yearly population count of Aotearoa New Zealand. Best available populations (BAP) are subnational projections based on census data and demographic assumptions developed for healthcare planning and funding allocation but are also used as the denominator for health indicator monitoring. Pacific people are systematically undercounted, but the impact on health statistics is not well studied. For COVID-19 vaccination coverage, health service user (HSU) data were considered a more reliable denominator than BAP but introduced new biases. We aimed to understand how the choice of denominator population impacts estimates of population size and health system performance for Pacific people at a local level.MethodsWe described how declining census response rates affected population data quality. We compared BAP and HSU data at district level. For the indicators ‘access to primary care’ and ‘cervical cancer screening uptake’ we replaced currently used BAP denominators with HSU and examined the impact for different ethnic groups in different geographic districts.ResultsOverall Census 2018 response declined by 10%, but for Māori and Pacific people by 21% and 23%, respectively. This inequitably affected BAP accuracy. Census undercount was highest in the district with the largest Pacific populations, where HSU exceeded BAP most. Notably, ‘access to primary care’ for Pacific people in this district consistently exceeds 100%. Using BAP, both health indicators are currently estimated as highest for Pacific people compared to other ethnic groups, but when based on HSU, they dropped to lowest. Similar, but less pronounced trends occurred in other districts. Changes in trends over time for both indicators coincided mostly with adjustments in BAP, rather than changes in the numerators.ConclusionsThe current use of BAP denominators for health statistics does not enable reliable monitoring of key health indicators for Pacific people. HSU denominators are also unsuitable for monitoring health. Exploring the feasibility of a real-time population register is strongly recommended as a new, transparent, way of obtaining more reliable, timely population data to guide policymaking and underpin a more equitable health system under the health reforms. Meanwhile, reporting of ethnic specific outcomes need to include a clear assessment of the potential for bias due to inaccurate population estimates.

Mathematical model to optimize solar spectrum allocation for a Stirling engine-based concentrated photovoltaic thermal system

Incorporating a Stirling engine into a concentrated photovoltaic thermal system (CPVT) with spectral beam splitting converts wasted photovoltaic panel energy into electricity, boosting efficiency. However, complex Stirling engine development slows system performance estimation, which explains the scarce studies on the Stirling engine-based CPVT spectrum allocation range. Furthermore, the literature shows inconsistent remarks on low or high-energy photon allocation to a thermal system and uncertainty of solar irradiance variation effect on spectrum allocation. A generic methodology is presented for determining optimal spectrum allocation for a Stirling engine-based CPVT system with a dichroic filter. The analysis in this work shows that considering non-absorption and thermalization losses improves performance by 13.2 % compared to solely accounting for non-absorption loss in beam splitting. Additionally, a shift of up to 40 nm in the optimal spectrum allocation range is observed with a daily spectral irradiance source compared to the AM1.5D source. The PV mathematical model is validated with an average error of 8.9 %, and the Stirling engine model with an error of 5.2 %. Through this study, future researchers can determine an optimal spectrum allocation range for Stirling engine-based CPVT that maximizes PV performance based on a specified location with the proposed generalized CPVT model.

Channel Estimation for Multiple-Input Multiple-Output Orthogonal Chirp-Division Multiplexing Systems

In multiple-input multiple-output (MIMO) systems, channel estimation is of crucial importance to guarantee reliable recovery of ultra-high-speed MIMO signals. This paper proposes a novel channel estimation algorithm for the emerging MIMO-based orthogonal chirp-division multiplexing (OCDM) systems by utilizing the unique features of OCDM signals. In the proposed algorithm, a set of pilot signals is designed based on the Fresnel basis, which is essentially a family of orthogonal linear chirps. The pilots are assigned to different antennas for transmission occupying the same time slot and bandwidth. According to the convolution-preservation theorem of the Fresnel transforms, the transfer matrices of MIMO-OCDM systems can be readily estimated at the receiver without any inter-antenna interference, even if the pilots overlap in both the time and frequency domains. The proposed algorithm avoids bandwidth waste in conventional channel estimators, in which silent pilots will be required in time and/or frequency to ensure the received MIMO pilots separable. We show that the proposed algorithm is unbiased for the unique OCDM pilots and has better estimate accuracy and system performance. Finally, analysis and numerical results are provided to validate its advantages as a promising algorithm for emerging wireless access technology based on MIMO-OCDM.

Multi server queuing model with dynamic power shifting and performance efficiency factor

The need for high performance models in the queuing systems; availability in the fields of computing and communication systems and logistics management poses extensive challenges in design and development of the appropriate modeling. In this work, we propose a robust probabilistic model to mitigate these problems by appropriately choosing a multi-server queuing system with dynamic service facility. The arrival substances, such as packets or jobs or customers or consumers, follow a Poisson arrival process and these arrivals enter a queuing system according to FIFO discipline. The system designed in the system is armed with a fixed number of service stations, in which some servers are capable of allocating additional service capacity by adjusting dynamically. When a customer arrives at the system, if a free server is available, it is immediately served by one of the free processors; if no server is free, that is all are busy, the arrived job is accommodated in the queue and waits for service in the system. In this paper, we proposed a stochastic model to handle peak loads efficiently, boosting service capacity during burst arrivals. Numerical results presented in this paper are generated by the spectral expansion method to demonstrate the model’s performance, offering insights into its efficiency and accuracy. Furthermore, we derived some important special cases of speedup factor, which provide the mathematical estimation of system performance in terms of computation and communication times.

System Performance Estimation Accounting for Stimulated Raman Scattering in 14.1-THz Bandwidth S+C+L Inline-amplified Transmission

System Performance Estimation Accounting for Stimulated Raman Scattering in 14.1-THz Bandwidth S+C+L Inline-amplified Transmission

Prediction of electric power performance of the exhaust waste heat recovery system of an automobile with thermoelectrical generator under real driving conditions by means of machine learning algorithms

In internal combustion engines, approximately 40% of the thermal power obtained from fuel burning is thrown out to the environment from the exhaust system. Implementations of waste heat recovery systems with thermoelectrical generator over exhaust system have become widespread as it is the waste heat resource with the highest temperature in a vehicle. During literature research, experiments related to waste heat recovery under real road conditions are very few and no study on estimation of system performance by machine learning algorithms has been found; therefore, Toyota Corolla has designed an air-cooled waste heat recovery system using 3 thermoelectrical generators for the exhaust system of the automobile. During the road tests, temperature and electric power generation values obtained as per gear, vehicle speed and engine speed have been recorded. In the study, a dataset consisting of 10 attributes was created with each record as a result of the path test. Using this dataset, Random Forest, Support Vector Machine (SVM) and Naive Bayes machine learning algorithms estimated the electrical power to be generated from the thermoelectric generator recovery system. In the study, 7 electric power classification estimates were made. In the estimation process, 76% of the dataset was used for training and 24% was used for testing. In terms of estimation success; an estimation success of 96.6% has been achieved by Random Forest method; 94.6% by Support Vector Machine (SVM) method; and 76.7% by Naive Bayes method. The results show that prospective electricity generation estimation can be achieved with high level of accuracy.

An statistical model for the short-term albedo estimation applied to PV bifacial modules

Albedo estimation is an essential input parameter for bifacial PV modules. However, there was no clear agreement on which albedo values should be used and how they should be measured, either with satellite measurements or using onsite measurements which can be obtained from nearby meteorological stations. Since long-term measurements are not always available for new PV system locations, short-term albedo measurements are also used as input parameters in PV system performance estimation models. Short-term albedo presents high variability due to factors such as weather, seasonality or changes in the surrounding surface among others. In addition, apparently random albedo variations of 60% can be observed, even during consecutive days or within the same day. Therefore, this study presents a two-parameter exponential model that modelates the albedo data statistical distribution with an error of less than 5% in all cases and from which it is possible to determine the reliability of the obtained data. This model has been evaluated at a set of locations, with different surface types and climates, and assessed using onsite and satellite data. The impact of the methodology used to estimate the albedo data uncertainty and its reliability has also been studied as a function of several parameters such as the global horizontal irradiance and measurement time, allowing the process optimization and enhancing its reliability.

Predicting the energy consumption of a VRF heat pump using manufacturer performance data and limited experimentation for dynamic data collection

The variable refrigerant flow (VRF) air conditioner is widely used because it can control indoor air conditioning units individually, allowing for efficient energy use. However, accurately modeling a VRF system is challenging because of its complex operating mechanism. Despite this challenge, manufacturers often only provide basic system information that adheres to regulatory standards, and they do not typically disclose detailed product specifications. In this study, for describing actual VRF system operation and estimating system performance reasonably, calibrated catalog-based performance estimation model is proposed using a small amount of experimental data. The proposed model uses a machine learning method to predict the power input of a VRF via the XGBoost algorithm. The results show that the prediction performance of the proposed model has an R2 higher than 0.9 and root mean squared error (RMSE) less than 0.2, whereas the typical catalog-based model has an R2 of 0.07 and RMSE of 0.54. The proposed model using only 20 % or more experimental data collected during the 12 h period outperforms the existing catalog-based model significantly.

Characterization of Nonlinear Distortion in Intensity Modulation and Direct Detection Systems

Intensity modulation and direct detection (IM-DD) systems are widely used in short-reach optical networks. Thus, developing a practical and accurate method to characterize nonlinear distortion and to estimate system performance is essential, as this is the foundation of IM-DD system design. The amount of nonlinear distortion can be characterized using the noise-to-power ratio (NPR). Although measuring the NPR using a notch requires only a spectrum analyzer, this method cannot be applied to a nonlinear system with non-Gaussian stimuli in general. We employ the method of measuring the NPR using a notch to characterize the nonlinear distortion in directly modulated distributed feedback (DM-DFB) laser-based IM-DD systems. Nonlinear distortion in these IM-DD systems can be attributed to two specific mechanisms: the imperfect power–current characteristic of the DM-DFB laser and the interaction among the modulation, chromatic dispersion (CD), and detection. Experimental results demonstrated that the nonlinear distortion can be accurately characterized with a mean absolute error of 0.47 dB, even when the stimuli are pulse amplitude modulation. The system performance subjected to nonlinearity could be estimated by the equivalent additive noise model. Besides, the method of measuring the NPR using a notch is also effective when the Mach–Zehnder modulator-based IM-DD system has nonnegligible CD. However, it failed in the case of negligible CD. Considering that this method is not always effective, we analyzed the attributes of various nonlinear systems and found that the above method is applicable only to systems dominated by second-order nonlinearity.

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation.

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

Performance and outlook for the SADAR array network at the Newell County facility

Since installation in November 2021, a sparse network of SADAR compact volumetric arrays has been continuously and persistently acquiring data for passive microseismic monitoring of the Newell County Facility (NCF) CO2 storage pilot site. This report summarises several fundamental results from a year of continuous monitoring of the NCF using the SADAR permanent array network, including an updated seismic velocity model determined from ground truth calibration events, the observed seismicity derived from the curated year-long bulletin, and quantified estimates of system performance. In particular, the bulletin includes more than 560 analyst-verified located events excluding those at the surface, with at least 200 considered to be well-located. We have documented noise reduction provided by coherent processing of SADAR array data in excess of 30 dB compared to surface sensors and signal-to-noise ratio (SNR) improvements up to 20 dB compared to single channels. Combining the bulletin information and performance measurements, we estimate magnitude of completeness Mc ≈ −2.5 Mw, and an event-to-receiver range for locatable events of at least 600 m from individual arrays. We conclude with a look at continuing and planned projects using the SADAR network at the NCF including tests of active source acquisition.

Stochastic simulation uncertainty analysis to accelerate flexible biomanufacturing process development

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.

Measuring camera information capacity with slanted-edges (Invited)

As Machine Vision (MV) and Artificial Intelligence (AI) systems are incorporated to an ever-increasing range of imaging applications, there is a corresponding need for camera measurements that can accurately predict the performance of these systems. At the present time, the standard practice is to separately measure the two major factors, sharpness and noise (or Signal-to-Noise Ratio), along with several additional factors, then to estimate system performance based on a combination of these factors. This estimate is usually based on experience, and is often more of an art than a science. Camera information capacity (C), based on Claude Shannon's ground-breaking work on information theory, holds great promise as a figure of merit for a variety of imaging systems, but it has traditionally been difficult to measure. We describe a new method for measuring camera information capacity that uses the popular slanted-edge test pattern, specified by the ISO 12233:2014/2017 standard. Measuring information capacity requires no extra effort: it essentially comes for free with slanted-edge MTF measurements. C has units of bits per pixel or bits per image for a specified ISO speed and chart contrast, making it easy to compare very different cameras. The new measurement can be used to solve some important problems, such as finding a camera that meets information capacity requirements with a minimum number of pixels, important because fewer pixels mean faster processing as well as lower cost.

Nonlinear noise spectrum measurement using a probability-maintained noise power ratio method

Nonlinear distortion (noise) limits many communication systems, demanding a means of estimating system performance via device nonlinear characteristics. The noise power ratio (NPR) method which was proposed in 1971 solves this problem for systems with Gaussian stimuli or with special nonlinearity, but practical and accurate methods for many communication systems with non-Gaussian stimuli are rare. Here we propose a probability-maintained (PM) NPR method to accurately measure the spectrum of nonlinear noise via a spectrum analyzer in non-specific systems, including systems with non-Gaussian stimuli. Using an equivalent additive noise model in which the spectrum of equivalent nonlinear noise is the measurement result of PM NPRs, nonlinear system performance could be estimated with an error of 0.5 dB. Further, we find that zero-mean Chi-square noise, instead of Gaussian noise, should be selected for large memory and low-order nonlinear systems. Our method is verified in seven different scenarios with various nonlinear mechanisms and communication applications.

Towards a Recommender System for In-Vehicle Antenna Placement in Harsh Propagation Environments.

This paper presents a novel approach to improving wireless communications in harsh propagation environments to achieve higher overall reliability and durability of wireless battery powered sensor systems in the context of in-vehicle communication. The goal is to investigate the physical layer and establish an antenna recommendation system for a specific harsh environment, i.e., an engine compartment of a vehicle. We propose the usage of electromagnetic (EM) and ray tracing simulations as a computationally cost-effective method to establish such a recommendation system, which we test by means of an experimental testbed—or test environment—that consists of both a physical, as well as its identical simulation, model. A pool of antennas is evaluated to identify and verify antenna behavior and properties at specified positions in the harsh environment. We use a vector network analyzer (VNA) for accurate measurements and a received signal strength indicator (RSSI) for a first estimation of system performance. Our analysis of the experimental measurements and its EM simulation counterparts shows that both types of data lead to equivalent antenna recommendations at each of the defined positions and experimental conditions. This evaluation and verification process by measurements on an experimental testbed is important to validate the antenna recommendation process. Our results indicate that—with properly characterized antennas—such measurements can be substituted with EM simulations on an accurate EM model, which can contribute to dramatically speeding up the antenna positioning and selection process.