Performance Evaluation Of System Research Articles

Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis

Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Comprehensive performance evaluation of basic type of lunar habitat

ABSTRACT Amid the burgeoning lunar economy, habitat establishment is vital for exploration. The investigation into lunar habitat morphology requires systematic organization. To systematically and comprehensively delve into lunar habitats morphological design, this study analyzes spatial elements and relationships through the profiling of lunar habitats. It comprehensively analyzes spatial elements and relationships, laying the groundwork for a dedicated performance evaluation system. It provides theoretical and technical references for lunar habitat forms in the early design phase. First, it summarizes four basic types of lunar habitat profiles. Second, it selects 23 factors influencing lunar habitat profile design, using the Delphi method to filter six major ones. Third, various simulation experiments assess each type’s performance under factors. Fourth, the TOPSIS method analyzes comprehensive performance. This study has developed a comprehensive performance evaluation system and an integrated experimental methodology for assessing the architectural profile morphology of lunar habitat structures. Employing the evaluation system, a comprehensive experimental analysis was conducted on four basic types in the current phase. This analysis yielded two design suggestions for profile elements and three optimization directions for profile design. The discussion offers insights for future evaluations, considering mission innovation and technological advancements.

A Study on the Development of Driver Behavior Simulation Dummy for the Performance Evaluation of Driver Monitoring System

AbstractDriver monitoring system (DMS) was mainly developed to prevent accident risks by analyzing facial movements related to drowsiness and carelessness in real time such as driver’s gaze, blink, and head angle through cameras and warning the driver. Recently, the scope has been expanded to monitor passengers, and it has been linked to safety functions such as neglecting children, empty seats, or controlling airbags on seats with people under safety weight. However, evaluation research for algorithm advancement and performance optimization is relatively insufficient. In addition, the verification system is facing limitations such as personal information protection problems caused by the subject’s face data, errors in reproducing the subject’s drowsy and careless behavior, and differences in behavior according to individual differences. Therefore, as the importance of traffic safety is emphasized, an evaluation tool that can more efficiently and systematically evaluate the performance of DMS is needed. In this study, a driver behavior simulation dummy was developed that can quantitatively control the movement of the driver’s face and upper body. The driver behavior simulation dummy was developed in three stages in the order of function and specification definition, design and manufacture according to specifications, and verification through error tests for each function.

Design and fabrication of a novel microswitch made in LTCC-PDMS technology applicable to micro total analysis systems

AbstractThe physical design and implementation of an LTCC on–off microswitch device is demonstrated. One model was used to describe microchannel fluidic behavior, especially those with rectangular cross-section, routinely used in microfluidic devices. Another model to describe PDMS microbridge deformation was applied, allowing for estimating the elastic stiffness of several manufactured membranes. From this approach, microswitch design was conducted regarding the simulation results associated with both models, aiming at miniaturization. Applying LTCC-PDMS fabrication methodology, the fluidic devices were manufactured as established by theoretical design and an experimental setup was proposed in order to evaluate system performance. Experimental investigations indicate that a weight of 120 g was necessary to reduce the water flow rate of the normally open LTCC-PDMS microswitch from 0.42 mL/min to 50 μL/min when submitted to a controllable pressure-driven system. The leakage observed when testing the fabricated microswitches regards the rough way in which the PDMS membrane was attached to the LTCC body. However, experimental results suggest that the novel microswitch can operate satisfactorily regarding micro total analysis applications.

Experiment-Validated multiphysics modeling, generalizable deep learning and interpretable global sensitivity analyses for thermoelectric generators

Despite extensive studies on thermoelectric materials in a thermoelectric generator (TEG), the inverse design of TEG operating conditions remains challenging. Recent efforts in deep learning of TEGs using artificial neural network (ANN) lack global sensitivity analyses and interpretation of the connection-weights, leading to unknown coupling between input parameters and errors in importance ranking. This work reports a deep learning framework for TEGs using generalizable ANN, interpretable local (LSA) and global sensitivity analyses (GSA). Sensitivity analyses are applied to determine how energy conversion efficiency (η) under the practical Neumann and Robin thermal boundary conditions is affected by TEG operating conditions, including hot-side heat flux (qh), cold-side convective heat transfer coefficient (hf), temperature of the cooling medium (Ta), load resistance (RL), electric contact resistance (Rc), and thermal contact resistance (Kc). First, multiphysics TEG simulation results based on 3D finite element method (FEM) are validated by our experimental measurements using a self-built TEG performance evaluation system. The partial derivative-based LSA is done on a 1D theoretical TEG model to obtain the trend in sensitivity factors. Then, an ANN model is trained and validated based on the FEM simulation results and applied to the GSA process, aiming at reducing the computational load. Variance-based GSA is applied on the ANN model to determine the importance ranking and reflect the coupling between input parameters. The variance-based GSA ranks the importance as qh>hf>RL>Ta≈Rc≈Kc, and reflects the coupling between qh and hf. Finally, to interpret the ANN and the hidden deep learning process, a method combining the connection-weights algorithm and neural interpretation diagram (NID) is proposed to extract the hidden information in the connection weights. The ANN model is proven generalizable towards larger ranges of operating conditions. This work provides a framework to design TEG operating conditions towards higher energy conversion efficiencies.

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

Performance Analysis of Moving Target Shadow Detection in Video SAR Systems

The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods.

Design and performance evaluation of Front glass-covered photovoltaics-thermal hybrid system for enhanced electrical output and hot water production

Proof of concept is established for the thermal management of PV modules for the simultaneous benefit of electrical and thermal efficiency. It was achieved by designing the controlled open-loop water-based hybrid PV-T system and demonstrated for thermal management of 150W photovoltaic panel at natural solar conditions. In this integrated front glass-covered PV-T hybrid (IFG-PV-T) system, the PV panel is clipped between the front glass and rear aluminium collector without causing any permanent changes in the existing panel. The flow of water from the source tank is controlled by automated solenoid valve assembly coupled with thermocouples. The solenoid operational temperature is fixed at 40°C to controlled the PV surface temperature at an optimum range to mitigate the adverse effect on voltage and current output. The water layer thickness in the front glass box is optimized to filter the maximum infrared radiations and the collector's toughened glass feature allows the maximum light transmittance with super safety. The performance of the IFG-PV-T system has been evaluated in terms of variation in open circuit voltage, short circuit current, maximum power output, electric and thermal efficiency under the natural solar insolation for a week. Experimental investigations revealed that the open-circuit voltage is increased by ∼16.1 % with IFG-PV-T as compared to conventional PV panel. The increment can be attributed to the synergy of the front glass collector and solenoid valve operation at 40oC that regulates PV surface temperature and boost the current output. These factors have ameliorated the electrical efficiency of IFG-PV-T compared to conventional PV panel. The average thermal efficiency was 39.4 ​% wherein the IFG-PV-T system provides ∼100 ​L of hot water (38–41 ​°C) per day. The present controlled loop operation system and collector configuration have proven their significance for electrical power increment and concomitantly able to deliver moderate hot water which can be useful for any household or commercial application.

The use of pencil ionization chamber with temporal readout capabilities to measure CT beam full width half maximum.

Measurement of Computed Tomography (CT) beam width is required by accrediting and regulating bodies for routine physics evaluations due to its direct correlation to patient dose. Current methods for performing CT beam width measurement require special hardware, software, and/or consumable films. Today, most 100-mm pencil chambers with a digital interface used to evaluate Computed Tomography Dose Index (CTDIvol) have a sufficiently high sampling rate to reconstruct a high-resolution dose profile for any acquisition mode. The goal of this study is to measure the CT beam width from the sampled dose profile under a single helical acquisition with the 100-mm pencil chamber used for CTDIvol measurements. The dose profiles for different scanners were measured for helical scans with varying collimation settings using a 100-mm pencil chamber placed at the isocenter and co-moving with the patient table. The measured dose profiles from the 100-mm pencil chamber were corrected for table attenuation by extracting a periodic correction function (PCF) to eliminate table interference. The corrected dose profiles were then deconvolved with the response function of the chamber to compute the beam profile. The beam width was defined by the full width half maximum (FWHM) of the resulting beam profile. Reference dose profiles were also measured using Gafchromic film for comparison. The beam widths, estimated using the innovative deconvolution method from the 100-mm pencil chamber, exhibit an average percentage difference of 1.6±1.8 when compared with measurements obtained through Gafchromic film for beam width assessment. The proposed approach to deconvolve the pencil chamber response demonstrates the potential of obtaining the CT beam width at high accuracy without the need of special hardware, software, or consumable films. This technique can improve workflow for routine performance evaluation of CT systems.

Design and Performance Evaluation of Network Intrusion Detection System Based on Deep Learning

Today’s internets are made up of nearly half million various networks. In any network connection, detecting attacks by their kinds is challenging task as various attacks may have several connections, their number vary from few to hundreds of network connections. In this paper, Design and Performance Evaluation of Network Intrusion Detection System Based on Deep Learning (NID-SPGAN-STO) is proposed. Initially, input data are collected by NSL-KDD dataset. Afterward, data are fed to preprocessing. In preprocessing, Distributed Set-Membership Fusion Filtering is used to remove redundant and biased records from input data. The pre-processed output is given to feature selection for selecting optimal features utilizing Piranha foraging Optimization Algorithm. Finally the selected features are transferred into the Semantic-Preserved Generative Adversarial Network (SPGAN) for detecting Network Intrusion, like DoS, Probe, R2L, U2R and Normal. Generally SPGAN doesn’t reveal some adoption of optimization techniques for computing optimal parameters for promising precise network intrusion detection. Hence Siberian Tiger Optimisation (STO) is used to enhance weight parameters of SPGAN. The proposed NID-SPGAN-STO method is implemented using Python. To detect network intrusion detection, performance metrics likes precision, sensitivity, FI-score, specificity, accuracy, RoC, computational time are considered. The NID-SPGAN-STO method attains 30.58%, 28.73% and 25.62%, higher precision, 20.48%, 24.73%, 29.32% higher specificity and 30.98%, 26.66% and 21.32% higher F-score, 26.78%, 34.47%, and 22.86% higher recall analysed, with existing techniques likes improved binary gray wolf optimizer with SVM for intrusion detection system in WSNs (NID-SVM-IDS), network intrusion detection system utilizing deep learning (NID-DNN), design with improvement of efficient network intrusion detection system utilizing ML methods (NID-IDS-ANN) respectively.

Does target-based performance evaluation system reduce pollution emissions? Firm-level evidence from China

This paper sheds light on the effectiveness of a concrete target-based performance evaluation system (TPES) for localities in enhancing local environmental enforcement in China. We conduct a difference-in-differences (DID) specification and exploit unique firm-level data to investigate the effects of the TPES on SO2 emission reduction through the Two control Zones (TCZ) policy. The results suggest that the TPES significantly reduces SO2 emissions of firms located at the TCZ cities, both in extensive and intensive margins. We also find that the treatment effects vary largely across enterprise sizes, ownership types, and local economic development. Our findings further verify how the long-standing distortions in the decentralized implementation of nationwide environmental regulations in China are remedied with a proper performance evaluation system.

MAGNO TUNE: ADVANCED ROAD SURFACE RECOGNITION & EMS OPTIMIZATION

Electromagnetic suspension systems represent a revolutionary technology in the automotive industry, offering unparalleled comfort, control, and safety in vehicle dynamics. This project delves into the design, implementation, and performance evaluation of electromagnetic suspension systems used in vehicles and road surface detection. This paper is an overview of traditional suspension systems and their limitations in providing a smooth ride quality while maintaining optimal handling characteristics. It then introduces electromagnetic suspension systems as an innovative solution to these challenges. Key components and principles of electromagnetic suspension systems are discussed, including electromagnetic actuators and sensors for road surface detection. The integration of these components into the vehicle chassis is explained, highlighting the synergy between mechanical and electrical engineering disciplines. Furthermore, the project explores the benefits of electromagnetic suspension systems, such as enhanced ride comfort, improved stability, and adaptive damping capabilities. Overall, this project provides valuable insights into the advancements and applications of electromagnetic suspension systems, paving the way for the future of vehicle ride and handling dynamics.

Retraction Note: The construction of the inheritance performance evaluation system for inheritors of intangible cultural heritage of traditional sports

Retraction Note: The construction of the inheritance performance evaluation system for inheritors of intangible cultural heritage of traditional sports

Performance evaluation of the suspension system on Maglev trains based on measurement data

Performance evaluation of the suspension system on Maglev trains based on measurement data

Research on Stakeholder Behavior Strategy in Green PPP Project Financing

In the context of green development, while promoting green industry and sustainable development, green PPP faces the challenge of financing difficulties. Based on the evolutionary game theory of bounded rationality and taking government, social capital and financial institutions as the game players, this paper constructs an evolutionary game model for the behavioral decision-making of stakeholders in green PPP project financing. Using MATLAB software, we simulate and analyze the key factors affecting the behavioral strategies of each game player. The results indicate that: (1) The additional income obtained from participation in green PPP projects is a key factor affecting the decision-making of social capital and financial institutions, so it is necessary to improve the reasonable return mechanism of green PPP projects and attach importance to the construction of reputation mechanism; (2) Government subsidies can effectively encourage social capital and financial institutions to participate in green PPP projects, but the amount of subsidies should be within a reasonable range; (3) The government's reward and punishment intensity is a significant factor affecting the decision-making of social capital behavior. To this end, it is imperative for the government to enhance the performance evaluation system for green PPP projects and establish a balanced reward and punishment mechanism. This paper puts forward some suggestions on the decision-making of stakeholders in the financing process of green PPP projects, and provides references for the policy formulation of encouraging social capital and financial institutions to participate in green PPP projects.

Optimizing Nanofluid Hybrid Solar Collectors through Artificial Intelligence Models

This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R2) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R2 values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R2 values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems.

Heat flow topology-driven thermo-mass decoupling strategy: Cross-scale regularization modeling and optimization analysis

The liquid desiccant dehumidification (LDD) is both eco-friendly and highly effective in humidity control, widely investigated across diverse angles. This study expands the application of the newly devised heat current method to examine the liquid desiccant dehumidification process. It introduces a new regularized temperature, which includes using a heat flow topology driven thermo-mass decoupling strategy, and constructing the heat current model by defining both the thermal resistance and moisture resistance. It significantly extends the application of the heat current method, clarifying the thermo-mass coupling energy transfer mechanism. The findings indicate that key factors influencing system performance indicators include total thermal conductivity (αA), solution mass flow rate (ms), air-solution mass flow rate ratio (RA/S,D), and heat source temperature (tH). Furthermore, under the influence of distinct thermal conductivities, the optimal output values for performance indicators, including moisture removal rate, exergy efficiency and entransy efficiency, are determined as 8.4 g/s, 71.2%, and 12.3%, respectively. The results of synergistic influence analysis reveal that, for the performance indicator entransy efficiency, msand RA/S,D display a significant degree of synergistic influence, resulting in a strong positive effect and benefiting 68.53% of the total area. Multi-objective optimization indicates a trade-off relationship among the system performance evaluation indicators moisture removal rate, exergy efficiency and entransy efficiency, with optimal solution values of 3.25 g/s, 65.92%, and 12.01%, respectively.

Performance evaluation of off-grid solar systems for critical medical instruments in remote regions

In areas with limited access to electricity or during power outages, ensuring continuous and reliable use of grid-powered electrical instruments is challenging, especially in remote emergency centers. A stable source of electricity is crucial to prevent disruptions in power supply, as they can lead to severe medical complications, jeopardize patient well-being, and even result in fatal incidents. To address this issue, integrating renewable electricity sources such as off-grid solar systems into existing power supply infrastructure can be a viable solution. Recent studies suggest that off-grid solar systems can be reliable and sustainable options for powering medical facilities, particularly in areas lacking uninterrupted grid connectivity. Therefore, this research aims to evaluate the feasibility, benefits, and challenges of integrating off-grid power to support critical medical instruments. Based on an extensive literature review, interviews with experts, and surveys among relevant users, this study finds that the system has the capability to maintain a consistent, dependable power supply, even during adverse weather conditions. Cost and feasibility are highlighted as paramount considerations, based on surveys and focus group results. The study concludes that off-grid solar systems offer an economical, low-maintenance, and environmentally sustainable alternative for remote medical facilities. Finally, this study proposes recommendations to optimize critical device performance during power outages and improve overall system functionality and efficiency.

Performance evaluation of IRS-assisted one-bit control-based mixed FSO-RF communication system

Performance evaluation of IRS-assisted one-bit control-based mixed FSO-RF communication system