Performance Evaluation Of System Research Articles

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.

POMACS V8, N2, June 2024 Editorial

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS/IFIP Performance 2024 conference on June 10-14, 2024, in Venice, Italy. These papers were selected during the Winter submission round by the 93 members of the ACM SIGMETRICS/IFIP Performance 2024 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two SIGMETRICS/Performance deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the Winter deadline, POMACS is publishing 10 papers out of 127 submissions, of which 2 had previously received a one-shot revision decision. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 28% of the submissions were in the Theory track, 55% were in the Measurement & Applied Modeling track, 29% were in the Systems track, and 22% were in the Learning track (papers could be part of more than one track). Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/Performance/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinions on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Mor Harchol-Balter, the IFIP Working Group 7.3 Chair, Mark S. Squillante, the previous SIGMETRICS Board Chair, Giuliano Casale, and the past program committee Chairs, Konstantin Avrachenkov, Phillipa Gill, and Bhuvan Urgaonkar, who provided a wealth of information and guidance. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/IFIP Performance 2024.

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.

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.

Modelling photovoltaic modules with enhanced accuracy using particle swarm clustered optimization

Accurately simulating and operating photovoltaic (PV) modules is vital for thoroughly analyzing their performance under different conditions. The main focus of this paper is to address the inherent nonlinearity in solar PV systems. To achieve this, the particle swarm clustered optimization (PSCO) is applied to extract parameters of solar modules, allowing for a more comprehensive understanding of their behavior. PSCO aims to enhance the accuracy and effectiveness of PV module analysis. For that, PSCO utilizes clusters within the particle population, enabling localized communication and information sharing. By doing so, it effectively facilitates efficient exploration and exploitation of diverse regions, fostering a comprehensive understanding of the behavior of PV modules under different conditions. Through this approach, PSCO maximizes the accuracy and effectiveness of parameter extraction, contributing to advancements in PV system analysis and performance evaluation. The effectiveness of PSCO is demonstrated in extracting parameters for the three-diode model (TDM) of the STP6-120/36 and Photowatt-PWP201 PV modules. PSCO surpasses state-of-the-art algorithms with significantly low root mean square error (RMSE) values of 0.0145 A and 0.0019 A, showcasing its superior accuracy. Additionally, PSCO achieves the lowest power errors of 0.16054 W and 0.01484 W for the respective modules, emphasizing its excellent performance.

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

Security Design In Doors And Windows Arduino Based Using Fingerprint And Sms Gateway (Case Study At Pesona Asri Tembalang Housing)

This research discusses security design for Arduino-based doors and windows using fingerprint technology and SMS Gateway. The case study was conducted at Pesona Asri Tembalang Housing. The main goal of this research is to improve household security by combining fingerprint authentication technology and text messaging (SMS) as a safer and more efficient access mechanism.This system development method includes analyzing household security needs, designing an Arduino-based system, integrating fingerprint modules and SMS Gateway, as well as testing and evaluating system performance. The research results show that this system is able to provide a high level of security with accurate fingerprint authentication and direct notification via SMS Gateway when suspicious activity occurs.In conclusion, an Arduino-based door and window security design with fingerprint technology and SMS Gateway is an effective solution for increasing household security. Implementing these systems can help reduce the risk of intrusion and give homeowners better control over access to their property.

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

Modeling of heat and solute transport in a fracture-matrix mine thermal energy storage system and energy storage performance evaluation

Repurposing groundwater-filled mine cavities for thermal energy storage has demonstrated promising potential to buffer the imbalance of energy supply and demand. Fractured formations are widespread in old mines due to previous excavation activities, which dominates the performance and efficiency of the mine thermal energy storage (MTES) system. Therefore, understanding the mechanisms of fluid flow, heat, and solute transport in fractured reservoirs in the MTES system is essential for its application and the assessment of environmental impact. In this study, fluid flow, heat, and solute transport in a 3D fracture-matrix MTES system are modeled simultaneously based on site-specific data in Freiberg, Germany. The simulations are conducted with a coupled Hydro-Thermo-Component process newly developed in the open-source software OpenGeoSys (OGS). To stabilize the simulation in the fracture-matrix hybrid system, a flux-corrected transport scheme is specifically implemented and applied in the model. Thus, heat and mass exchange between the embedded fractures and the matrix in the formation of MTES can be accurately simulated on the basis of the conforming mesh. In a hypothetical short-term scenario of a 15-day heating and cooling cycle of the MTES operation, the solute is transported faster than the heat in the fractured formation, indicating that disturbance of mineral compositions in the original mine water can affect a larger region than heat. The embedded fracture network in the surrounding formation is also found to increase the thermal energy storage capacity by 44%, while decreasing the recovery ratio by 14% compared to those of the unfractured formation. More energy will be transported and stored in the more fractured formation from heated mine water, but less energy can be recovered. The behavior of the MTES system strongly depends on local geology and hydraulic conditions and therefore needs to be evaluated in a site- and application-specific manner. This study provides preliminary insights into the performance and efficiency, as well as the environmental impact of the MTES operation in the short term.

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

Hand Gesture Recognition System Using Deep Learning

Hand Gesture Recognition Systems have undergone significant advancements, ushering in a new era of human-computer interaction. This paper offers a thorough examination of the current state of the art in hand gesture recognition, addressing both the notable progress achieved and the persistent challenges. By leveraging state-of-the-art technologies such as computer vision and deep learning, the paper explores the methodologies employed in data collection, preprocessing, and the implementation of various algorithms. The research delves into the complexities of popular hand gesture datasets, emphasizing their role in training and testing models. A critical analysis of different algorithms and models, including Hidden Markov Models, Support Vector Machines, and Neural Networks, is presented. The paper scrutinizes their strengths and limitations, providing insights into the delicate balance between accuracy and real-time processing. Furthermore, it investigates the diverse applications of hand gesture recognition, spanning from enriching human-computer interaction to its pivotal role in virtual reality, gaming, and robotics. Despite these advancements, challenges persist, such as occlusion, varying lighting conditions, and the imperative for real-time processing. The hardware utilized in hand gesture recognition systems, including depth sensors, RGB-D cameras, and wearable devices, is examined. Evaluation metrics, such as accuracy, precision, recall, and the F1 score, are employed to evaluate system performance.

Carbon and economic prices optimization of a solar-gas coupling energy system with a modified non-dominated sorting genetic algorithm considering operating sequences of water-cooled chillers

To promote the continuous operating of solar energy systems and increase the proportion of renewable energy consumption, solar devices are often integrated with conventional energy systems to reduce greenhouse gas emissions. In this study, a solar-natural gas coupling system is constructed, utilizing solar photovoltaics, thermal collectors, and high-efficiency energy conversion devices. After modelling the energy system, the operating modes are illustrated considering working sequences of water-cooled devices. A modified genetic algorithm, which incorporates reverse learning, controlled elite, and dynamic crowding distance theory, is constructed to optimize the configurations of the proposed energy system, with the aim of minimizing carbon and economic prices of the products. The performance of the energy system is compared with different algorithms and cases, and the results demonstrate that the proposed algorithm addresses the limitations of conventional methods, albeit with increased optimization times. Moreover, the proposed operating mode (Case 1) exhibits a stable performance, leading to a higher environmental and economic performance compared to conventional modes, with carbon and economic prices of 0.242 kg/kWh and 0.067 $/kWh, respectively. This study provides new insights into carbon footprints and economic performance evaluation of high-efficiency energy systems, offering valuable directions for future research in this field.

Performance evaluation and optimization of compact tube-fin latent heat storage system in phase change nanoemulsion discharge mode

The low thermal conductivity of phase change materials (PCMs) and the reduced heat transfer temperature difference along a tube both limit the thermal performance of latent heat thermal energy storage (LHTES). Herein, a compact tube-fin LHTES system based on phase change nanoemulsions was developed and the heat transfer process of nanoemulsion and PCM was numerically investigated. The effects of the heat transfer structure of the heat exchanger and the thermophysical parameters of the heat transfer fluid (HTF) on the cold energy discharge performance of the system were quantified. The optimized model reduced the discharge time by 24.4 % in nanoemulsion mode relative to water. Heat transfer improvement on the PCM side could be achieved by decreasing the fin spacing and tube spacing and by increasing the fin thickness. Such improvement reinforced the heat transfer enhancement of the nanoemulsion. The heat transfer mechanism was explained by the temperature distributions of the HTF and PCM along the tube. The phase-change thermostatic properties of the nanoemulsion and PCM contributed to the formation of dual-phase-change coupled heat transfer (i.e., the heat transfer process when the fluid inside the tube and the PCM filled outside the tube undergo a simultaneous phase change) within the LHTES, increasing the heat transfer temperature difference and the magnitude of the driving force. Increasing the phase change temperature difference accelerated the onset of dual-phase-change heat transfer, while increasing the PCM content prolonged the process. Briefly, this study provided operational and design assistance for LHTES systems in nanoemulsion mode with the intention to accelerate heat transfer rates.

Thermal response and performance evaluation of floor radiant heating system based on fuzzy logic control

The floor radiant heating system is widely used in various buildings due to its advantages such as high efficiency, comfort, and uniform heating. Generally, research on floor radiant heating systems focuses on energy saving, but the issue of slow thermal response during the heating process has not been solved. To solve the problem of slow thermal response and uneven indoor thermal environment in floor radiant heating systems, a fuzzy logic control theory based on optimal fuzzy rules was proposed. The floor radiant heating system based on fuzzy logic control was established by the TRNSYS-MATLAB integrated control platform. The study compared the thermal response and thermal comfort performance of PID control, fuzzy logic control, and optimized fuzzy control methods. The results showed that fuzzy logic control performed better than PID control in controlling the system's thermal performance and indoor thermal environment. Compared to PID control, the optimized fuzzy control reduced the thermal response time by 64.60%, and the total energy consumption of the water pump and heat pump decreased by 222 kWh and 329 kWh, respectively. The proposed fuzzy logic control demonstrates favorable thermal response characteristics and holds substantial potential for energy conservation within the increasingly prevalent floor radiant heating systems.