System Performance Indicators Research Articles

A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance, and safety. The results of the questionnaires are statistically analyzed. In addition, an exploratory factor analysis was conducted to extract the data of the study, which were ultimately used to calculate the weight and importance of each index using the fuzzy hierarchical analysis method. This study constructed an evaluation system for AI painting software based on GAN technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety, respectively, whose alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, 72.606%, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance, and safety can enhance the market competitiveness of the software.

Survey of Reliability Challenges and Assessment in Power Grids with High Penetration of Inverter-Based Resources

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.

Analisis Indeks Kinerja Jaringan Saluran Irigasi Studi Kasus Irigasi Sungai Siulak Deras Kabupaten Kerinci

The Siulak Deras Irrigation canal network is the main source of water distribution in several areas. The Siulak Deras Irrigation Area in Kerinci Regency was built in 1993 with water flow regulation and complete Siulak Deras Irrigation network facilities including the Technical irrigation group. As time goes by, the service function of the irrigation network experiences a decline in performance, so that operation and maintenance of the irrigation network is an activity that is really needed to make the irrigation network remain optimal and maintain its performance. In this regard, an assessment of the Performance Index of the irrigation system in the Siulak Deras Irrigation Area needs to be carried out to determine the value of aspects or components that are not yet optimal in this irrigation area so that special treatment can be immediately carried out or improved. The assessment is carried out by starting an inventory of the condition and function of the physical infrastructure of the irrigation network, planting productivity results, supporting facilities, organization, documentation and institutional P3A to determine the performance value and recommendations for handling it. The results of the irrigation network system performance index in the Siulak Deras irrigation area for the Left Irrigation Channel section are 69.59%, which means that the irrigation system performance is close to good performance, namely 70%, but still needs to be improved and needs attention. With a physical infrastructure value of 29.32%, planting productivity 14.21%, supporting facilities 6.15%, personnel organization 10.94%, documentation 2.46%, and P3A institutions 6.50%. Based on the performance index value obtained, and the total performance score in the form of a percentage, it is necessary to specifically maintain and repair physical facilities and infrastructure at several points that are damaged, facilitate supporting facilities for OP officers so that officers can act quickly and precisely in carrying out activities, and paying more attention to P3A so that it can always contribute to maintaining and utilizing the Siulak Deras irrigation network properly and optimally in the future.

A dynamic framework for contractors to measure the performance of highway maintenance based on grey theory

Highway maintenance performance measurement is the key to implementing performance-based contracting (PBC). However, contractors have struggled with accurately understanding client intent and translating performance goals into measurable indicators. To make PBC successfully applied, this study proposes a dynamic performance measurement framework of highway routine maintenance based on grey theory. A performance indicator system is established based on the organizational structure of highway assets and the bill of quantities in the real world. In addition, a statistical analysis using the data about maintenance cost and highway condition score has been conducted to ascertain the weights and thresholds assigned to various indicators. As a result, the performance indicators, their corresponding weights, and thresholds can be flexibly adjusted to accommodate diverse periods and areas. Then, the grey theory has been employed to evaluate the comprehensive performance level of service within a region. This framework is experimented with using the data of three maintenance areas in Shaanxi Province, China: Qiaogouwan, Huaziping, and Ansai. At the end of the process, comparison and sensitivity analysis are conducted. The results demonstrate the model’s reasonability and robustness, providing valuable tools for contractors to assess the performance of highway maintenance in meeting client specifications.

Comparative Analysis of Environmental Sustainability and Performance Indices of Different Gas Turbine Inlet Cooling Systems

Comparative Analysis of Environmental Sustainability and Performance Indices of Different Gas Turbine Inlet Cooling Systems

Prediction of key performance indicators of multi-effect vacuum membrane distillation systems integrated with PVT and Organic Rankin Cycle

The study presented a numerical simulation conducted using MATLAB® to analyze the performance of a poly-generation system. This system comprises solar collectors, a Photovoltaic-Thermal (PVT) system, an Organic Rankine Cycle (ORC), and a multi-effects vacuum membrane distillation (V-MEMD) system. The study investigates the impact of changes in operating conditions of the water stream on the performance of the V-MEMD system. The optimal performance values observed include a water flux of up to 13 kg/ m2·h, a GOR of 5.5, a SEEC of 49 kWh/ m3, and a STEC of 145 kWh/ m3, with each parameter reaching its optimum at different operational settings. The paper explores the impact of different working fluids in the ORC system on water and power generation. It also discusses recent research on combined PVT and ORC systems in Riyadh, Saudi Arabia, considering the region's specific meteorological conditions. The findings reveal that the highest electricity generation occurs in June, with the ORC system producing 18,540 kWh when R123 is used and the PVT system generating 13,536 kWh. The cumulative electricity output for the year reaches 266,564 kWh. Moreover, the V-MEMD system produces 1333 m3 of water over the year, with the highest water yield of 178 m3 observed in June.

Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions

Mortality during the post-weaning phase is a critical indicator of swine production system performance, influenced by a complex interaction of multiple factors of the epidemiological triad. This study leveraged retrospective data from 1723 groups of pigs marketed within a US swine production system to develop a Wean-Quality Score (WQS) using machine learning techniques. The study evaluated three machine learning models, Random Forest, Support Vector Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day mortality, where high mortality groups represented 25 % of the groups among the study population with the highest mortality values (n=431; 60-day mortality=9.98 %), and the remaining 75 % of the groups were of low mortality (n=1292; 60-day mortality=2.75 %). The best-performing model, Random Forest (RF), outperformed the other ML models in terms of accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for further analysis, which consisted of creating the WQS and ranking the most important factors for classifying groups as high or low mortality. The most important factors ranked through the RF model to classify groups with high mortality were pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as stocking density and time to fill the barn were important predictors of high mortality. The WQS was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a valuable tool for assessing post-weaning survivability in swine production systems before weaning. This study highlights the potential of machine learning and comprehensive data utilization to improve the assessment and management of weaned pig quality in commercial swine production, which producers can utilize to identify and intervene in groups, according to the WQS.

Application of Online Flow Cytometry for Early Biofouling Detection in Reverse Osmosis Membrane Systems.

Biofouling poses a significant challenge to reverse osmosis (RO) membrane systems, necessitating timely detection for effective control. This study evaluated the efficacy of flow cytometry (FCM) for early biofilm detection in comparison to conventional system performance indicators. Feed channel pressure drop and total cell concentration in the Membrane Fouling Simulator (MFS) flowcell cross-flow outlet water were monitored over time as early biofouling indicators. The results demonstrated the potential of increased bacterial cell concentration in cross-flow outlet water as a reliable indicator of biofouling development on the membrane. Water outlet monitoring enabled faster biofouling detection compared to feed channel pressure drop. Membrane autopsy confirmed biofilm presence prior to the pressure drop increase, highlighting the advantage of early detection in implementing corrective measures. Timely intervention reduces operational costs and energy consumption in membrane-based processes.

Measurement of safety state of cross-jointed segmental lining based on system performance index

The correlation between convergence deformation and the safety state of a cross-jointed segmental lining is investigated and clarified. The limitations of convergence deformation as a measuring scale is explored also. Firstly, an analytical algorithm (known as the relative stiffness method, RSM) is developed and verified for tracing the mechanical response of a cross-jointed segmental lining in failure history. Then, an explicit mapping relationship between convergence deformation and the causal factors is established using the RSM. Finally, the root causes of the limitations in using convergence deformation as a scale to evaluate tunnel safety state are discussed, and an alternative quantity, the system performance index (SPI), is proposed. It shows that: (1) The convergence deformation consists of three components induced by segment bending, elastic joint rotation, and joint stiffness attenuating, respectively. The deformation induced by attenuating joint stiffness is dependent on the failure index of segment joints and provides crucial information about the load-bearing state of the segment joints; (2) The components of elastic joint rotation and joint stiffness attenuating are significantly affected by the contact stiffness of the segment joint interface, including the physical and mechanical properties of the packing material. The magnitudes of convergence deformation and its components are not suitable for use as a quantitative scale to evaluate the safety state of the lining due to their inherent drawbacks; (3) The proposed dimensionless variable SPI can eliminate the influence of packing material on the safety state of the segment lining and reflect the comprehensive influence of the failure indexes of the segment joints.

Experimental development of a method of short and medium-term photovoltaic generation forecasting using multivariate statistics and mathematical modeling

Uncertainties in photovoltaic solar energy production can make it challenging to dispatch energy into the electricity grid. Although photovoltaic generation storage solves this problem, a forecasting of the photovoltaic solar energy produced is necessary to control the energy injected into the grid. This article aims to develop the probabilistic methodology Reduced-Rank Regression (RRR) for forecasting photovoltaic generation in the short and medium terms. The RRR methodology forecasting uses the generation data of a grid-connected photovoltaic system. The proposed RRR model is simple, easy to access and apply, and does not use irradiance data. The model developed uses the multivariate statistical analysis technique. A advantage is that with a correlation with the performance indices of photovoltaic solar energy systems, the proposed method can be applied in any geographical location on the planet and with different photovoltaic solar energy systems. The application of the RRR methodology requires two searches/inputs. The first input is weather forecast data obtained from a weather forecasting platform, and the second is actual historical data on photovoltaic generation at the site where the method was developed. The proposed method was compared with the persistence method. Using a horizon of 1–10 h, the average monthly root mean square error for the RRR ranged from 7.3 % to 50.1 %. For the persistence method, the average monthly root mean square error ranged from 15.1 % to 65.0 %. Therefore, with the horizon of 24 h, the average monthly root mean square error for the RRR ranged from 4.5 % to 43.2 %. For the persistence method, the average monthly root mean square error ranged from 11.5 % to 75.0 %. We show experimentally that our method is competitive with the state-of-the-art in terms of obtaining photovoltaic generation forecasting without using solar radiation data.

Optimizing domestic energy management with a wild Mice colony-inspired algorithm: Enhancing efficiency and coordination in smart grids through dynamic distributed energy storage

Energy management (EM) is a critical strategy that spans the production and consumption of electricity, enhancing the stability of the electricity network. Smart grid technology significantly improves the electrical system's energy efficiency (EE), facilitating a transformation from a conventional power grid (PG) to a smart PG. This paper presents a key strategy for modeling the EE of the smart grid tailored to domestic demand, establishing smart coordination between domestic demand, energy production, and storage to reduce energy waste and costs. Our model integrates various energy sources, including renewable energy (RE), photovoltaic (PV) systems, wind power, and an energy storage system (ESS), interconnected with the PG. The model's structure ensures the coordinated flow of electricity in a residential house through an optimal control method (OCM). To develop a robust closed-loop control model, we employ Demand Response (DR) schemes within the Real-Time Electricity Pricing (RTEP) framework. We construct a dynamic model of the ESS to compute the System Performance Index (SPI), corresponding to energy costs. To enhance our model, we introduce a Dynamic Distributed Energy Storage Strategy (DDESS). Additionally, we introduce a novel optimization algorithm inspired by the behavioral patterns of wild mice, called the Wild Mice Colony (WMC). By analyzing the targeted and advantageous behaviors of wild mice in colonies, we propose that these behaviors can serve as a model for addressing complex, uncertain problems. This strategy is highly advantageous, capable of reducing total energy consumption (EC) from the main grid by over 100 % of the load demand, optimizing the energy system, and ensuring synchronization. The performance of DDESS optimizes energy flow (EF) during the repayment plan, leading to minimized EC costs from the PG.

Finite MX/M/C Queue with State Dependent Service, Two-Class Arrivals and Impatient Customers

Objectives: 1. To compute the transition probabilities for the queueing system to be in different states. 2. To calculate the average system length and mean waiting time under the specified parameters. 3. To investigate and test the impact of system characteristics on the anticipated system length as well as mean waiting times. Method: MATLAB software is used for the Runge-Kutta method to calculate probabilities and system constants. Findings: An increase in Type-I arrival rate λ1 and Type-II arrival rate λ2 will cause both mean waiting times and queue length to increase, and they are reduced with an increase in service rates µ1 and µ2 of Type-I and Type-II classes. A rise in parameters such as special service rate µ3, probability of balking and reneging will result in a reduction in both mean waiting times and queue length. Novelty: This article covers a batch arrival finite Markovian queueing system with multi-server support and consumer impatience. In real time, all arrivals need not enter the queue at the same arrival rates, as it is based on the needs of the customer. In view of this, we have assumed two types of customers along with two different rates of arrival and state-dependent service. We did our work in transient mode, as time can influence the waiting lines. Also determined the system's performance indices and demonstrated the impact of the input parameters on the system's constants. Keywords: Batch Arrival, Customer Impatience, Two Class Customers, State Dependent Service, Multi Server Facility

Energy, exergy, economic, and environmental analyses and optimization of a multigeneration system considering high-temperature island environment

Renewable energy based multigeneration systems help satisfy the diverse demand for marine resources exploration in off-grid island area, where the storage of fluctuating renewable energy for continuous power supply becomes necessary. Therefore, this paper proposes a solar-driven multigeneration system for island environment integrated with energy storage and production of electricity, cooling, heating, and freshwater. The system comprises an ejector power subsystem, a multiple-effect distillation subsystem, and a CO2 binary mixture-based compressed gas energy storage subsystem. The system performance from the perspectives of energy, exergy, economics, and environment, as well as the impact on performance of the working fluid and several key system parameters, are investigated in this paper. According to the results, an increase in the mass fraction of organic in the CO2 mixture leads to an improvement in system energy efficiency and exergy density at the cost of lower system power efficiency. Increasing the high-pressure tank pressure enhances system performance except for system power efficiency. Increased low-pressure tank pressure leads to improved system energy efficiency and exergy density, accompanied by lower system power efficiency and exergy efficiency. When the ambient temperature increases from 298 K to 303 K, the system energy efficiency noticeably decreases, whereas an increase in direct normal irradiance improves various system performance indicators. Among several mixtures, CO2/R32 mixture was chosen for further analysis and optimization for its wider operating range with R32 mass fraction and better system performance. The optimal operating point had system energy efficiency, exergy density, and modified levelized cost of energy of 1.281, 22.216 kWh/m3, and 0.131 $/kWh, respectively. The proposed system in this research is proven to be a novel technical solution to meet the diverse energy demands on unique island environments.

Key performance indicators of marketing mix effectiveness in the pharmaceutical business

The article is devoted to the study of determining key performance indicators of the marketing mix effectiveness in the pharmaceutical business. The problems and contradictions in ensuring the effectiveness of marketing in pharmaceutical companies are emphasized. Theoretical and applied foundations for implementing the marketing mix in the pharmaceutical business are highlighted. The expediency of following the «5Ps» and «6Ps» marketing mix is justified due to their correspondence to the specific features of marketing in the pharmaceutical business. Based on the research, a system of key performance indicators of the marketing mix effectiveness in the pharmaceutical business is identified.

Commercialization Efforts of Capacitive Deionization Technology in Water Treatment Processes

AbstractAlthough capacitive deionization (CDI) technology has been studied intensively for more than 20 years, its commercialization remains in the initial stage, which is partly caused by the insufficient knowledge exchange between academia and industry. This concept reviews multiple scaling‐up efforts in the CDI technology for treating real water streams, following a case‐by‐case fashion. While the cell architecture in pilot scales is limited to the membrane CDI, highlighting the necessary role of ion‐exchange components during scaling‐ups, different ways of electrode stacking, i. e., monopolar and bipolar, are available. The performance indicators of these CDI systems when treating real water streams are summarized to gain insights into industrial practices. Importantly, key discrepancies in cell components and performances between pilot‐scale and lab‐scale studies are emphasized. The main challenges in large‐scale CDI systems for industrial purposes are discussed, providing hints for a better integration of research and applications.

Stochastic modeling and optimization of discrete-time cold standby repairable systems with unreliable repair facility and retrial mechanism

This study develops novel reliability models for discrete-time cold standby repairable systems with unreliable repair facility and retrial mechanism. In view of the situation that the state of some engineering systems cannot be continuously monitored, discrete distribution is used to describe the distribution of random variables involved in this paper. For the case that multiple events can occur simultaneously in the discrete-time system model, the priority order of multiple events occurring simultaneously is defined. Two different models are proposed based on two types of priority rules. The retrial mechanism of the components is considered in the case that the failure information of the system components cannot be successfully transmitted to the repair facility. The preventive maintenance (PM) and repair after the breakdown of the repair facility are considered. The key reliability indices are derived by using the iterative algorithm of the difference equation and the generative function method. The algorithms for calculating system state probability, key reliability indices and cost–benefit ratio (CBR) are designed. To demonstrate the impact of each parameter on the system reliability indices, numerical results are provided. The optimal repair rate configuration scheme is chosen using the Particle Swarm Optimization (PSO) algorithm to achieve the lowest possible CBR. The impact of the system model with or without PM on stationary availability and CBR is shown. In addition, the impact of the number of system components on a series of system performance indices is analyzed, and the optimal number of components of the system corresponding to the minimum value of CBR is obtained.

Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement.

Innovative and efficient integrations of desalination plants coupled absorption, adsorption, and humidification-dehumidification desalination units employing external heat recovery techniques

Thermal desalination technologies have been developed recently to solve some pressing problems, such as high energy expenditure, increasing fuel costs, and environmental problems related to conventional plants. However, these technologies still need to be enhanced in terms of water production and gain output ratio (GOR). This paper presents innovative and efficient combined thermal desalination plants consisting of absorption (ABDS), adsorption (ADDS), and humidification-dehumidification (HDH) desalination units to enhance energy utilization, water production, and overall system performance. Four innovative combined desalination plants employing different external heat recovery scenarios are presented and compared. The result from each scenario is evaluated and compared with those of the standalone ABDS and with other studies found in the literature at the same heat source temperatures. The heat source is mainly utilized to drive the ABDS plant. The ADDS and HDH are driven by the heat rejection from the ABDS components with different external heat recovery scenarios. Results illustrated that the maximum freshwater production generated from the proposed scenarios ranges between 1.57 and 2.94 m3.day−1 with a GOR of between 1.38 and 1.75 at 120 °C. Raising heat source temperatures from 55 to 120 °C decreased the improvement in the water productivity from 618.5 to 224 % and decreased the enhancement in the GOR from 222.8 to 99 %. In scenarios where the ADDS and HDH are driven by the heat rejection from the absorption and condensation process of the ABDS, the maximum freshwater production results from the ABDS unit. On the other hand, in scenarios where the ADDS and HDH are driven by the outlet hot water from the ABDS-generator, the maximum freshwater production is obtained from the HDH unit. The finding provided that the proposed innovative scenarios have an effective improvement in overall system performance indicators.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization–Long Short-Term Memory Model

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.