Performance Index Research Articles

Cases of PD-CFPID selector for frequency adjustment of integrated power grid

In current article an improved salp swarm algorithm (SSA) has been suggested. Likewise, the adjustable scaling-component is locked in to manage salp’s situation in time of the hunt system to limit arbitrary development salps. The upgraded salp swarm method combines random objection-based learning, multiple management, and simulated hardening in swarm brilliant-based metaheuristic salp swarm method, which rises the exploration and exploitation of the primary salp swarm method. To exhibit viability of the improved SSA, a bunch of multichannel test capabilities are locked in. A partially decentralized combined fuzzy proportional integral derivative (PD-CFPID) regulator is planned for integrated grid. A partially decentralized robust control plan is introduced to deal with a class of multi-inputs multi outputs (MIMO) non-square, linear framework different systems. Partially decentralized command is a control composition that lies between a completely decentralized structure and a wholly centralized one, and has the primacy of achieving comparable performance as an entirely centralized regulator but with simpler shape. The proposed flexible partially decoupled command scheme works in a straightforward and systematic way. Simulations show that the proposed partially decentralized control performs well for the nonlinear system, nearly equivalent to the multivariable control, and they achieve better performance indexes. Simulation outcomes appear that the stated method is simple and can reach better performance. The results of the PD-CFPID regulator are compare among CFPID and PID as many circumstances of the presented control outlook.

Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems.

Improved deep learning based state of charge estimation of lithium ion battery for electrified transportation

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in the battery management systems (BMS) of electrified vehicles. To ensure the safe and reliable operation of an electric vehicle, it is essential to determine the state of charge (SOC) among the battery's numerous states. Deep learning models have been effective for SOC estimation due to the nonlinear and time-varying characteristics of a battery management system. This article seeks to describe an SOC estimator design method using deep neural network (DNN) models, particularly non-recurrent and recurrent neural networks, owing to their excellent computational intelligence and capacity for self-learning. A thorough investigation has been conducted at various temperatures, and the model is trained on data received from the cell during discharging processes like the urban dynamometer driving schedule (UDDS). Finally, the accuracy of the models is compared based on various performance indices. From the results, it has been concluded that the DNN models acquire a Mean Absolute Error (MAE) of less than 1 % and a Root Mean Square Error (RMSE) of close to 1 % when validated over various datasets at temperatures of 10 °C, 25 °C, and 40 °C.

Proteomics discovery in children and young adults with HIV identifies fibrosis, inflammatory, and immune biomarkers associated with myocardial impairment.

People with HIV are at increased risk of cardiac dysfunction; however, limited tools are available to identify patients at highest risk for future cardiac disease. We performed proteomic profiling using plasma samples from children and young adults with perinatally acquired HIV without clinical cardiac disease, comparing samples from participants with and without an abnormal myocardial performance index (MPI). We identified four proteins independently associated with subclinical cardiac dysfunction: ST2, CA1, EN-RAGE, and VSIG2.

Performance analysis of different control models for smart demand–supply energy management system

Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction

ABSTRACT In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error.

Development of a chemiluminescence detection technique for malachite green

AbstractMalachite green (MG), a triphenylmethane dye is often used as a fungicide and preservative in fisheries due to its effectiveness against water molds in fish and fish eggs. However, excessive inhalation can be hazardous to human health. To quantify the MG concentration, we created and evaluated a 96‐well plate‐based chemiluminescence immunoassay (CLIA). This method used provided readings in <30 min, with an optimal incubation time of 15 min and a limit of detection of 0.20 ng·ml−1. The strong correlation (R2 >0.99) between the measured values of real fish samples examined using the method and the high‐performance liquid chromatography results confirmed the accurate quantitative detection of MG. In this study, CLIA was also used in conjunction with point‐of‐care testing (POCT) to greatly improve the efficiency of the experiments. Thus, a quantitative detection method based on MG plate chemiluminescence was established herein, with performance indexes that meet the requirements of on‐site detection. This approach is also applicable to the detection of small molecule compounds such as chloramphenicol and sulfadiazine pyrimethamine, providing a new direction in the field of food safety detection.

Performance Improvement of a Limaçon Gas Expander Using an Inlet Control Valve: Two Case Studies

Renewable energy-based compact energy-generation systems based on the organic Rankine cycle (ORC) can be employed to meet the ever-growing thirst for affordable and clean energy. The overall performance and effectiveness of ORC systems are constrained by the low efficiency of the gas expander, specifically the positive displacement expander, which is responsible for energy conversion from the working fluid. This low-efficiency scenario can be significantly improved by employing a control valve to regulate and restrict the flow of the working fluid into the expander. A control valve can effectively curve the loss of costly compressed and energized working fluids by allowing them to expand in the expander chamber before discharging through the outlet port. They can thus be used to regulate the amount of energy yield and output power. In this work, two direct drive rotary valves (DDRVs) operated by a stepper motor (SM-DDRV) and rotary solenoid (RS-DDRV) are suggested, and the behavior of the valves is examined. The effect of friction and temperature on the valve response is also studied. Additionally, the effect of inlet control valves on the overall system performance of the limaçon expander is assessed. Thermodynamic properties such as the isentropic efficiency and filling factor are also computed. The effect of leakage due to valve response delay is analyzed at different inlet pressures. The performance indices are compared to the expander performance without any inlet valve. The SM-DDRV setup results in a 14.86% increase in isentropic efficiency and a 220% increase in the filling factor, whereas the RS-DDRV performs moderately with a 2.58% increase in isentropic efficiency and an 80% increase in the filling factor compared to a ported expander. The SM-DDRV provides better performance indices compared to the RS-DDRV and without valve setups. However, the performance of the limaçon expander with the SM-DDRV is sensitive to the inlet pressure and degrades at higher pressure. Overall, the valves proposed in this work present key insights into improving the performance characteristics of gas expanders of ORC systems.

Recent advances in multifunctional electrochromic devices

AbstractElectrochromic (EC) technology has been regarded as a promising energy‐saving technology in various applications, including smart windows, displays, thermal management, rear views, etc. Benefiting from the progress in electrochromic material synthesis, electrochromic electrode fabrication, and electrochromic device configuration design, the focus in electrochromic community has gradually shifted to multifunctional electrochromic devices (ECDs) in the era of Internet of Things. Multifunctional ECDs, such as electrochromic energy storage devices, multi‐color displays, deformable ECDs, smart windows, etc. have been showcased the ability to expand potential applications. In this review, the available device configurations, performance indexes and advanced characterization techniques for multifunctional ECDs are introduced and classified accordingly. The applications of multifunctional ECDs for energy storage, multicolor displays, deformable devices, self‐chargeable devices, smart windows, actuators, etc., are exemplified. The future development trends and perspectives of multifunctional ECDs are also overlooked. The aim of this review is to guide and inspire further efforts in the exploration of novel and advanced multifunctional ECDs.

Monopoly pricing with unknown demand

AbstractThe optimal pricing of goods, especially when they are new and the innovating firm is a monopolist, must proceed without precise knowledge of the demand curve. This paper provides a pricing method with a relative robustness guarantee by maximizing a performance index which amounts to a worst‐case ratio of the obtained payoff to the best possible payoff. Assuming monotonicity and complementarity of demand in price and the unknown demand parameter, the performance index is fully determined by its behavior at the boundary of the parameter space. This allows for an efficient computation of an optimal robust price. In the linear case, which can also be used for nonlinear demand with bounded slope, the method provides a simple closed‐form solution. A comparison with the standard worst‐case payoff criterion reveals substantial improvements in both absolute and relative performance, at only a small cost relative to the maximized expected profit.

Distributed Hybrid Electric Propulsion Aircraft Design Based on Convex Optimized Power Allocation Management

In order to ensure that aircraft have medium and long-range flights, enhanced aerodynamic performance, and reduced fuel consumption, this paper presents an original Distributed Hybrid Electric Propulsion Aircraft (DHEPA) design scheme and proposes a novel power allocation management method based on convex optimization. Firstly, by taking the Tecnam P2006T general-purpose aircraft as a reference, key components of DHEPA are selected and modeled. Then, a power allocation management method for DHEPA is proposed on the basis of convex optimization, which takes the minimum fuel consumption as the performance index to realize the reasonable power allocation of the battery and engine, while avoiding sliding into the local optimum of allocation. Finally, momentum theory and numerical simulation methods are used to analyze the aerodynamic enhancement effect of the propeller on the wing in the DHEPA, and a dynamics method is utilized to calculate the dynamics performance of the aircraft at several important stages. The results show that, compared with the reference aircraft, the lift of the DHEPA is increased by 46%. Under typical sectors, the DHEPA has a higher rate of climb and maximum leveling off speed at cruise, and a significantly lower fuel consumption.

Do Acoustic Characteristics of Dysarthria in People With Parkinson's Disease Differ Across Languages?

Cross-language studies suggest more similarities than differences in how dysarthria affects the speech of people with Parkinson's disease (PwPD) who speak different languages. In this study, we aimed to identify the relative contribution of acoustic variables to distinguish PwPD from controls who spoke varieties of two Romance languages, French and Portuguese. This bi-national, cross-sectional, and case-controlled study included 129 PwPD and 124 healthy controls who spoke French or Portuguese. All participants underwent the same clinical examinations, voice/speech recordings, and self-assessment questionnaires. PwPD were evaluated off and on optimal medication. Inferential analyses included Disease (controls vs. PwPD) and Language (French vs. Portuguese) as factors, and random decision forest algorithms identified relevant acoustic variables able to distinguish participants: (a) by language (French vs. Portuguese) and (b) by clinical status (PwPD on and off medication vs. controls). French-speaking and Portuguese-speaking individuals were distinguished from each other with over 90% accuracy by five acoustic variables (the mean fundamental frequency and the shimmer of the sustained vowel /a/ production, the oral diadochokinesis performance index, the relative sound level pressure and the relative sound pressure level standard deviation of the text reading). A distinct set of parameters discriminated between controls and PwPD: for men, maximum phonation time and the oral diadochokinesis speech proportion were the most significant variables; for women, variables calculated from the oral diadochokinesis were the most discriminative. Acoustic variables related to phonation and voice quality distinguished between speakers of the two languages. Variables related to pneumophonic coordination and articulation rate were the more effective in distinguishing PwPD from controls. Thus, our research findings support that respiration and diadochokinesis tasks appear to be the most appropriate to pinpoint signs of dysarthria, which are largely homogeneous and language-universal. In contrast, identifying language-specific variables with the speech tasks and acoustic variables studied was less conclusive.

Optimizing reverse osmosis desalination from brackish waters: Predictive approach employing response surface methodology and artificial neural network models

A mathematical model frame has been developed to optimize the performance of reverse osmosis (RO) desalination in brackish waters with medium to high salt concentrations using response surface methodology (RSM) and artificial neural network (ANN), specifically catered towards efficiently operating RO desalination systems. The RSM model aims at predicting RO performance with second-order polynomials based on 15 experimental datasets. Inputs for RSM model include feed TDS concentration, feed water flow rate, and concentration-to-desalination ratio, while the outputs consider rejection, membrane flux, performance index, and specific energy consumption. The Genetic Algorithm optimized back-propagation Neural Network was employed to update network weights and biases, leading to an optimal ANN network structure of 3–10-4. A ratio of 70 % training, 15 % validation, and 15 % test data was chosen to establish an ANN model that predicts the performance of RO desalination. The ANN model demonstrated significantly higher prediction accuracy than the RSM model, with impressive correlation coefficients of 0.9936 for the performance index and 0.9932 for SEC prediction, and low RMSE of 0.85 for rejection. To maximize the performance index and minimize specific energy consumption, a two-step optimization method was further set up by applying the RSM model to initially determine optimal process conditions followed by the ANN model to enhance the accuracy of performance forecasts. Optimal process conditions were proposed for specific feed TDS scenarios, which involved setting a feed TDS concentration limit to meet drinking water standards.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

A Probability-based Fuzzy Multi-objective Optimization for Material Selection

Abstract: In the present paper, a rational fuzzy multi-objective optimization for material selection is developed in respect of probabilistic method for multi-objective optimization, which agrees with the viewpoint of system theory for the whole optimization of a system and is the novelty of this work. The basic ideas and algorithms of fuzzy theory together with probability theory are taken as the cornerstone to perform the formulation. In the treatment, the intersection of the membership function of fuzzy numbers of alternative material performance and the membership function of fuzzy numbers of desired material performance is used as the utility of the material performance index. Thereafter, the utility of each material performance index is further used to conduct the assessment of its partial preferable probability and formulate the multi-objective optimization by means of probability theory. Moreover, a typical example is presented to provide the rational process of the probabilistic fuzzy multi-objective optimization for material selection.

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area.

Constructing sustainable halal tourism composite performance index for the global halal tourism industry

ABSTRACT This study constructs a Sustainable Halal Tourism Composite Performance Index (SHTCPI) to evaluate the progress of the global halal tourism industry from a sustainable tourism perspective. To create the SHTCPI, this study uses four dimensions (environment, social, economic, and halal tourism) and 24 indicators covering the 2010 to 2020 period and 116 countries. The findings suggest that halal tourism is still in the development stage. Interestingly, most of the Muslim-majority countries are far behind the top score. The developed SHTCPI will guide policymakers and destination managers to formulate strategic policies for more sustainable halal tourism development.

Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models and implementing the controllers and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior, such that the process evolves almost identically. The resulting controllers’ execution time have decreased hundreds of times while keeping their optimality; the performance index has even slightly increased.

The role of institutional diversity in sustainable water use: Performance comparison among water user organizations

AbstractThe aim of this study was to compare the performance of water user organizations (WUOs) in the agricultural sector in terms of their managerial efficiency. A survey was carried out across the study area to evaluate 67 WUOs, including irrigation cooperatives, municipalities, village legal entities (VLEs) and water user associations (WUAs). The findings were then used to create a management performance index. It has been determined that municipalities excel in terms of physical performance, irrigation cooperatives in enterprise and social performance and WUAs in institutional and investment performance. The general management performance index revealed that the most successful WUOs in the Konya closed basin were WUAs. Therefore, proposals have been put forward to begin institutionalization processes in other institutions to attain the successful institutionalization in WUAs. Additionally, the shift towards prepaid systems is recommended to mitigate collection problems, while water should be priced according to the full cost method. Finally, supporting the use of alternative energy sources for irrigation is crucial.

A framework for human–robot collaboration enhanced by preference learning and ergonomics

Industry 5.0 aims to prioritize human operators, focusing on their well-being and capabilities, while promoting collaboration between humans and robots to enhance efficiency and productivity. The integration of collaborative robots must ensure the health and well-being of human operators. Indeed, this paper addresses the need for a human-centered framework proposing a preference-based optimization algorithm in a human–robot collaboration (HRC) scenario with an ergonomics assessment to improve working conditions. The HRC application consists of optimizing a collaborative robot end-effector pose during an object-handling task. The following approach (AmPL-RULA) utilizes an Active multi-Preference Learning (AmPL) algorithm, a preference-based optimization method, where the user is requested to iteratively provide qualitative feedback by expressing pairwise preferences between a couple of candidates. To address physical well-being, an ergonomic performance index, Rapid Upper Limb Assessment (RULA), is combined with the user’s pairwise preferences, so that the optimal setting can be computed. Experimental tests have been conducted to validate the method, involving collaborative assembly during the object handling performed by the robot. Results illustrate that the proposed method can improve the physical workload of the operator while easing the collaborative task.