Framework For Performance Analysis Research Articles

Using Importance–Performance Analysis to Reveal Priorities for Multifunctional Landscape Optimization in Urban Parks

In the context of urban renewal, residents have presented elevated expectations for the quality of urban parks, necessitating the optimization of parks’ multifunctional landscapes. Transforming residents’ preferences for landscape services into a prioritized index for multifunctional landscape renewal poses a significant challenge. This study addresses this research gap by integrating importance–performance analysis (IPA) with residents’ perception evaluations of landscape services. We establish an index system to evaluate perceptions of urban park landscape services. By employing the importance–performance analysis framework, we identify landscape service types that exhibit high importance but low satisfaction levels, thereby establishing priorities for multifunctional landscape renewal. Using Guangzhou’s urban parks as a case study, our findings reveal variations in users’ demands for different landscape services and differences in demand among various user groups for similar services. Users assign utmost importance to safety services while expressing the highest satisfaction with physical and mental health or microclimate regulation services. Significant disparities exist between middle-aged/elderly groups and young people regarding perceptions of social interaction, waste disposal, and sense of belonging services. Our results demonstrate that IPA analysis can elucidate priorities for multifunctional landscape renewal, facilitate public participation in improving urban park landscapes, and provide decision-making support for optimizing these landscapes.

A smart parametrisation for robust adaptive PI controller applied on renewable energy power generation systems under weak and uttermost weak grid conditions

The increasing penetration of renewable energy systems into the global energy matrix is a necessity for sustainable development. The benefits impact the well-being of humanity and the environment directly. However, these power generation systems demand reliable controllers to regulate the current injection into the electrical grid. Therefore, this paper presents a smart parametrisation for a robust adaptive proportional integral controller applied to grid-injected current control of a voltage inverter source with an LCL filter. Controller parameter optimisation and the initialisation of their gains are performed using the particle swarm optimisation algorithm. Three cost functions are assessed on controller parametrisation, considering the current tracking error minimisation and control action limitations: integral of absolute error (IAE) + integral of absolute control action (IAU), integral of square error (ISE) + IAU, and integral of the time-weighted absolute error (ITAE) + IAU. A framework for performance analysis and an in-depth discussion are presented, providing a systematic decision-support methodology for controller configuration. The function determined as the best one is implemented experimentally and compared with a designer’s experience-based robust adaptive proportional integral controller using a hardware-in-the-loop test bench, where the improvements in controller performance are corroborated, contributing to the enhancement of energy quality in renewable energy systems. In addition, the controller’s robustness is evaluated on a weak grid and an uttermost weak grid, where it presents high robustness and global stability.

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis

The article discusses the need for a lightweight software architecture evaluation framework that can address practitioners' concerns. Specifically, the proposed framework uses process mining and Petri nets to analyze security and performance in software development's early and late stages. Moreover, the framework has been implemented in six case studies, and the results show that it is a feasible and effective solution that can detect security and performance issues in complex and heterogeneous architecture with less time and effort. Furthermore, the article provides a detailed explanation of the framework's features, factors, and evaluation criteria. Additionally, this article discusses the challenges associated with traditional software architecture documentation methods using Unified Modeling Language diagrams and the limitations of code alone for creating comprehensive Software Architecture models. Various methods have been developed to extract implicit Software Architecture from code artifacts, but they tend to produce code-oriented diagrams instead of Software Architecture diagrams. Therefore, to bridge the model-code gap, the article proposes a framework that considers existing Software Architecture in the source code as architectural components and focuses on Software Architecture behaviors for analyzing performance and security. The proposed framework also suggests comparing Software Architecture extracted by different Process Mining algorithms to achieve consensus on architecture descriptions, using visualizations to understand differences and similarities. Finally, the article suggests that analyzing the previous version of a system's Software Architecture can lead to improvements and deviations from planned Software Architecture can be detected using traceability approaches to aid software architects in detecting inconsistencies.

Noise performance analysis of PT-symmetric non-reciprocal transmission systems

Non-reciprocity is a crucial concept in the field of signal transmission and processing as it protects sensitive elements from the effects of feedback signal. The non-reciprocal components based on ferrite materials are bound to show shortcomings due to incompatibility with chip-level ICs. In recent years, parity-time (PT)-symmetric non-reciprocal transmission in electronic systems has been proposed and analyzed. Here, we propose the simplified noise analysis model based on a PT-symmetric non-reciprocal transmission system. And we verify the theoretical noise model by simulation analysis and experimental demonstrations. The experimental result shows that the noise figure of the proposed PT-symmetric non-reciprocal transmission device is 7.5 dB because of the employment of the active amplifier. This work establishes a noise performance analysis framework, providing guidance for the development of lossless and miniaturized applications of non-reciprocal components.

Integrated [formula omitted] anti-disturbance synchronization control for switched neural networks with unknown delays

This research is concerned with the integrated L∞ anti-disturbance synchronization control problem for continuous-time switched neural networks (SNNs). Different from the relevant synchronization results about SNNs, multiple disturbances and unknown delays are simultaneously addressed in a uniform framework. By constructing the novel Lyapunov function and the ameliorative combined switching strategy, an L∞ performance analysis framework with less conservativeness is then established, where each individual subnetwork is unnecessary to possess a prescribed L∞ performance index. By designing an appropriate disturbance observer and transforming the delay-dependent neuron activation function into a bounded disturbance, an integrated L∞ anti-disturbance synchronization control paradigm is also presented on the basis of the proposed analysis framework. Finally, three illustrative examples are employed to substantiate the applicability and superiority of the developed theoretical results.

Environmental Performance of Carbon Emissions Trading Policy: Evidence From a New Comprehensive Analysis Model

Carbon emissions trading (CET) policy is a major institution innovation in promoting green and low-carbon development, but it is still in the initial stage of implementation, and there are many problems to be solved in the research of CET. Therefore, in this article, we aim to design an environmental performance analysis framework integrating various models for CET and conduct an empirical study on the panel data of 30 provinces in China during 2002–2017. The results indicate that, compared with other regions, there is no technology gap of economic–environmental efficiency (EEE-TG) in east China, and Guangdong has the best technology level during the sample period; the CET significantly improves the economic–environmental efficiency under a metafrontier (EEE-M), but it has no significant effect on the EEE-TG; and the external cost of environmental regulation has a mediating effect on the CET and EEE-M, whereas the internal cost of environmental regulation has a masking effect.

A deep learning framework for students' academic performance analysis

A deep learning framework for students' academic performance analysis

A novel performance analysis framework for adaptive cycle engine variable geometry components based on topological sorting with rules

The adaptive cycle engine (ACE) can fully utilize the engine's operating performance. Compared to conventional aero-engine, ACE has more types and quantities of variable geometry component (VGC) combinations. However, the diversity of mission profiles and cycle mode makes the configuration selection of ACE's components extremely complex. It urgently needs to develop a scalable method for analyzing the performance of any VGC combinations of ACE in uncertain configurations. Therefore, this paper establishes a novel performance analysis framework (VAIHT) in the form of fully VGC combination, taking into account the multi task profile of ACE. It improves the traditional topological sorting method of graph theory based on adaptive rules to optimize the state points of the task profile according to the performance and reliability of multiple objectives when the configuration is unknown, avoiding repeated modeling and analysis work. According to the comprehensive weight allocation result of improved topological sorting with rules, the average fuel consumption rate of the ACE dropped by around 3.71%, and the thrust in each task profile rose by at least 13%. Through the method of digital simulation optimization, the paper provides a novel framework for configuration analysis and VGC combination design of more complex engines in the future.

An optimal initialisation for robust model reference adaptive PI controller for grid-tied power systems under unbalanced grid conditions

This paper presents an Optimal Robust Model Reference Adaptive Proportional–Integral controller for grid-tied power systems, which is automatically parametrised using a Genetic Algorithm. The task of choosing the adaptation rate and the controller’s initial gains set is now performed in a computational and automatic way, saving design time and ensuring satisfactory transient regimes.The use of a Genetic Algorithm for complete parametrisation of robust adaptive controllers applied to renewable energy power generation systems with LCL filter is first here explored. Five cost functions are considered in the optimisation process. A comparison of simulation results applying the optimised controller in a grid-tied Voltage Source Inverter with LCL filter points out the benefits and drawbacks of using each cost function. A framework for performance analysis and an in-depth discussion are provided. The function determined as the best one, which is integral of absolute error, is used in the controller implemented experimentally, ensuring suitable tracking of grid current references and rejection of grid voltage harmonics, even under unbalanced grid voltage conditions. An experimental comparison in a three-phase 6.75 kW power converter with the non-optimised controller is also provided, where the optimised controller obtains a reduction in the overshoot in all transient regimes and smaller tracking error, resulting in a reduction in the mean error of 14.31% and 60.30% in α and β coordinates, respectively. In addition, the optimised controller reduces total harmonic content, improving energy quality. This parametrisation procedure can be immediately extended to the control of any other power converter systems.

Approach to the Impact of Digital Technologies on Sustainability Reporting through Structural Equation Modeling and Artificial Neural Networks

The motivation for study derives from the requirements imposed by the European Union Corporate Sustainability Reporting Directive, which increases the sustainability reporting scope and the need for companies to use emerging digital technologies. The research aim is to evaluate the digital transformation impact of the European Union companies on sustainability reporting expressed through three sustainable performance indicators (economic, social, and ecological) based on a conceptual model. The data were collected from Eurostat for 2011–2021. The study proposes a framework for sustainable performance analysis through linear regression models and structural equations. Additionally, a hierarchy of digitization indicators is created by modeling structural equations, depending on their impact on sustainability performance indicators, which is validated using neural networks. The results indicate that the company’s digital transformation indicators positively influence economic and social performance and lead to an improved environmental protection (a decrease in pollution), proving the established hypotheses’ validity. The proposed model can be the basis for companies to create their dashboards for analyzing and monitoring sustainable performance. This research can be the basis of other studies, having a significant role in establishing economic and environmental strategies to stimulate an increase of companies that carry out sustainability reporting.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential, designing a field development plan, and making investment decisions. However, quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors. In fact, each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models. Inspired by the idea, we propose a hybrid data-driven analysis framework in this study, where the contributions of dominant factors are quantitatively evaluated, the productions are precisely forecasted, and the development optimization suggestions are comprehensively generated. More specifically, game theory and machine learning models are coupled to determine the dominating geological and engineering factors. The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors. A multi-model-fused stacked model is trained for production forecast, which provides the basis for derivative-free optimization algorithms to optimize the development plan. The complete workflow is validated with actual production data collected from the Fuling shale gas field, Sichuan Basin, China. The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization. Comparing with traditional and experience-based approaches, the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

Sustainability in Global Business: Analytical Framework for Selected Value Chains in Sub-Saharan Africa

During the last two decades, the dimensions of business sustainability have expanded from an environmental focus to a ‘triple bottom line (TBL)’ i.e. Economic, Social and Environmental. The analysis shows that the real challenge for businesses lies in balancing these three TBL dimensions. The TBL sustainability equation of any business is relative to the context in which it operates. In the case of global business value chains, it is observed that the TBL performance varies depending on the particular stage of the value-chain i.e. first sourcing to manufacturing, distribution, consumption and reverse logistics. There is no absolute formula for defining the TBL performance. Therefore, the issue requires contextual analysis of the different value chain levels in relation to the contextual considerations. The current paper focuses on analyzing three different value chains in Sub-Saharan Africa: Lithium Batteries; Coffee; and Pharmaceuticals. Based on the literature review, we first identify the important parameters for analyzing these three value chains. Two of the selected value chains focus on the sustainability issues in the inbound part of value chains (sourcing) while the third one focuses on the distribution (outbound) value chain. The paper explores how the considerations and approach of analysis shift depending on the contextual requirements. The paper offers insights for further developing the sustainability performance analysis framework in global value chains. instructions give you guidelines for preparing papers for this journal.

Modeling and optimization of multi-altitude leo satellite networks using cox point processes

This research focuses on the modeling and optimization of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes to achieve efficient coverage and performance analysis. LEO satellite networks have gained attention for their potential to provide global connectivity with reduced latency and increased network capacity. Accurately modeling the spatial distribution of satellites at different altitudes and optimizing their deployment pose significant challenges. This research proposes a mathematical framework based on Cox point processes to capture the randomness and irregularity of satellite deployments. Optimization algorithms, such as genetic algorithms, are employed to determine the optimal satellite locations, altitude allocation, and network parameters. Performance analysis considers metrics such as coverage probability, signal strength, interference levels, capacity, and quality of service. The research contributes to the development of advanced modeling techniques, optimization algorithms, and performance analysis frameworks, enabling efficient coverage and performance optimization in multi-altitude LEO satellite networks. The numerical examples and discussions illustrate the effectiveness and potential of the proposed approach in enhancing the design and operation of satellite communication systems

Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

Signal processing based research was adopted with Electroencephalogram (EEG) for predicting the abnormality and cerebral activities. The proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or not. Early detection and intervention are vital for better prognosis. However, the diagnosis of schizophrenia still depends on clinical observation to date. Without reliable biomarkers, schizophrenia is difficult to detect in its early phase and hence we have proposed this idea. In this work, the EEG signal series are divided into non-linear feature mining, classification and validation, and t-test integrated feature selection process. For this work, 19-channel EEG signals are utilized from schizophrenia class and normal pattern. Here, the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s sequences. With this process, 1142 features of normal and schizophrenia class patterns can be obtained. In other hand, 157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential features. At last, the Deep Learning (DL) technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network (DCNN) with mayfly optimization algorithm. The proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy, Signal to Noise Ratio (SNR), Mean Square Error, Normalized Mean Square Error (NMSE) and Loss. Through comparison, the proposed technique is proved to a better technique than other existing techniques.

Work density analysis and thermoeconomic optimisation of modified Carnot cycle engine

The modified Carnot cycle, a novel thermodynamic cycle, is introduced, and the cycle's performance is analysed in order to more accurately depict the performance and design parameters of actual heat engines. Work output, thermal efficiency, and work density are all considered in conjunction during the performance analysis. In terms of maximum pressure, maximum volume, work density, thermal efficiency, and size, the results demonstrate that the modified cycle performs better than the classic Carnot cycle at simulating the real heat engine. In the performance analysis framework, a thermoeconomic optimisation has been carried out based on an objective function defined as the total cost per unit work output, which depends on an economic parameter, work density, and thermal efficiency. The findings might be used to select the modified cycle's ideal design and operation parameters, which can be applied to actual engines.

Streamlined semi-automatic data processing framework for ship performance analysis

The hydrodynamic performance of a sea-going ship can be analyzed using data from different sources, like onboard recorded in-service data, AIS data, and noon reports. Each of these sources is known to have its inherent problems. The current work highlights the most prominent issues, explained with examples from actual datasets. A streamlined semi-automatic approach to processing the data is finally outlined, which can be used to prepare a dataset for ship performance analysis. Typical data processing steps like interpolating metocean data, deriving additional features, estimating resistance components, data cleaning, and outlier detection are arranged in the best possible manner not only to streamline the data processing but also to obtain reliable results. A semi-automatic implementation of the data processing framework, with limited user intervention, is used to process the datasets here and present the example plots for various data processing steps, proving the effectiveness of the proposed approach.

Novel Mathematical Framework for Performance Analysis of Energy Harvesting-Based Point-to-Point Communications

Novel Mathematical Framework for Performance Analysis of Energy Harvesting-Based Point-to-Point Communications

Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game.

Every soccer game influences each player's performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player's energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players' responses to various game situations.

A comprehensive review of control techniques for compensating the fault current in resonant grounded distribution networks: From the perspective of mitigating powerline bushfires

Powerline faults are responsible for major bushfires around the world where arcs provoked from ground faults are common causes for igniting such fires. The grounding techniques (mainly resonant grounding) used in distribution substations and arc suppression devices play a crucial role for compensating fault currents in order to extinguish arcs so that the likelihoods of powerline bushfires are significantly reduced. Though passive arc suppression devices (e.g. Petersen coils) are extensively used for compensating the reactive component of the fault current, the active component of this current is still large enough to ignite the fire in bushfire prone areas for which active arc suppression devices are recently used. These active arc suppression devices incorporate residual current compensation inverters and the full compensation of the fault current rely on the control scheme of these inverters. This paper comprehensively reviews different control schemes that are used for compensating the fault current in resonant grounded power distribution systems. The existing control schemes are discussed in terms of the model used during the controller design process, loop structures, control block diagrams, and performance analysis frameworks (i.e. the type of fault impedances). It is worth mentioning that faults on resonant grounded power distribution networks exhibit the characteristics of high impedance faults and it important to consider this aspect for performance analysis of the control scheme. This paper also covers a brief overview of different grounding techniques used for mitigating fault currents and finally, the challenges with the existing controllers are identified in terms of extinguishing powerline bushfires. The comprehensive review motivates and guides future research activities on developing more efficient fault compensation techniques.

Performance analysis of DPDK-based applications through tracing

The Data Plane Development Kit (DPDK) is an efficient framework developed based on the kernel bypass approach to accelerate packet processing in userspace. This framework includes many libraries and Poll Mode Drivers (PMDs) that are optimized for maximum network performance. Several studies have demonstrated that porting existing data plane applications to DPDK can considerably increase their performance. Unfortunately, kernel bypassing makes traditional network monitoring and debugging tools obsolete. Therefore, considering the complexity of networking software, detecting unexpected behaviors, and diagnosing its root causes became a tedious task. The literature reports a few tools intended for debugging DPDK application bugs, but they are mostly ineffective against performance bugs.In this paper, we propose a tracing-based performance analysis framework that is dedicated to DPDK applications. The framework provides many analyses enabling the practitioner to gain insights into the application internals and diagnose performance bugs. We use an efficient approach to collect tracing data from DPDK libraries and drivers, with a very low overhead (<0.2%). Our framework correlates the data collected to build a data model that illustrates the states of the monitored application' components. We leverage this data model to calculate adapted performance metrics and display them in time-synchronized graphical views. The numerous use cases that we present demonstrate the ability of our tool to identify and diagnose efficiently various performance bugs.