Problem Of Performance Assessment Research Articles

Research on resilience assessment method of train control on-board system

To address the problem of difficult performance assessment of train control on-board system after recovery from failures, we have proposed a resilience assessment methodology that uses reliability as an indicator of system resilience. Since the system failures are time-dependent, we adopted the Discrete Time Bayesian Network method to obtain the system's reliability before and after failure. Subsequently, we used an exponential recovery model to quantify the system's performance curve during the recovery phase, and finally utilized the resilient triangle area method to quantify its resilience size. Analyzing the CTCS3-300T train control on-board system, we found that the resilience of the system with cold standby redundancy design and hot standby redundancy design were 89.44 % and 87.34 %, respectively, indicating a slight decrease in system performance after recovery from failures compared to pre-failure levels. At that time, it was necessary to adjust operational plans based on actual conditions to avoid greater impact on the railway network. This paper realizes performance resilience of train control on-board system after failure recovery, which can be applied to similar systems and provide theoretical references for realizing intelligent maintenance of the high-speed train.

Step-wise segment partition based stationary subspace analysis and Gaussian mixture model for nonstationary process performance assessment

This article focuses on solving the problem of performance assessment for nonstationary processes without significant cointegrated correlations, which is a basic assumption of traditional methods. The time axis is replaced by indicator axis of working condition. Thus, the irregular non-stationarity along time axis could turns to be typical piecewise stationarity along indicator axis of working condition. The proposed method provides a general tool to deal with non-stationarities and relax the cointegrated restriction on variable correlations. Since a nonstationary process has specific working patterns under different conditions, the process can thus be divided into multiple segments along the axis of working condition. To characterize the properties of different performances within a segment meticulously, random sampling-based stationary subspace analysis (SSA) is conducted to separate the Gaussian and non-Gaussian subspaces. Since the operating performance is mainly distinguied by the non-Gaussian information, the non-Gaussian subspace obtained by SSA is further characterized by Gaussian mixture model (GMM). For online application, the segment model is selected by the indicator of working condition, and then the performance is assessed using Bayesian inference to provide a probabilistic result. The efficacy of the proposed method is validated by a coal mill from a real thermal power plant.

Multispectral and hyperspectral image fusion in remote sensing: A survey

The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the spotlight. The combination of high spatial resolution MS images with HS data showing a lower spatial resolution but a more accurate spectral resolution is the aim of these techniques. This survey presents a deep review of the literature designed for students and professionals who want to know more about the topic. The basis aspects of the MS and HS image fusion are presented and the related approaches are classified into three different classes (pansharpening-based, decomposition-based, and machine learning-based). The ending part of this survey is devoted to the description of widely used datasets for this task and the performance assessment problem, even describing open issues and drawing guidelines for future research.

A Novel Gibbs Entropy Model Based upon Cross-Efficiency Measurement for Ranking Decision Making Units

Cross-efficiency measurement in data envelopment analysis (DEA) was developed to overcome the main disadvantage of DEA in discriminating decision making units (DMUs). However, the results obtained from each cross-efficiency model (Benevolent and aggressive models) may not generally be the same for similar problems, and each model may provide different viewpoints that we should take each model into account at the same time. Since Gibbs entropy is one of powerful tools to measure uncertainty, in this paper a novel linear programming model based on the concepts of Gibbs entropy (GE model) has been offered to combine cross-efficiency scores, which are obtained from the viewpoints of benevolent and aggressive models, for ranking DMUs. In order to validate the proposed GE model, it is tested with two examples, including the performance assessment problem and the relative efficiency of seven Thai provinces. The main advantages of the GE model are that it can be used to tackle large size problems with uncertainty, and it can be used to combine other models for ranking DMUs. In addition, the set of multiple solutions of optimal weights for each model can be ignored. By using the proposed model, decision-makers can achieve more reliable decision than individual models.

Linear and nonlinear solvers for simulating multiphase flow within large-scale engineered subsurface systems

Multiphase flow simulation is well-known to be computationally demanding, and modeling large-scale engineered subsurface systems entails significant additional numerical challenges. These challenges arise from: (a) the presence of small-scale discrete features like shafts, tunnels, waste packages, and barriers; (b) the need to accurately represent both the waste form processes at the small spatial scale of the repository and the large-scale transport processes throughout heterogeneous geological formations; (c) the strong contrast in material properties such as porosity and permeability, as well as the nonlinear constitutive relations for multiphase flow. Numerical solution entails discretization of the coupled system of nonlinear governing equations and solving a linear system of equations at each Newton–Raphson iteration. Practical problems require a very large number of unknowns that must be solved efficiently using iterative methods in parallel on high-performance computers. The unique challenges noted above can lead to an ill-conditioned Jacobian matrix and non-convergence with Newton’s method due to discontinuous nonlinearity in constitutive models. Moreover, practical applications can require numerous Monte-Carlo simulations to explore uncertainly in material properties, geological heterogeneity, failure scenarios, or other factors; governmental regulatory agencies can mandate these as part of Performance Assessments. Hence there is a need for flexible, robust, and computationally efficient methods for multiphase flow in large-scale engineered subsurface systems.We apply the open-source simulator PFLOTRAN to the practical problem of performance assessment of the US DOE Waste Isolation Pilot Plant (WIPP) site. The simulator employs a finite volume discretization and uses the PETSc parallel framework. We evaluate the performance of several preconditioners for the iterative solution of the linearized Jacobian system; these range from stabilized-biconjugate-gradient with block-Jacobi preconditioning (BCGS) to methods adopted from reservoir modeling, such as the constrained pressure residual (CPR) two-stage preconditioner and flexible generalized residual solver (FGMRES). We also implement within PETSc the general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), which truncates the Newton update or modifies the update with a Cauchy solution that is within the quadratic model trust-region of the objective function. We demonstrate the effectiveness of each method for a series of test problems with increasing difficulty. We find that the NTRDC and FGMRES-CPR-ABF (FCA) preconditioners generally perform best for the test problem having the extreme nonlinear processes, achieving a 50x speed-up compared with BCGS. The most ill-conditioned and extreme nonlinear simulations do not converge with BCGS (as one may expect), but they do complete the simulation with NTRDC and FCA. We also investigate the strong scalability of each method and demonstrate the impact of node-packing upon parallel performance on modern processor architectures.

Ranking DMUs using a novel combination method for integrating the results of relative closeness benevolent and relative closeness aggressive models

In this paper, a novel combination method is offered to integrate the results of two new relative closeness models, called relative closeness benevolent (RCB) and relative closeness aggressive (RCA) models, for ranking all DMUs. To prove the applicability of the proposed method, it is examined in three numerical examples, performance assessment problem, six nursing homes and fourteen international passenger airlines. Firstly, RCB and RCA models were formulated in order to generate the cross-efficiency intervals matrix (CEIM). After obtaining CEIM, the RC index was utilized to generate a combined cross-efficiency matrix (combined CEM). In combined CEM, target DMUs were viewed as criteria and DMUs were viewed as alternatives. After that, the weights of each criterion were generated using a new weighting method based on standard deviation technique (MSDT). Finally, all DMUs were evaluated and ranked. Comparison with existing cross-efficiency models indicates the more reliable results through the use of the proposed method.

Linearity Decomposition-Based Cointegration Analysis for Nonlinear and Nonstationary Process Performance Assessment

The assessment of operating performance plays an important role in guaranteeing high efficiency, low cost, large economic benefit, etc., for modern industry under routine operating conditions. However, the nonlinear and nonstationary properties that widely exist in real industrial processes, make it a great challenge to obtain accurate assessment of operating performance. To solve the problem of performance assessment for nonstationary processes with nonlinearly cointegrated relationships, the linearity decomposition based cointegration analysis (CA) is proposed through multi-objective optimization in the present work. First, the nonstationary critical-to-performance variables are selected to remove the redundant information and improve the accuracy for assessment. Second, the selected nonstationary critical-to-performance variables are then decomposed into a set of local sub-blocks. Each sub-block contains a group of linearly cointegrated variables, based on which a two-layer multiblock assessing model can be established for nonlinear and nonstationary process using CA. Bayesian inference based criterion is adopted to indicate the operating performance for online assessment. Finally, the feasibility and efficacy are illustrated via a numerical example and a pulverizing process of a real thermal power plant.

Operator Performance Prediction based on Fuzzy Modeling Approach

In this paper, physiological signals were measured from five participants, each participating in two sessions of experiment with identical experimental procedure. A simulation platform, AutoCAMS (Automation-enhanced Cabin Air Management System), was used to simulate a complex task environment of human-machine shared process control. Fuzzy models were constructed to quantitatively predict the human operator performance based on three EEG input features. The incremental-PID-controlled particle swarm optimization (IPID-PSO) algorithm was utilized to optimize the parameters of fuzzy models. The IPID-PSO algorithm incorporated incremental-PID-controlled search strategy to speed up the convergence of standard PSO algorithm. The operator performance modeling results are given to show the effectiveness of the IPID-PSO-tuned fuzzy modeling approach proposed to momentary operator performance assessment problem under consideration.

Decision support system with TOPSIS method for lecturer appraisal in Universitas PGRI Madiun

Assessment of lecturer at university is a form of activity to evaluate the performance of each lecturer. PGRI University of Madiun (UNIPMA) always strives to improve accreditation by improving quality with other universities. Making a decision support system is one way to assist in determining lecturer’s assessment in UNIPMA. This decision support system uses the TOPSIS method and will be developed using the Waterfall model. In this study to solve the problem of educator performance assessment that is UNIPMA lecturer. The assessment process includes qualifications of education, learning, research, and the number of community service activities that have been done. By using the TOPSIS method of counting process and giving the result of lecturer appraisal that has been roughed more efficient and precise. Based on the results of the research using lecturer data sampling of 10 educators obtained the highest value data is 0.75 (Very Good) where the range of decision assessment 1 - 0.65 (Very Good), 0.64 - 0.4 (Good), 0.39 - 0.25 (Enough) and 0.24 - 0 (Bad).

Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree

Multirotor has been applied to many military and civilian mission scenarios. From the perspective of reliability, it is difficult to ensure that multirotors do not generate hardware and software failures or performance anomalies during the flight process. These failures and anomalies may result in mission interruptions, crashes, and even threats to the lives and property of human beings. Thus, the study of flight reliability problems of multirotors is conductive to the development of the drone industry and has theoretical significance and engineering value. This paper proposes a reliable flight performance assessment method of multirotors based on an Interacting Multiple Model Particle Filter (IMMPF) algorithm and health degree as the performance indicator. First, the multirotor is modeled by the Stochastic Hybrid System (SHS) model, and the problem of reliable flight performance assessment is formulated. In order to solve the problem, the IMMPF algorithm is presented to estimate the real-time probability distribution of hybrid state of the established SHS-based multirotor model, since it can decrease estimation errors compared with the standard interacting multiple model algorithm based on extended Kalman filter. Then, the reliable flight performance is assessed with health degree based on the estimation result. Finally, a case study of a multirotor suffering from sensor anomalies is presented to validate the effectiveness of the proposed method.

Effectiveness Evaluation Model of Moving Target Defense Based on System Attack Surface

Evaluation of moving target defense (MTD) effectiveness has become one of the fundamental problems in current studies. In this paper, an evaluation model of MTD effectiveness based on system attack surface (SAS) is proposed to extend this model covering enterprise-class topology and multi-layered moving target (MT) techniques. The model is focused on the problem of incorrect performance assessment caused by inaccurately characterizing the process of attacking and defending. Existing evaluation models often fail to describe MTD dynamically in a process. To deal with this static view, offensive and defensive process based on a player's move is presented. Besides, it converts all the attack and defense actions into the process, and interactivities are evaluated by system view extended attack surface model. Previously, the proposed attack surface models are not concerned about the links between nodes and vulnerabilities affected by topologies. After comprehensively analyzing the impact of interactions in the system, a SAS model is proposed to demonstrate how resources of the system are affected by the actions of attackers and defenders, thus ensuring the correctness of parameters for SAS in measuring MT technology. Moreover, by generating a sequence of those shifting parameters, a nonhomogeneous hierarchical hidden Markov model is used to find the possible sequence of attacking states by introducing the partial Viterbi algorithm. Also, a sequence of attacking states is defined to illustrate how adversaries are handled by MT technologies and how much additional consumption costs are increased by the system resource reconfiguration. Finally, the simulation of the proposed approach is given in a case study to demonstrate the feasibility and validity of the proposed effectiveness evaluation model in a systematic and dynamic view.

Modern Approaches to Teacher Performance Assessment. An Overview of Foreign Publications

The need to develop a national teacher growth system requires that the existing approaches to assessing the quality of teaching be analyzed. This article provides an overview of foreign publications devoted to the problem of teacher performance assessment. Two major approaches are described: assessing teacher performance through student achievement and formative assessment based on teacher observation and follow-up feedback. Foreign researchers believe that there is no reason to expect that student achievement will be in complete alignment with the size and quality of teacher effort, as too many factors beyond the teacher’s control are in play. Some researchers suggest ways to increase the validity of using student attainment data in assessing teacher performance. Formative assessment of students is being gradually introduced into instruction processes, but formative assessment of teacher performance is only beginning to emerge in school practices. This article explores the methods and techniques of formative teacher assessment and presents the first findings on their opportunities and limitations. Most authors of the publications discussed here agree that the system of teacher performance assessment should be organized to foster the professional and personal development of teachers.

A GOAL-ORIENTED APPROACH TO THE PERFORMANCE ASSESSMENT OF RESEARCH TEAMS

The current growth of scientific knowledge has intensified the problem of research performance assessment. However, there is still a lack of the unified assessment methodology, largely due to the changing nature of modern science. At present, knowledge is increasingly generated by collective actors - research teams, rather than by individual scientists. These collective actors constitute a complex self-developing and evolutionary system, making its evaluation rather problematic. By using a goal-oriented approach, this paper seeks to identify specific characteristics of various research team types and suggests a qualitative criterion against which their research performance can be measured. It is shown that a research team forms and evolves around a goal that is shared by all its members; this common goal defines both structural-managerial characteristics of such a collective actor and qualitative attributes of attained research objectives. The obtained results - research team models and the qualitative criterion of their performance - can be used as a platform for further research into the cross-cultural, ethical, and economic dimensions of research teams, as well as for assessing research performance of actual teams.

Energy performance of dynamic thermal insulation built in the experimental façade system

Purpose The purpose of this paper is to investigate energy performance of thermal insulation modified by phase change materials (PCM). Special attention was paid to the problem of proper performance assessment of such components by computational techniques and methods of its evaluation. Design/methodology/approach Analysis was conducted on the basis of the results obtained using the dynamic building simulation technique performed by ESP-r software. Two cases of insulation components enhanced by a layer: characterised by increased latent heat capacity were analysed and compared. Results were investigated in terms of thermal comfort and energy efficiency, using evaluation methods from literature and new, original indicators proposed by authors. Findings The analysis revealed that performance of insulation enhanced by PCM is very dynamic and highly sensitive to changeable weather conditions. Thus, there is a strong need for the development of the assessment methods and guidelines for the performance of such components with changeable physical properties. Practical implications The methodology and the results reported in this paper could be used as a guideline for further parametric studies and optimisation tasks. Further development of phase change insulation can substantially change the existing approach to the building energy performance. Originality/value The paper introduces a new approach of the assessment of insulation components modified by PCM and highlights the dynamic characteristics of its performance.

A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks.

To solve the problem of coverage performance assessment, this study proposes an evaluation method based on the trajectory of the target, which is applicable to universal sensor networks, including both heterogeneous and homogeneous sensor networks. Different from the traditional Voronoi algorithm, the proposed Improved Coverage Force Division (ICFD) plans a coverage force division map whichscales the qualitative coverage performancebasedon both covering intensities andlocations of the nodes. Furthermore, the Trajectory-based Evaluating Schedule (TES) is responsible for solving the quantitative coverage evaluationproblem by measuringthe resulting trajectories’ Balance Values (BVs). A model of weak-point ranking conjoined in consideration of coverage force and distance can guide future deployment to compensate coverage. Comparative trials using the greedy algorithm, Voronoi algorithm, and the proposed TES verify that TES achieves the approximate results for two-stage and multistage heterogeneous sensor networks with acceptable difference and lower complexity, and it is superior to the Voronoi algorithm in homogeneous sensor networks interms of breaking the four-point circle block.

A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes

Decisions on attribute weights are important problems in multiple attribute decision making. Many methods have been proposed to create attribute weights which are used to aggregate attributes in a simple additive weighting way. In this paper, a method of deriving attribute weights from incompatibility among attributes and possible constraints on the weights is developed based on the evidential reasoning approach in which attribute aggregation is nonlinear rather than linear. The incompatibility is a flexible combination of deviation incompatibility and decision incompatibility with a relaxation coefficient. The deviation incompatibility measures differences between assessments of alternatives on each attribute and the decision incompatibility quantifies differences between assessments of alternatives on one attribute and the aggregated assessments of the alternatives. For a specific alternative, two pairs of optimization problems with a constraint on the difference between potential weights and the combination of deviation incompatibility and decision incompatibility are designed to generate the favorable intervals of attribute weights and those of utilities of assessment grades. A problem of car performance assessment is investigated to demonstrate the applicability of the proposed method. The method is validated by comparison with other methods of producing attribute weights using the problem.

Multivariable Pid Control Via Ilmis: Performances Assessment

Abstract This paper addresses the problem of performance assessment for MIMO PID controllers. An efficient comparative analysis is established using two Iterative Linear Matrix Inequality (ILMI) approaches. To evaluate the robustness and the performances of each ILMI approach, four tests are carried out on the closed-loop system including White Gaussian noise, set point changes, parametric perturbation, and multiple time-delays influence. Different performance criterion such as IAE, ISE, ITAE and ITSE are also presented to evaluate the closed-loop system performances. The quadruple-tank process as a benchmark of minimum/non-minimum phase systems is applied to show the effectiveness and utility of the proposed analysis. The comparative analysis between the two PID controllers leads to nearly similar performance results in parametric perturbation and noise attenuation but different results in terms of set point changes, multiple time-delays influence and performance indices

Robust evidential reasoning approach with unknown attribute weights

In multiple attribute decision making (MADM), different attribute weights may generate different solutions, which means that attribute weights significantly influence solutions. When there is a lack of sufficient data, knowledge, and experience for a decision maker to generate attribute weights, the decision maker may expect to find the most satisfactory solution based on unknown attribute weights called a robust solution in this study. To generate such a solution, this paper proposes a robust evidential reasoning (ER) approach to compare alternatives by measuring their robustness with respect to attribute weights in the ER context. Alternatives that can become the best with the support of one or more sets of attribute weights are firstly identified. The measurement of robustness of each identified alternative from two perspectives, i.e., the optimal situation of the alternative and the insensitivity of the alternative to a variation in attribute weights is then presented. The procedure of the proposed approach is described based on the combination of such identification of alternatives and the measurement of their robustness. A problem of car performance assessment is investigated to show that the proposed approach can effectively produce a robust solution to a MADM problem with unknown attribute weights.

Conjunctive combination of belief functions from dependent sources using positive and negative weight functions

This paper investigates the conjunctive combination of belief functions from dependent sources based on the cautious conjunctive rule (CCR). Weight functions in the canonical decomposition of a belief function are divided into two parts, namely, positive and negative weight functions, whose characteristics are described. Positive and negative weight functions of two belief functions are used to construct a new partial ordering between the belief functions. The partial ordering determines the committed relationship between two belief functions, which is different from that generated by the weight function based partial ordering in the CCR when one or two belief functions are not unnormalized separable. A new rule is developed using the constructed partial ordering to combine belief functions from dependent sources. The relevant properties are described and demonstrated by examples. A performance assessment problem is investigated to demonstrate the validity and applicability of the proposed rule and compare it with the CCR.

An approach to performance assessment and fault diagnosis for rotating machinery equipment

Abstract Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault diagnosis for rotating machinery, including wavelet packet decomposition for extracting energy feature samples from vibration signals acquired during normal and faulty conditions; clustering analysis for demonstrating the separability of the samples; and Fisher discriminant analysis for providing an optimal lower-dimensional representation, in terms of maximizing the separability among different populations, by projecting the samples into a new space. In the new low-dimensional space, the Mahalanobis distance (MD) between the new measurement data and normal population can be calculated for performance assessment. Moreover, this model for performance assessment only requires data to be available in normal conditions and any one of all possible fault conditions, without the necessity for the full life cycle of condition monitoring data. In addition, if monitoring data under different fault conditions are available, the fault mode can be identified accurately by comparing the MDs between the new measurement data and each fault population. Finally, the proposed method was verified to be successful on performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test.