Values Of Performance Measures Research Articles

Classification of Rice Varieties Using Artificial Intelligence Methods

Rice being one of the most widely produced and consumed cereal crops in the world, is also the one of the main sustenance in our country because of its economical and nutritious nature. Rice, starting from farm to our table, goes through some manufacturing steps such as a cleaning process, color sorting and classification. If these stages are to be mentioned briefly, cleaning is the process of separating rice from foreign substances, classification is the process of separating broken ones with sturdy ones; color extraction is the process of separating the stained and striped ones except the whiteness on the rice surface. In this study, a computerized vision system was developed in order to distinguish between two proprietary rice species. A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice. With these features, models were created using LR, MLP, SVM, DT, RF, NB and k-NN machine learning techniques and performance measurement values were obtained. Success rates in the classification were obtained 93.02% (LR), 92.86% (MLP), 92.83% (SVM), 92.49% (DT), 92.39% (RF), 91.71% (NB), 88.58% (k-NN). When we look at the results of the success rate of obtain, it is possible to say that the study achieved success. Click here to download the Rice (Cammeo and Osmancik) Data Set

Quantitative target setting in balanced scorecard method using simultaneous equations system and goal programming

Purpose The purpose of this paper is to identify the accurate cause and effect relationships among strategic objectives and also to demonstrate how decision makers can be guided in the process of defining quantitative strategic target values in the framework of balanced scorecard (BSC) and performance measurement system. Design/methodology/approach Based on the proposed method in this research, after determining strategic objectives and developing an initial strategy map according to decision makers’ opinions, simultaneous equations system (SES) was used to determine the significance of the relationships among strategic objectives in higher perspectives of the BSC and corresponding strategic objectives in lower perspectives. Afterward, desirable values for performance measures were determined based on the equations and relationships obtained through SES and were optimized by goal programming method. Findings By applying the proposed method, a clearer picture of the associations among strategic objectives is obtained and the influence of strategic objectives on one another is determined. Afterward, optimal values for strategic objectives are determined to achieve the organization’s goals. Research limitations/implications This paper proposes a framework for constructing a strategy map and setting quantitative targets in the framework of BSC. Indeed, this paper presents a case study to demonstrate the applicability and effectiveness of the proposed approach. However, SES technique requires a greater amount of data to generate more accurate results. Although the advent of the Information Age has forced organizations’ decision makers to provide sufficient information and data for business analysis, the data requirements are met. Practical implications The presented quantitative approach is a supporting approach for improving decision makers’ opinions and enabling them to reach a more accurate picture of the relationships, valuing strategic objectives and achieving strategic goals. This research also presents a case study to demonstrate the applicability of the proposed approach. The application and implication of the proposed method in banking services show that the contributions of the paper are not only theoretical, but also practical. Originality/value The proposed method provides a novel approach for determining the most appropriate targets and applies a comprehensive and scientific model together with decision makers’ opinions and experiences and has two main contributions: first, the associations among strategic objectives are investigated and obtained in an effective way by conducting the SES for the first time in the framework of BSC. Second, quantitative targets have been determined to help in achieving the long-term goals. This task has been accomplished through a combination of SES, the three-stage least squares regression analysis and optimization by using weighted goal programming method.

Generalized Performance Measures of X- Control Charts Based on Different Sampling Schemes

Different versions of X- control chart structure are available under various ranked set strategies. In these control charts, computation of performance measures was carried out through Monte Carlo simulation method (MCSM). In this article, we have defined a generalized structure of X- control charts under variant sampling strategies followed by derivation of their different performance measures. For the derivation of different performance measures, we have proposed pivotal quantity. For comparative analysis, we have presented results of generalized performance measures by involving numerical method (NM) as computation. We found that values of generalized performance measures based on NM are almost similar to values of performance measures based on MCSM. Also, NM is time efficient and can be considered as an alternative of MCSM.

Defining and Adopting Clinical Performance Measures in Graduate Medical Education: Where Are We Now and Where Are We Going?

Assessment and evaluation of trainees' clinical performance measures is needed to ensure safe, high-quality patient care. These measures also aid in the development of reflective, high-performing clinicians and hold graduate medical education (GME) accountable to the public. Although clinical performance measures hold great potential, challenges of defining, extracting, and measuring clinical performance in this way hinder their use for educational and quality improvement purposes. This article provides a way forward by identifying and articulating how clinical performance measures can be used to enhance GME by linking educational objectives with relevant clinical outcomes. The authors explore four key challenges: defining as well as measuring clinical performance measures, using electronic health record and clinical registry data to capture clinical performance, and bridging silos of medical education and health care quality improvement. The authors also propose solutions to showcase the value of clinical performance measures and conclude with a research and implementation agenda. Developing a common taxonomy of uniform specialty-specific clinical performance measures, linking these measures to large-scale GME databases, and applying both quantitative and qualitative methods to create a rich understanding of how GME affects quality of care and patient outcomes is important, the authors argue. The focus of this article is primarily GME, yet similar challenges and solutions will be applicable to other areas of medical and health professions education as well.

ANALISIS ANTREAN DAN KINERJA SISTEM PELAYANAN GARDU TOL OTOMATIS GERBANG TOL MUKTIHARJO (Studi Kasus: Gardu Tol Otomatis Gerbang Tol Muktiharjo)

Queue process is a process related to the arrival of customers in a service facility, waiting in line queue if it cannot be served, get service and finally leaves the facility after being served. Research on the queue process can be seen directly through the queue system at the automatic toll booth Muktiharjo. Queue models and their distribution were obtained using the Sigma Magic program. The model of the vehicle queue system at the Muktiharjo Automatic Toll Gate is (GAMM/ GAMM/ 4): (GD/ ∞/ ∞). Based on the values of the queue system performance measures obtained through the MATLAB GUI program as a whole it can be concluded that the queue of vehicles at the Muktiharjo Automatic Toll Gate has a condition where the average number of vehicles estimated in the system every 15 minutes is 25,5646 vehicles. The average number of vehicles in the queue system every 15 minutes is 24,5639 vehicles. The waiting time in the system is estimated to be around 7,99332 seconds. The estimated waiting time in line is around 7,68042 seconds. The queue system has a busy opportunity of 63.2849% and the remaining 36.7106% is a chance the queue system is not busy. The simulation of the vehicle queue system at the Automatic Toll Gate of Muktiharjo Toll Gate by using ARENA is optimal with the number of service points as many as 4 automatic toll booths. Keywords: Automatic Toll Booth, Queue, Gamma Distribution, Performance Size, Queue Simulation

Optimal design of reduced-order observers with specified eigenvalues and performance measurement of minimizing estimation errors using evolutionary optimization

A new method is proposed in this paper which designs a reduced-order observer of a non-observable-form-based dynamical system such that: (i) the eigenvalues are specified to satisfy desired convergence performance, (ii) a full-rank condition is satisfied, and (iii) a quadratic performance measurement of the deviation of the estimates from the actual states is minimized so as to reduce the large error occurring during the transient period of observation. The proposed approach combines the merits of both the orthogonal functions approach and evolutionary optimization. By solving a Sylvester equation, the proposed optimal design method can not only be used to design the reduced-order observer of a non-observable-form-based dynamical system, but also can avoid the shortcomings of approaches already existing in relevant literatures. Two examples are given to demonstrate the effectiveness and efficiency of the proposed new optimization method on the performance of state estimations. From the demonstrative examples, it can be seen that the estimated state errors asymptotically converge quickly to zero. In addition, the performance measurement values based on the proposed optimal design approach are apparently much lower than those based on the existing nonoptimal design method.

Low-frequency oscillation damping in the electric network through the optimal design of UPFC coordinated PSS employing MGGP

The coordination of flexible AC transmission systems (FACTS) with power system stabilizers (PSS) can significantly enhance the overall network stability by damping out the low-frequency oscillations (LFO). This paper proposes a multi-gene genetic programming (MGGP) approach to optimize PSS parameters coordinated with unified power flow controller (UPFC) to enhance power system stability by damping out LFO. The obtained results (minimum damping ratio, eigenvalues and time domain simulations) of the proposed MGGP approach are compared with the results of the conventional fixed gain model, the single-gene genetic programming (SGGP) approach and the referenced work to inquire the efficacy of the proposed approach for various operating conditions of the considered electric network. Besides, the acceptable values of standard statistical performance measures in estimating UPFC-PSS parameters provide confidence on the evolved MGGP models. Furthermore, the proposed approach requires a minimal time (∼less than a cycle) to estimate the key parameters that signal the real-time application of the proposed technique.

PENGUKURAN KINERJA PDAM KOTA MAKASSAR BERBASIS BALANCED SCORECARD

This study aims to analyze the performance ratings of PDAM Makassar City based on performance assessment issued by BPPSPAM to assess the aspects studied in the form of managerial functions by using Balanced Scorecard approach as a measuring instrument. The research approach used is normative approach and using qualitative method. The results showed that the performance of financial perspective on the level of ability to meet short-term liabilities (cash ratio) companies experiencing illiquid where PDAM Makassar City is financial distress short-term. Customer perspective shows the result of customer growth ratio and domestic water consumption show less performance value. For internal business process perspective experiencing the same thing that waterlessness indicator happened big enough water loss, also because by error of recording (human error) related to record water account, this cause low water sold. While the perspective of human resources on the indicator ratio training and employee training load showed a degree that is not good, this case show development of employee competency is not top priority of PDAM Makassar City.Finally, although there are many problems found, but overall by looking at the accumulation value of balanced scorecard performance measurement of PDAM Makassar city shows a healthy performance (shows 3,37 points in 2015 and 3,39 points in 2014 , with the standard value of healthy performance category is ≥ 2,8 points).Researchers expect PDAM Makassar City to make improvements to the problems found, to create more optimal performance in the future.

Automated Tumor Segmentation Based on Hidden Markov Classifier using Singular Value Decomposition Feature Extraction in Brain MR images

ntroduction: Diagnosing brain tumor is not always easy for doctors, and existence of an assistant that facilitates the interpretation process is an asset in the clinic. Computer vision techniques are devised to aid the clinic in detecting tumors based on a database of tumor contained images. These precise techniques open up the way for recognizing a tumor even in the early stages of cancer development depicted as very low contrast object in an image. To this end, at first a set of similarity measures are calculated for each pixel in the image database. Then, this dataset is fed into a classifier that defines the mathematical rules necessary for making decision about a new image. After defining the rules, a model is formed which is ready to make decisions for new images outside the database. This decision is accompanied with an accuracy which is calculated in the end as the classifier performance measures. Materials and Methods: In this study, Singular Value Decomposition – SVD – method is used as a tool to extract similarity measures for each malignant image in the database. These measures form a matrix with which the classifier is trained. The classifier in this study, is a model based on Hidden Markov Random Field – HMRF – idea, that utilizes Bayes’ rule and maximum a posteriori criterion to obtain labels for each pixel and making decisions about he new images respectively. After sorting the new image as either malignant or not, the malignant image goes through a wavelet process for tumor segmentation. In this section, the image is transformed into a multilevel wavelet structure. Image segmentation is done using direct and inverse wavelet transform. Results: The classification performance is done quantitatively and qualitatively by calculating Volume Overlap Ratio (VOR), Recognition Rate (RR), True Negative Rate (TNR), True Positive Rate (TPR), Accuracy (ACC), Sensitivity and Specificity parameters, and 5 output images respectively. Also, a qualitative comparison is made between MRF, HMRF and morphological image segmentation methods. Conclusion: The application of HMRF-Wavelet pattern recognition has been investigated in this work. VOR, RR, TNR, TPR, ACC, Sensitivity and Specificity are calculated, and most values have been reported as over 95% for five test images. Despite the complexity of the HMRF statistical modeling, acceptable performance measure values indicate the sufficiency of this algorithm.

On efficient construction and evaluation of runs rules–based control chart for known and unknown parameters under different distributions

AbstractIn this study, we have considered two design structures of control chart by covering the situations of known and unknown parameters, variety of probability distributions, and runs rules. The design structures are dependent on constants which generally considered hard to compute analytically. For construction of constants and also for evaluating performance of the design structures through performance measures, we have illustrated Monte Carlo simulation procedure/algorithm for researcher and practitioners. Furthermore, based on the Monte Carlo simulation procedures, we have established a program in R language to compute values of different constants and performance measures. Results illustrated that design structures for known and unknown parameters under variety of runs rules and probability distributions have outstanding performance in contrast to existing structures. Moreover, design structure for unknown parameters behaves alike the design structure for known parameters. This indicates that design structure for unknown parameters has the ability to resolve the issue of runs rules which generally occur when parameters are estimated. Besides, two real‐life examples have been included in which physicochemical characteristic of groundwater and plasticizer characteristic of petrochemical process are monitored through design structures.

Dashboards to Support Operational Decision Making in Health Centers: A Case for Role-Specific Design

ABSTRACTApplying a user-centered design (UCD) process within the framework of a learning healthcare system (LHS), this study unearths role-specific performance measures to support operational and financial decision making in community health centers (CHCs). We first built a multidimensional EHR/Practice Management data warehouse through a large collaborative of seven CHCs in the state of Indiana aimed at improving efficiency and access to care. A UCD process that comprised contextual interviews, card sorting and high fidelity dashboard prototyping was used to uncover over 45 different operational performance measures, many of which were unique and hitherto unknown measures of relevance to different individual roles within a CHC that included frontline staff, providers, clinical support, and executive management. Within the LHS paradigm, the study highlights the value of role-specific performance measurement and their delivery through interactive user-centered visualizations, while continuing to guide the development of future informatics tools.

Robust topology optimization of skeletal structures with imperfect structural members

A topology optimization framework is proposed for robust design of skeletal structures with stochastically imperfect structural members. Imperfections are modeled as uncertain members’ out-of-straightness using curved frame elements in the form of predefined functions with random magnitudes throughout the structure. The stochastic perturbation method is used for propagating the imperfection uncertainty up to the structural response level, and the expected value of performance measure or constraint is used to form the stochastic topology optimization problem. Sensitivities are derived explicitly using the adjoint method and are used in conjunction with an efficient gradient-based optimizer in search for robust optimal topologies. Topological designs for three representative examples are investigated with the proposed algorithm and the resulting topologies are compared with the deterministic designs. It is observed that the new designs primarily feature load path diversification, which is pronounced with increasing level of uncertainty, and occasionally member thickening to mitigate the impact of the uncertainty in members’ out-of-straightness on structural performance.

Determining quantitative targets for performance measures in the balanced scorecard method using response surface methodology

PurposeThe purpose of this paper is to propose a quantitative methodology for setting targets in the framework of Balanced Scorecard (BSC) in order to achieve vision and goals.Design/methodology/approachResponse Surface Methodology is proposed to find the significant relationships that should be included in the strategy map and the optimal values of performance measures are assessed by using the desirability function-based approach of RSM. The proposed method was created by reviewing the existing literature, modeling the problem, and applying it in an oil company. In fact, RSM is used to execute the design matrix, analyze the collected data, extract models, analyze the results, and optimize the procedures that generate multiple responses.FindingsBy applying this methodological design, a clearer picture of the relationships between strategic objectives is obtained and the influence of strategic objectives on one another is determined. Afterward, optimal values for performance measures are determined.Research limitations/implicationsThis paper proposes a framework for constructing a strategy map and setting quantitative targets to translate the goals and strategies into corresponding performance measures and targets. Also, this paper presents a case study to demonstrate the applicability and effectiveness of the proposed approach. However, RSM-based techniques require a greater amount of data to generate more accurate results. Although the advent of the Information Age has forced organizations’ decision makers to provide sufficient information and data for business analysis, the data requirements of RSM-based techniques are met.Practical implicationsIn practice, the process of setting targets for performance measures can be challenging in terms of reaching a consensus between managers and decision makers. The findings of this paper can offer a new approach for performance evaluation based on the BSC which allows the organization’s decision makers to reach a more accurate picture of the relationship model between organization goals and those objectives within the BSC. It also demonstrates how decision makers can be guided in the process of defining performance target values in the BSC method.Originality/valueReviewing the literature on setting quantitative targets within the framework of the BSC showed no prior study in which RSM is used. This approach has two main contributions: the associations among strategic objectives are investigated and obtained in an effective way which analytically identifies the direction and degree of the relations among the performance measures. Considering the performance evaluation structure based on the BSC, quantitative targets have been determined to help in achieving the long-term goals of the organization. The application of the proposed method in a company showed that the contributions of this research are not only theoretical, but practical as well.

Online tuning of power system stabilizer employing genetic programming for stability enhancement

This paper aims to design optimal power system stabilizer (PSS) employing genetic programming (GP) to enhance system stability through damping the low-frequency oscillations out. GP is used to develop model equations for PSS parameters where the inputs are generator terminal voltage, active and reactive powers. The dynamic performance of the proposed technique is validated through the values of statistical performance measures. The mean squared error (MSE) and mean absolute percentage error (MAPE) are quite low whereas the coefficient of determination (R2) is reasonably high for the developed model. In addition, the eigenvalue and minimum damping ratio analysis, as well as time domain simulation, were carried out, which also demonstrate the efficacy of the proposed model.

Optimal Configuration of the Metro Rail Transit Station Service Facilities by Integrated Simulation-Optimization Method Using Passengers’ Flow Fluctuation

This paper proposes an integrated discrete-event simulation (DES) using phase-type (PH) distribution and optimization method for the performance assessment, as well as optimal configuration of metro rail transit (MRT) station service facilities. The MRT facilities (including ticket facilities and elevator) are represented as a queuing network system with the passengers’ arrival flow and the service time based on the PH distribution, which considers the flow and service time fluctuation. The necessary performance measures include facility utilization, mean number of passengers and waiting time of passengers in the facilities are obtained by PH-based DES model of service facilities. The model is then coupled with a genetic algorithm that works simultaneously to give the optimal configuration of service facilities. The results of PH-based DES model are first verified by existing PH-based analytical models and then compared with other existing exponential-based and deterministic-based queuing models. The results reveal that increase in squared coefficient of variation of arrival interval causes an increase in the performance measure values and high number of ticket facilities compared to existing models. The existing models therefore underestimate the results. The squared coefficient of variation is an important parameter and cannot be ignored in analysis and design of service facilities. This research provides a more realistic and novel PH-based DES, as well as PH-based simulation-optimization method that will assist the planners and designers of MRT stations to make intelligent decisions regarding analysis and design.

Application of Various Multiple Criteria Analysis Methods for the Evaluation of Rural Road Projects

Introduction:Ex-ante evaluation of rural road projects is important for proper utilisation of scarce financial resources. In recent years, several multiple criteria analysis methods have been applied for the evaluation of transportation projects.Methods:Here we compare three multi-criteria methods (i.e. TOPSIS, MOORA, and PROMETHEE) in ranking rural roads. The criteria and weightage were identified with an expert questionnaire survey and the decision matrix was obtained from a field survey of four rural roads of Nepal. These data demonstrate the successful application of various multi-criteria analysis methods for the evaluation of rural road projects with sustainable indicators.Results:The results of multi-criteria methods depend upon the value of performance measures, weightage, normalization, and aggregation procedures.Conclusion:We also concluded that the multi-criteria methods used (i.e. TOPSIS, MOORA, and PROMETHEE), in which weightage is derived using robust methods (e.g. AHP) and precise measurement of criteria of rural roads, in general return similar rankings.

Amyloidogenic motifs revealed by n-gram analysis

Amyloids are proteins associated with several clinical disorders, including Alzheimer’s, and Creutzfeldt-Jakob’s. Despite their diversity, all amyloid proteins can undergo aggregation initiated by short segments called hot spots. To find the patterns defining the hot spots, we trained predictors of amyloidogenicity, using n-grams and random forest classifiers. Since the amyloidogenicity may not depend on the exact sequence of amino acids but on their more general properties, we tested 524,284 reduced amino acid alphabets of different lengths (three to six letters) to find the alphabet providing the best performance in cross-validation. The predictor based on this alphabet, called AmyloGram, was benchmarked against the most popular tools for the detection of amyloid peptides using an external data set and obtained the highest values of performance measures (AUC: 0.90, MCC: 0.63). Our results showed sequential patterns in the amyloids which are strongly correlated with hydrophobicity, a tendency to form β-sheets, and lower flexibility of amino acid residues. Among the most informative n-grams of AmyloGram we identified 15 that were previously confirmed experimentally. AmyloGram is available as the web-server: http://smorfland.uni.wroc.pl/shiny/AmyloGram/ and as the R package AmyloGram. R scripts and data used to produce the results of this manuscript are available at http://github.com/michbur/AmyloGramAnalysis.

Levenberg–Marquardt neural network to estimate UPFC-coordinated PSS parameters to enhance power system stability

Due to the presence of weak tie line interconnections, small signal oscillations are created in power system networks. Damping out these oscillations is one of the most crucial issues to be settled down for the stability of power system industry. The employment of flexible AC transmission systems (FACTS) may suppress these oscillations effectively in addition to the enhancement of power transfer capability. Unified power flow controller (UPFC) is one of those FACTS devices which are installed in the powers grids, which ensures proper functionality of high-voltage transmission lines. To select the proper parameters of power system stabilizer (PSS) when applied with UPFC is a challenge in this field which can be represented as a multi-objective optimization problem. This work aims to optimize the PSS parameters of power network incorporating UPFC using the artificial neural network (ANN) in real time to damp out the small signal oscillations with a view to enhancing the stability of the power system where the Levenberg–Marquardt (LM) algorithm is used as the training algorithm. System eigenvalues obtained from ANN-tuned PSS coordinated with UPFC and the fixed gain conventional PSS with UPFC are compared to investigate the efficiency of the proposed technique for different loading conditions. Additionally, the comparison has been made in time domain simulation results which prove the superiority of the proposed technique over conventional technique. Moreover, the satisfactory values of statistical performance measures validate the efficacy of the prediction capability of the proposed LM-NN approach.

Performance measurement maturity in a national set of universities

PurposePerformance measurement in higher education has attracted substantial attention, often focussing on the applicability and value of performance measurement concepts to the sector. The purpose of this paper is to use components of a seven-element maturity model to examine the development of performance measurement maturity in New Zealand universities in the period 2008-2013.Design/methodology/approachDocumentary analysis was the primary approach. A total of 48 annual reports were examined. The focus was the statement of service performance, but all surrounding material was also examined. Each annual report was subjected to a range of quantitative and semi-quantitative analyses.FindingsUniversities have shown strengths in aligning measures to strategic direction, the quality of commentary, and improvement in the use of outcomes frameworks. More variable results have been seen in the breadth and quality of measures, and most importantly, in the use of performance information to guide institutional decision-making. This lack of evolution is likely to be linked to the particular accountability relationships surrounding the universities, which while part of the public sector are semi-autonomous. It is also likely to be linked to academic organisational culture.Originality/valueThere have been few examinations of the use of performance measurement by universities, with most studies focussing less on operational practice than on broader theoretical issues. This study provides useful information about the actual use of performance measurement.

Provide management package in irrigation networks to achieve optimal performance measures

Saving water and improving water productivity influence the socioeconomic measures like equity that should be considered in water distribution management especially in drought conditions. In this research for different scenarios of irrigation water management, the performance measures of water equity and productivity in Ordibehesht channel (Doroodzan irrigation network) was optimised simultaneously by using genetic algorithm (GA). The results showed that rising water reduction fraction (WRF) (0.0 to 0.8) has the incremental effect on water equity and productivity (on average 39.2% and 43.3%, respectively). With increasing irrigation application efficiency (Ea) (40% to 90%), the values of water equity and productivity increased by on average 59.9% and 113.5%, respectively. Increment of conveyance efficiency of channels (Ec) (70% to 90%) resulted in minimum incremental effect on water equity and productivity (on average 19.8% and 14.1%, respectively). Furthermore, the values of performance measure decreased from wet water year to drought water year.