Existing Performance Evaluation Methods Research Articles

Development and implementation of real-time staff performance monitoring and evaluation system based on Global Positioning System (GPS)

This work presents the development and implementation of a Real-Time Staff Performance Monitoring and Evaluation System (RT-SPMES) using Global Positioning System (GPS) technology. In line with the dynamism of the workplace, due to rapid changes in technology, existing staff performance appraisal systems have fallen short of expectations due to their manual nature. Hence, the need arises for a more robust approach to monitoring and evaluating employee work performance. To achieve this, this work carried out an exhaustive review of existing performance evaluation methods within the academic and a conceptual architecture was developed. A real-time monitoring and evaluation system was developed, using PHP programming language, Hyper Text Markup Language and Cascading Styles Sheet. The system is hosted on Azure Cloud infrastructure. Through sufficient testing of the developed system developed in this work, it was asserted effectively monitors employee performance in real-time, paving the way for an objective appraisal process devoid of bias and manipulation.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters’ opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

Enterprise performance online evaluation based on extended belief rule-base model

The performance evaluation method based on data envelopment analysis (DEA) is one of the important tools to measure the competitiveness and productivity of enterprises. However, the input and output of enterprises may contain negative data and the essence of DEA is an iterative optimization model, resulting in a low applicability of the DEA-based performance evaluation method in the real word, especially for the dilemma of evaluating enterprise performance within a limited time for new enterprises. Therefore, this study firstly develops a DEA model that can handle negative data for enterprise performance evaluation, and then further establishes a new method base on the extended belief rule-base (EBRB) model for enterprise performance online evaluation. A case study about 35 Chinese state-owned enterprises are conducted to verify the effectiveness of the proposed enterprise performance online evaluation method. Experimental results showed that the proposed method has capable of evaluating enterprise performance with accurate efficiency values better than some existing performance evaluation methods, and its computation time is significantly less than the DEA-based performance evaluation method, which guarantee that the proposed enterprise performance online evaluation method can serve as a reference for the promotion of enterprise productivity and sustainable economic development.

Analysis and Research on Provincial Postgraduate Cultivation Performance Based on BP Neural Network

According to relevant data, the comprehensive competitiveness and quality of graduate education in Henan Province are in the middle of the 31 provinces in China, and its graduate education has a great room for improvement. Through the research on the current situation of educational performance evaluation at home and abroad, and the analysis of the limitations in the evaluation process of postgraduate training in Henan province, we proposed a performance evaluation method based on BP neural network. This method uses the theory and technology of information theory, analytic hierarchy process, and BP neural network to establish an educational performance evaluation model. Through this model, the shortcomings of the existing performance evaluation methods of graduate student training are overcome, and performance evaluation methods that are closer to the actual situation are excavated, which provides a scientific decision-making reference for the performance evaluation of graduate education in Henan Province. This experiment uses the university education data of Henan Province as the experimental data test set. The experimental results show that this model has good generalization ability and can provide reference for the reform and construction of postgraduate education in Henan province.

Development of a participation assessment system based on multimodal evaluation of user responses for upper limb rehabilitation

The patient's active participation in exercises is a crucial factor to increase the functional outputs received from therapy. For improving the patient's active and voluntary involvement, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient's therapy performance. However, the existing performance evaluation methods are based on either some specific device designs or certain therapy tasks. In this work, a patient performance evaluation method is proposed based on a multimodal sensor fusion formed by the trajectory tracking error signal and the patient's physiological responses (heart rate and skin conductance) during the upper extremity rehabilitation. The novelty of the system is assessing the patient's performance independently from any device designs or therapy tasks. The developed system also evaluates the patient's tiredness and slacking, which are substantial factors affecting the therapy performance. The patient's upper limb joints' angles are measured via inertial measurement unit sensors. Arm movements of the patient are estimated by an upper limb kinematic module for evaluating the trajectory tracking. The patient's performance, tiredness, and slacking are assessed by a fuzzy inference system using physiological responses and exercise profile. The developed system is tested experimentally with healthy subjects on five therapy tasks. Also, for demonstrating the proposed method efficacy, additional experiments have been performed for different cases while measuring the sEMG signals of the subjects. The experimental results showed that the proposed system estimates subjects’ participation successfully and adjusts the therapy tasks according to subjects' performance and tiredness.

Integrating Simulation and Numerical Analysis in the Evaluation of Generalized Stochastic Petri Nets

The standard existing performance evaluation methods for discrete-state stochastic models such as Petri nets either generate the reachability graph followed by a numerical solution of equations or use some variant of simulation. Both methods have characteristic advantages and disadvantages depending on the size of the reachability graph and type of performance measure. This article proposes a hybrid performance evaluation algorithm for the steady-state solution of Generalized Stochastic Petri Nets that integrates elements of both methods. It automatically adapts its behavior depending on the available size of main memory and number of model states. As such, the algorithm unifies simulation and numerical analysis in a joint framework. It is proved to result in an unbiased estimator whose variance tends to zero with increasing simulation time. The article extends earlier results with an algorithm variant that starts with a small maximum number of particles and increases them during the run to increase the efficiency in cases that are rapidly solved by regular simulation. The algorithm’s applicability is demonstrated through case studies, including an example where it outperforms the standard methods.

Performance evaluation of employees using Bayesian belief network model

It is a generally acknowledged fact that employee performance evaluation is a critical managerial tool for any organisation. In the global economy, the modern industrial and commercial organisation needs to develop effective methods for assessing the performance of their human resources. In this study, a Bayesian belief network (BBN) model is developed to evaluate the performance of an employee considering the dependencies and correlations between the criteria. The capabilities of the proposed approach are demonstrated on the lumber assembly section of a furniture manufacturing company in Bangladesh. Kendall’s rank correlation coefficient is used to identify the correlation between the criteria. The results indicate that the proposed BBN-based model can explicitly quantify uncertainties and handle the complex relationships between the criteria better when compared with existing performance evaluation methods. The proposed model is also capable of assessing the credibility of multiple experts and ranking employees for different purposes such as reward, improvement, training, promotion, termination, compensation, etc.

Laboratory evaluation of short and long term performance of hot-poured sealants

Hot-poured sealants are commonly applied to repair cracks on asphalt pavements. However, existing methods of performance evaluation do not fully reflect the performance of hot-poured sealant for pavement application. Therefore, this article proposes different tests, melting point and melt flow index of packing films, sealant viscosity, segregation, thermal aging performance, and bond after water immersion, to specifically address identified problems and evaluate the short and long term performance of hot-poured sealant. A sealant performance ranking system was proposed based on the tests of nine sealants. The indicators of current performance evaluation, installation performance as well as thermal and immersion stability were weighted in this system. Different test results were classified and scored and then nine sealants were ranked quantitatively. The proposed system provides a more comprehensive evaluation of field performance of hot-poured sealant compared to existing performance evaluation methods, and correctly ranks the sealants in terms of their practical performance. Therefore, the proposed tests can be employed to complement current performance evaluation indicators.

Performance Evaluation and Optimal Management of Distance-Based Registration Using a Semi-Markov Process

We consider the distance-based registration (DBR) which is a kind of dynamic location registration scheme in a mobile communication network. In the DBR, the location of a mobile station (MS) is updated when it enters a base station more than or equal to a specified distance away from the base station where the location registration for the MS was done last. In this study, we first investigate the existing performance-evaluation methods on the DBR with implicit registration (DBIR) presented to improve the performance of the DBR and point out some problems of the evaluation methods. We propose a new performance-evaluation method for the DBIR scheme using a semi-Markov process (SMP) which can resolve the controversial issues of the existing methods. The numerical results obtained with the proposed SMP model are compared with those from previous models. It is shown that the SMP model should be considered to get an accurate performance of the DBIR scheme.

안개 제거 기술의 정량적인 성능 평가 기법 조사

Image dehazing has been extensively studied, but the performance evaluation method for dehazing techniques has not attracted significant interest. This paper surveys many existing performance evaluation methods of image dehazing. In order to analyze the reliability of the evaluation methods, synthetic hazy images are first reconstructed using the ground-truth color and depth image pairs, and the dehazed images are then compared with the original haze-free images. Meanwhile we also evaluate dehazing algorithms not by the dehazed images' quality but by the performance of computer vision algorithms before/after applying image dehazing. All the aforementioned evaluation methods are analyzed and compared, and research direction for improving the existing methods is discussed.

Performance Evaluation Method of Self-Consolidating Concrete

Due to its outstanding performance and merits, Self-Consolidating Concrete (SCC) has attracted a wide attention from scientists, researchers and engineers all over the world. At present, many research activities have been conducted in developing and evaluating SCC and its related technologies. However, existing performance evaluation methods of are mostly limited in the field of material, while structure performance evaluation methods of SCC are rarely reported. As we all known, the lifetime of the SCC structure is mainly subject to its material, structural and durability performances as a whole. Undoubtedly, it is crucial for us to study and develop a sound structural and durability performance evaluation method to complement the existing material performance evaluation methods. As such, the structural and durability performance evaluation method of SCC was studied in this paper.

Performance Evaluation of Worm Attack and Defense Strategies Based on Static Bayesian Game

PDF HTML阅读 XML下载 导出引用 引用提醒 基于静态贝叶斯博弈的蠕虫攻防策略绩效评估 DOI: 10.3724/SP.J.1001.2012.03997 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家高技术研究发展计划(863)(2009AA01Z439); 国家高技术产业化项目信息安全专项; 信息网络安全公安部重点实验室(公安部第三研究所)开放基金(C10606) Performance Evaluation of Worm Attack and Defense Strategies Based on Static Bayesian Game Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:现有蠕虫攻击策略(防护策略)评估方法没有考虑防护策略(攻击策略)变化对攻击策略(防护策略)绩效评估的影响,且防护策略评估忽视了策略实施成本.针对这种情况,构建了基于静态贝叶斯博弈的绩效评估模型 (performance evaluation model based on static Bayesian game,简称PEM-SBG)以及对抗情形下的蠕虫攻防策略绩效评估方法.在模型PEM-SBG 基础上提出的基于灰色多属性理论的防护策略绩效评估方法,综合考虑了成本类和收益类的多个评估属性,有助于提高评估过程的全面性.针对典型的蠕虫攻防场景.利用仿真工具SSFNet 进行了模拟实验,验证了所提出的模型和方法的有效性. Abstract:The existing performance evaluation methods of worm attack strategies (defense strategies) are not considered defense strategies (attack strategies) change’s influence on attack strategies (defense strategies) and performance evaluation of defense strategies are ignoring the implementation cost. In view of this situation, a performance evaluation model based on static Bayesian game (PEM-SBG) is proposed, and the performance evaluation methods of worm attack and defense mechanisms are presented. The performance evaluation method of defense mechanisms is based on gray multiple attributes theory and considers several evaluation metrics about cost and utility, so the evaluation process is much more comprehensive. Finally, the paper uses simulation tools SSFNet to implement simulation experiments under different attack and defense scenarios and validate the method. 参考文献 相似文献 引证文献

A novel performance evaluation paradigm for automated video surveillance systems

Abstract Most existing performance evaluation methods concentrate on defining various metrics over a wide range of conditions and generating standard benchmarking video sequences to examine the effectiveness of a video tracking system. It is a common practice to incorporate a robustness margin or factor into the system/algorithm design. However, these methods, deterministic approaches, often lead to overdesign, thus increasing costs, or underdesign, causing frequent system failures. In order to overcome the aforementioned limitations, we propose an alternative framework to analyze the physics of the failure process via the concept of reliability. In comparison with existing approaches where system performance is evaluated based on a given benchmarking sequence, the advantage of our proposed framework lies in that a unified and statistical index is used to evaluate the performance of an automated video surveillance system independent of input sequences. Meanwhile, based on our proposed framework, the uncertainty problem of a failure process caused by the system’s complexity, imprecise measurements of the relevant physical constants and variables, and the indeterminate nature of future events can be addressed accordingly.

공공 정보화사업에 대한 통합성과 평가모델 개발과 적용에 관한 연구

최근 정보화에 대한 성과평가는 투자성과에 미치는 효과를 정량적, 정성적으로 분석하여, 전통적인 투자 수익률에 근거한 가치평가방법의 한계를 극복하기 위한 다양한 성과평가방법이 응용되고 있다. 하지만 공공분야의 성과평가는 평가영역에 대한 정성적 평가 중심의 성과여부를 도출하는데 머무를 뿐, 정보화 품질을 향상시키는 데까지는 이르지 못하는 단점이 있다. 따라서 본 논문에서는 기존 공공분야 성과평가의 한계점을 개선하고 품질평가와 정량적 평가를 반영함으로써, 정보화 품질 향상과 평가결과의 객관성 및 신뢰성을 보장하는 통합성과평가모델을 제안한다. 그리고 정보화사업으로 추진된 행정정보DB구축사업을 대상으로 사업목적을 고려한 평가영역별 반영비율 및 가중치를 적용한 평가결과를 도출하였다. Recently, an evaluation for information system performance analysis an investment effect quantitatively and qualitatively. So various performance evaluation methods are applied to overcome a limitation which is based on traditional investment returns. However a performance evaluation in public service only derives that a result which is qualitatively evaluated about evaluation domain is yes or no, the performance evaluation does not improve in quality of information system. So in this paper we improve existing performance evaluation methods in public service and add a quality evaluation and quantitative evaluation in the methods. Therefore we propose an integrated evaluation model which guarantees objectivity and reliability of evaluation result and which improves an information system. We consider a project object of an Administration Information DB Construction Project which is promoted in information-oriented project and derive an evaluation result which is applied reflection ratio and weight in each evaluation domain to show a validity of the proposed model.

Empirical performance evaluation methodology and its application to page segmentation algorithms

While numerous page segmentation algorithms have been proposed in the literature, there is lack of comparative evaluation of these algorithms. In the existing performance evaluation methods, two crucial components are usually missing: 1) automatic training of algorithms with free parameters and 2) statistical and error analysis of experimental results. We use the following five-step methodology to quantitatively compare the performance of page segmentation algorithms: 1) first, we create mutually exclusive training and test data sets with groundtruth, 2) we then select a meaningful and computable performance metric, 3) an optimization procedure is then used to search automatically for the optimal parameter values of the segmentation algorithms on the training data set, 4) the segmentation algorithms are then evaluated on the test data set, and, finally, 5) a statistical and error analysis is performed to give the statistical significance of the experimental results. In particular, instead of the ad hoc and manual approach typically used in the literature for training algorithms, we pose the automatic training of algorithms as an optimization problem and use the simplex algorithm to search for the optimal parameter value. A paired-model statistical analysis and an error analysis are then conducted to provide confidence intervals for the experimental results of the algorithms. This methodology is applied to the evaluation of live page segmentation algorithms of which, three are representative research algorithms and the other two are well-known commercial products, on 978 images from the University of Washington III data set. It is found that the performance indices of the Voronoi, Docstrum, and Caere segmentation algorithms are not significantly different from each other, but they are significantly better than that of ScanSoft's segmentation algorithm, which, in turn, is significantly better than that of X-Y cut.