Criteria For Performance Comparison Research Articles

Recommender System for Predicting the Academic Status of Students of Bangladesh Using Artificial Neural Network

Today, information technology in different areas produces a large amount of data every day. In recent years, many researchers have started to extract knowledge from data to make data-based decisions and improve the quality of processes in various organizations. Educational data mining helps educational institutions to operate effectively and efficiently by leveraging data from all stakeholders. It can help students at risk; create recommendation systems and alert students at different levels. Recently, neural network has received much attention in the educational sector compared to other methods. In this article, a study has been done using neural network on the dataset of Bangladesh University. The evaluation criteria for performance comparison are MAE (mean absolute error) and MSE (mean square error). The MAE and MSE of the neural network in the first run are 0.1357 and 0.026123, respectively. With the neural network, a recommender system has been presented to predict the students' academic status, which considers the student's status in four states: "excellent", "good", "needs more effort" and "needs guidance".

Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

This paper presents a general testing coverage software reliability modeling framework that covers imperfect debugging and considers not only fault detection processes (FDP) but also fault correction processes (FCP). Numerous software reliability growth models have evaluated the reliability of software over the last few decades, but most of them attached importance to modeling the fault detection process rather than modeling the fault correction process. Previous studies analyzed the time dependency between the fault detection and correction processes and modeled the fault correction process as a delayed detection process with a random or deterministic time delay. We study the quantitative dependency between dual processes from the viewpoint of fault amount dependency instead of time dependency, then propose a generalized modeling framework along with imperfect debugging and testing coverage. New models are derived by adopting different testing coverage functions. We compared the performance of these proposed models with existing models under the context of two kinds of failure data, one of which only includes observations of faults detected, and the other includes not only fault detection but also fault correction data. Different parameter estimation methods and performance comparison criteria are presented according to the characteristics of different kinds of datasets. No matter what kind of data, the comparison results reveal that the proposed models generally give improved descriptive and predictive performance than existing models.

Ionic thermoelectric materials for waste heat harvesting

Ionic thermoelectric polymers are a new class of materials with great potential for use in low-grade waste heat harvesting and the field has seen much progress during the recent years. In this work, we briefly review the working mechanism of such materials, the main advances in the field and the main criteria for performance comparison. We examine two types of polymer-based ionic thermoelectric materials: ionic conductive polymer and ionogels. Moreover, as a comparison, we also examine the more conventional ionic liquid electrolytes. Their performance, possible directions of improvements and potential applications have been evaluated.

Comparison of extreme learning machine models for gasoline octane number forecasting by near-infrared spectra analysis

The octane number is often used in the evaluation of the gasoline quality due to its antiknock property. The traditional ASTM-CFR internal combustion engine method costs an enormous amount of expenditure and time to determine its level of each product upgrade. The extreme learning machine (ELM) and its improved models are a new type of single-hidden layer feed-forward neural network (SLFN). This structure has advantages over the traditional neural network technology in regression tasks. Based on the response relationship between the near-infrared spectra and the octane number, 60 groups of collected gasoline data are divided into training and test sets. The ELM, online sequential extreme learning machine (OS-ELM), and self-adaptive evolutionary extreme learning machine (SaDE-ELM) models are applied to the prediction task of the octane number. The statistical parameters RMSE, CORR, and R2 and the execution time are used as the performance comparison criteria for the three models. The simulation results show that the OS-ELM and the SaDE-ELM improve the prediction accuracy, the generalization ability, the stability of the ELM in different levels.

Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources

Distributed control strategies applied to power distribution control problems are meant to offer robust and scalable integration of distributed energy resources (DERs). However, the term “distributed control” is often loosely applied to a variety of very different control strategies. In particular there is a lack of discrimination between aspects related to communication topology, physical distribution of components, and associated control objectives. This has lead to a lack of objective criteria for performance comparison and general quality assessment of state of the art distributed control solutions. For such comparison, a classification is required that is consistent across the different aspects mentioned above. This paper develops systematic categories of control strategies that accounts for communication, control and physical distribution aspects of the problem, and provides a set of criteria that can be assessed for these categories. The proposed taxonomy is applied to the state of the art as part of a review of existing work on distributed control of DER. Finally, we demonstrate the applicability and usefulness of the proposed classification to researchers and system designers.

Convective heat transfer performance of aggregate-laden nanofluids

With the recent progress in nanotechnology, nanofluids are emerging as a new class of heat transfer fluids formed by adding nanometer-sized structures (e.g., particles, fibers, tubes) in conventional base fluids (e.g., water, ethylene glycol, engine oil). Due to attractive van der Waals forces, nanoparticles tend to agglomerate to form aggregates in nanofluids to form the so-called aggregate-laden nanofluids. Aggregation affects the nanofluid properties such as thermal conductivity and viscosity and further affects the heat transfer performance. The discrepancies regarding the influence of nanoparticles on thermophysical properties and heat transfer characteristics in the literature might arise due to nanoparticle aggregation. Firstly, three performance comparison criteria for nanofluids were proposed for thermally developing laminar flow, fully developed laminar flow and fully developed turbulent flow to evaluate the nanofluid efficiency as coolants. Secondly, parametric effects of aggregates on nanofluid viscosity and thermal conductivity were investigated. The cooling efficiency of the aggregate-laden nanofluids depends on aggregate parameters such as aggregate ratios, interfacial thermal resistance, volume fraction of aggregates in nanofluids and volume fraction of nanoparticles in the aggregates. One method to tailor the aggregate morphology is presented by dispersing nanoparticles of different size into a base fluid. By this method, the volume fraction of nanoparticles in the aggregates might increase, which thus enhances the nanofluid effectiveness due to reduction of viscosity.

A survey of calibration-free indoor positioning systems

Last decade observed a significant research effort directed towards indoor localization utilizing location fingerprinting techniques. Fingerprinting solutions generally require a pre-deployment site survey procedure during which a radio-map is constructed by laboriously collecting signal strength samples (e.g., Wi-Fi) over the whole localization area. However, such localization efforts have certain shortcomings. For example, it is time consuming, labor intensive, vulnerable to environmental changes, and the process requires certain pedigree on the surveyor that may deem the fingerprinting techniques impractical to be deployed over large areas (e.g., shopping malls, multi-storey offices/residences, etc.). Newer emerging techniques try to bypass this expensive pre-deployment effort of fingerprinting solutions altogether. They may build the radio-map through the implicit participation of the building occupants, office employee, shoppers, visitors, etc. Apart from the traditional performance comparison criteria like accuracy, precision, robustness, scalability, algorithmic complexity based on which the localization techniques were evaluated, these newer approaches warrant some additional ones. For example, whether they require an actual geographical map of the localization area, the percentage of occasional location fix to ensure reasonable accuracy, the usage of explicit/implicit user participation to construct the radio map, the usage of building landmarks (e.g., entrance, conference room, elevator, escalator, etc.) or additional sensors (e.g., accelerometer, gyroscope, compass, etc.), whether they address device heterogeneity, etc. In this article, we survey the newer emerging fingerprinting solutions that try to relieve the pre-deployment woes. We also identify some newer performance comparison criteria based on these solutions’ inherent characteristics, and apply them together with the traditional ones in order to evaluate a number of such proposed systems.

A Comparative Study of Energy Management Schemes for a Fuel-Cell Hybrid Emergency Power System of More-Electric Aircraft

This paper presents a comparative analysis of different energy management schemes for a fuel-cell-based emergency power system of a more-electric aircraft. The fuel-cell hybrid system considered in this paper consists of fuel cells, lithium-ion batteries, and supercapacitors, along with associated dc/dc and dc/ac converters. The energy management schemes addressed are state of the art and are most commonly used energy management techniques in fuel-cell vehicle applications, and they include the following: the state machine control strategy, the rule-based fuzzy logic strategy, the classical proportional–integral control strategy, the frequency decoupling/fuzzy logic control strategy, and the equivalent consumption minimization strategy. The main criteria for performance comparison are the hydrogen consumption, the state of charges of the batteries/supercapacitors, and the overall system efficiency. Moreover, the stresses on each energy source, which impact their life cycle, are measured using a new approach based on the wavelet transform of their instantaneous power. A simulation model and an experimental test bench are developed to validate all analysis and performances.

The usability inspection performance of work-domain experts: An empirical study

It is a challenge for usability experts to perform usability inspections of interactive systems that are tailored to work-domains of which these experts have little knowledge. To counter this, usability inspections with work-domain experts have been explored, but little empirical research has been reported on these experts’ performance as evaluators. The present study compared the performance of work-domain experts and usability experts with respect to validity and thoroughness. The work-domain experts were characterized by high computer experience and low system experience. The usability experts were recruited from different ICT companies. The usability inspection method applied was group-based expert walkthrough; a method particularly developed to support non-usability experts as evaluators. The criterion for performance comparison was established through user tests. Fifteen work-domain experts and 12 usability experts participated in the study. The work-domain experts generated equally valid but less thorough usability inspection results than did the usability experts. This finding implies that work-domain experts may be used as evaluators in usability inspections without compromising validity. Moreover, the usability inspection performance of nominal groups of evaluators was explored. It was found that nominal groups of work-domain experts produced results of similar quality as did nominal groups of usability experts, given that group size is disregarded. This finding may be used as basis for hypotheses in future studies on the usability inspection performance of nominal groups of work-domain experts.

Performance comparison criteria for ATM switch models

Performance of ATM networks will depend on switch performance and architecture. In this paper a set of 11 questions is presented in order to be able to study performance. It is necessary to clearly identify the main characteristics of switch architecture and traffic which are considered so as to be able to derive relevant comparisons. The questions concern the level of study, the placement of the memory, the monopath or multipath architecture, the uniformity of input stream, the destination hypotheses, the routing rules inside the switch, the eventual resequencing, etc. In case of cell level study and output buffers, the answers to the six main questions lead to a tree including 14 realistic switch and traffic types. A survey of the modelling method for these cases is then presented. In order to illustrate the methodology, few results are given in some of the realistic cases and some general conclusions appear to be pertinent: importance of traffic asymmetry, best performance for multipath switches, use of path reservations inside the switch.