Computer Performance Evaluation Research Articles

The evaluation of computational modelling performance within the context of rationality theory: finding the area between two curves

ABSTRACT This study aimed to investigate the development of computational modelling performances of students during the computer-assisted system (CAS) integrated ACODESA method based on Habermas’ construct of rationality. The participants included 22 university students. In the study, in which the embedded design was utilised, the qualitative phase aimed to examine the computational modelling performances of the students during the stages of CAS integrated ACODESA method. It was determined that the students showed improvement in modelling the Riemann sum accurately with CAS, and in choosing to use for loop to determine the area of the rectangles that represented the Riemann sum, and in comprehensibility and acceptability. In the quantitative phase of the study, pre and post-tests were conducted to determine the effect of the CAS-integrated ACODESA method on computational modelling performances of the students. The computational modelling scores were compared with the paired samples t-test. It was found that CAS-integrated ACODESA method made statistically significant effect on computational modelling performances of the students.

Computational imaging-based single-lens imaging systems and performance evaluation

The minimalist optical system has a simple structure, small size, and lightweight, but the low optical complexity will produce optical aberration. Addressing the significant aberration degradation in minimalist systems, we propose a high-quality computational optical framework. This framework integrates a global point spread function (PSF) change imaging model with a transformer-based U-Net deep learning algorithm to achieve high-quality imaging in minimalist systems. Additionally, we introduce an imaging performance evaluation method based on the modulation transfer degree of resolution (MTR). We addressed severe chromatic and spherical aberrations in single-lens systems, a typical example of minimalist optical systems, by simulating the degradation process and reconstructing the imaging effects. This approach demonstrated significant improvements, thus validating the feasibility of our method. Specifically, our technique calculated the MTR values in real images captured with the GCL010109 single lens at 0.8085, and with the GCL010110 single lens at 0.8055. Our method enhanced the imaging performance of minimalist systems by 4 times, upgrading minimalist system capabilities from poor to good lens grade. This work can provide reference for wavefront coding, matelens, diffraction optical systems, and other computational imaging work. It can also promote the application of miniaturization of medical, aerospace, and head-mounted optical systems.

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

Cost-effective internet of things privacy-aware data storage and real-time analysis

<span lang="EN-US">It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.</span>

Performance Evaluation of a Cloud Datacenter Using CPU Utilization Data

Cloud computing and its associated virtualization have already been the most vital architectures in the current computer system design. Due to the popularity and progress of cloud computing in different organizations, performance evaluation of cloud computing is particularly significant, which helps computer designers make plans for the system’s capacity. This paper aims to evaluate the performance of a cloud datacenter Bitbrains, using a queueing model only from CPU utilization data. More precisely, a simple but non-trivial queueing model is used to represent the task processing of each virtual machine (VM) in the cloud, where the input stream is supposed to follow a non-homogeneous Poisson process (NHPP). Then, the parameters of arrival streams for each VM in the cloud are estimated. Furthermore, the superposition of estimated arrivals is applied to represent the CPU behavior of an integrated virtual platform. Finally, the performance of the integrated virtual platform is evaluated based on the superposition of the estimations.

Computational Performance Evaluation of Column Generation and Generate-and-Solve Techniques for the One-Dimensional Cutting Stock Problem

The Cutting Stock Problem (CSP) is an optimisation problem that roughly consists of cutting large objects in order to produce small items. The computational effort for solving this problem is largely affected by the number of cutting patterns. In this article, in order to cope with large instances of the One-Dimensional Cutting Stock Problem (1D-CSP), we resort to a pattern generating procedure and propose a strategy to restrict the number of patterns generated. Integer Linear Programming (ILP) models, an implementation of the Column Generation (CG) technique, and an application of the Generate-and-Solve (G&S) framework were used to obtain solutions for benchmark instances from the literature. The exact method was capable of solving small and medium sized instances of the problem. For large sized instances, the exact method was not applicable, while the effectiveness of the other methods depended on the characteristics of the instances. In general, the G&S method presented successful results, obtaining quasi-optimal solutions for the majority of the instances, by employing the strategy of artificially reducing the number of cutting patterns and by exploiting them in a heuristic framework.

Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa.

Computer Coded Verbal Autopsy (CCVA) algorithms are commonly used to determine the cause of death (CoD) from questionnaire responses extracted from verbal autopsies (VAs). However, they can only operate on structured data and cannot effectively harness information from unstructured VA narratives. Machine Learning (ML) algorithms have also been applied successfully in determining the CoD from VA narratives, allowing the use of auxiliary information that CCVA algorithms cannot directly utilize. However, most ML-based studies only use responses from the structured questionnaire, and the results lack generalisability and comparability across studies. We present a comparative performance evaluation of ML methods and CCVA algorithms on South African VA narratives data, using data from Agincourt Health and Demographic Surveillance Site (HDSS) with physicians' classifications as the gold standard. The data were collected from 1993 to 2015 and have 16,338 cases. The random forest and extreme gradient boosting classifiers outperformed the other classifiers on the combined dataset, attaining accuracy of 96% respectively, with significant statistical differences in algorithmic performance (p < 0.0001). All our models attained Area Under Receiver Operating Characteristics (AUROC) of greater than 0.884. The InterVA CCVA attained 83% Cause Specific Mortality Fraction accuracy and an Overall Chance-Corrected Concordance of 0.36. We demonstrate that ML models could accurately determine the cause of death from VA narratives. Additionally, through mortality trends and pattern analysis, we discovered that in the first decade of the civil registration system in South Africa, the average life expectancy was approximately 50 years. However, in the second decade, life expectancy significantly dropped, and the population was dying at a much younger average age of 40 years, mostly from the leading HIV related causes. Interestingly, in the third decade, we see a gradual improvement in life expectancy, possibly attributed to effective health intervention programmes. Through a structure and semantic analysis of narratives where experts disagree, we also demonstrate the most frequent terms of traditional healer consultations and visits. The comparative approach also makes this study a baseline that can be used for future research enforcing generalization and comparability. Future study will entail exploring deep learning models for CoD classification.

Computational performance evaluation of sacrificial protective walls composed of lightweight concrete blocks: a parametric study of blast loads in a tunnel

Sacrificial wall systems consisting of lightweight concrete blocks have several advantages including versatility and high installation speed, making them effective tools for reducing the destructive effects of blast waves. Particularly, in confined structures, they can be placed along the path of potential explosion waves such that internal failure mechanisms in the concrete blocks cause the explosion wave energy to be dissipated. Numerous factors can affect the performance of such wall systems, including the material properties, location, and physical dimensions of the concrete blocks. This research presents an in-depth computational study on parameters influencing the performance of such sacrificial wall systems.

Performance evaluation of a portable single-board computer as a 3-tiered LAMP stack under 32-bit and 64-bit Operating Systems

In this work, we propose a 3-tiered architecture running the Linux, Apache/Nginx, MariaDB and PHP (LAMP) stack on a 64-bit Operating System (OS) and a Solid-State Disk inside a Raspberry Pi (RPI) for performance evaluation. The relative response time and Application Performance Index (Apdex) for a 32-bit OS were measured and compared against an increasing load with Moodle as the application. Our choice of Moodle as a testbed is influenced by, amongst others, the 3-tiered LAMP architecture of MoodleBox as an image for the RPI, the relatively large database underlying Moodle (with more than 200 relational tables), the convenience offered by its sample tests courses and test plans in developing performance tests. Moodle can be easily substituted from the environment to create portable LAMP-based applications. The relative response time improved by 11.9 s for a medium-sized course with 100 users with the upgrades. The Apdex showed that the CPU of the RPI was the limiting factor that prevented the web application from scaling to beyond 40 users for medium-sized courses. This work's methodology, tests, and findings are important to administrators, educators, and users in general involved in capacity planning for the use of portable applications running under the LAMP stack. A ready for use image of the portable 64-bit LAMP stack is available for download. For a video summary of this paper, please visit https://rpi64box.com . • A novel portable 3-tiered LAMP stack proposed for a 64-bit OS for Raspberry Pi. • Improvement in response time for accommodation of more users under Apdex rating. • A ready for use image of the 64-bit portable LAMP stack is available for download. • An open-source method is proposed to carry out a performance evaluation of LAMP. • Insight is given to extension of the LAMP stack to portability of other apps.

PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool

IntroductionPodocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set.MethodsWhole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid–Schiff counterstain were acquired. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool.ResultsPodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome.ConclusionPodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.

Multibody system transfer matrix method: The past, the present, and the future

AbstractThe multibody system transfer matrix method (MSTMM), a novel dynamics approach developed during the past three decades, has several advantages compared to conventional dynamics methods. Some of these advantages include avoiding global dynamics equations with a system inertia matrix, utilizing low‐order matrices independent of system degree of freedom, high computational speed, and simplicity of computer implementation. MSTMM has been widely used in computer modeling, simulations, and performance evaluation of approximately 150 different complex mechanical systems. In this paper, the following aspects regarding MSTMM are reviewed: basic theory, algorithms, simulation and design software, and applications. Future research directions and generalization to more applications in various fields of science, technology, and engineering are discussed.

Performance analysis and evaluation of ternary optical computer based on asynchronous multiple vacations

Ternary optical computer (TOC) has become a research hotspot in the field because of the advantages such as inherent parallelism, numerous trits, low power consumption, extendibility, bitwise allocability and dynamical bitwise reconfigurability. Meanwhile, its performance evaluation attracts more and more attentions from potential users and researchers. To model its computing ecology more accurately, this paper first builds a three-staged TOC service model by introducing asynchronous multi-vacations and tandem queueing, and then proposes a task scheduling algorithm and an optical processor allocation algorithm with asynchronous vacations of some small optical processors after dividing equally the entire optical processor into several small optical processors which can be used independently. At the same time, the analytical model was established to obtain important performance indicators such as response time, the number of tasks and utilization of optical processor, based on M/M/1 and M/M/n queuing system with asynchronous multi-vacations. In addition, relevant numerical simulation experiments are conducted. The results illustrate that the number of small optical processors, vacation rate and the number of small optical processors allowed to be on vacation have important effects on the system performance. Compared with synchronous vacation, asynchronous vacation not only ensures the system to obtain better maintenance but also improves the system performance to some degree.

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

A new robust identification algorithm is introduced in this study for a Hammerstein-like system based on identification error information. With the help of the half-substitution idea, the identification model is converted into a compact model where the coupling parameters are avoided. To reduce the effect of noise signals, a filter gain is proposed to obtain helpful system data. Then, on the basis of the filtered variables and developed forcing variables, the identification error information is extracted from the helpful system data. By using the identification error data, a new parameter estimation adaptive law that differs from the classic prediction error method is derived. Therefore, a new identification scheme framework is proposed and the weakness of the prediction error method is improved. Simulations and a real-life plant are presented to test the validity and practicality of the presented identification approach. The results of parameter estimation and estimation error qualitatively demonstrated the advantages of the proposed algorithm. The computational complexity and performance evaluation indicators results of the developed algorithm quantitatively indicated that the proposed algorithm produces higher estimation performance compared with the other algorithms.

Towards sustainability evaluation of urban landscapes using big data: a case study of Israel’s architecture, engineering and construction industry

ABSTRACT Sustainability Rating Systems are standard methods for achieving sustainable development of buildings and urban landscapes. However, they suffer from low adoption and implementation rates, mainly due to labour-intensive evaluation processes. This study explores how recent advancements in big data, combined with the availability of new urban environment datasets, could advance sustainability rating systems in landscape development. We compared between existing computational technology (supply) and industry performance evaluation needs (demand) using a systematic review and survey of Israel’s professional communities as a case study. Of the existing indicators, Israeli professionals prioritised measuring socio-ecological indicators of landscapes in development projects, mainly at the urban level. Our review revealed that this level also holds available big data sustainability evaluation methods and technologies. Specifically, directed data for measuring ecology and volunteered and automated data for measuring social indicators. Such supply-demand links could significantly advance evaluation methods towards achieving a broader application of sustainable urban development.

An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment

An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment

Computer Performance Appraisal System Based on Balanced Scorecard under the Internet Background

In the new era of rapid development of Internet information technology and data processing technology, although the emergence of Internet technology has brought changes that cannot be underestimated in various fields of society, the computer performance evaluation system based on the Internet in the era of big data will definitely follow Technology trends in the new era. The balanced scorecard is one of the most commonly used financial performance appraisal tools in academia. The research purpose of this article is to study the innovation of enterprise computer performance appraisal management system in the current Internet era with the continuous development of big data technology. This paper first uses a neural network algorithm to build a model for it. Through this algorithm combined with a questionnaire survey method to screen the evaluation indicators of the computer performance evaluation system of Internet companies in the eastern coastal area, and finally build a BP model through the enterprise computer performance evaluation system. Model calculation samples, after calculation, the computer performance analysis of the target company can be obtained, and on the basis of summarizing the computer performance evaluation system and the balanced score card of the enterprise in the Internet era, the in-depth understanding and conclusion can be drawn. After studying the setting of performance appraisal system indicators, the setting of personal growth indicators is insufficient, the lack of effective cost control and the setting of computer performance indicators have not yet formed a complete system problem. This paper combines the actual situation of computer performance appraisal of Internet companies in the eastern coastal areas, applies the balanced scorecard to set up a management index system for the company’s computer performance appraisal, and uses the analytic hierarchy process to analyze the weights of the various indicators of the corporate computer performance appraisal balanced scorecard. Compare the evaluation results with the designated enterprise computer performance management evaluation standards. Experimental research results show that the balanced scorecard can effectively improve the efficiency of the computer performance appraisal system, and the computer performance appraisal can be clearer, and based on the Internet background and big data technology, the performance appraisal system can be made clearer.

Reliability-Aware Joint Optimization for Cooperative Vehicular Communication and Computing

This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server’s privacy perception of collaboration events.

Performance evaluation of adiabatic quantum computation via quantum speed limits and possible applications to many-body systems

The quantum speed limit specifies a universal bound of the fidelity between the initial state and the time-evolved state. We apply this method to find a bound of the fidelity between the adiabatic state and the time-evolved state. The bound is characterized by the counterdiabatic Hamiltonian and can be used to evaluate the worst case performance of the adiabatic quantum computation. The result is improved by imposing additional conditions and we examine several models to find a tight bound. We also derive a different type of quantum speed limits that is meaningful even when we take the thermodynamic limit. By using solvable spin models, we study how the performance and the bound are affected by phase transitions.

Design and tests of a non-contact Bernoulli gripper for rough-surfaced and fragile objects gripping

PurposeVaried shapes and sizes of different products with irregular rough surface and fragile properties give a challenge to traditional contact gripping. Single Bernoulli grippers are not suited to handle fragile objects as the impact of center negative pressure force could result in large deformation and stress which damage the materials, and they are also have some limitations for gripping objects with different large and small shapes. Thus, this paper aims to design a non-contact gripper for soft, rough-surfaced and fragile objects gripping with multi Bernoulli heads, which have optimal structures and parameters.Design/methodology/approachThe compressed air is ejected into four Bernoulli heads through radial and long flow channels, then passes through four strip-shaped narrow gaps after fully developing in the annular cavity to provide negative pressure. Based on the mathematic model and the computational model, the key structural parameters affecting the gripping performance are selected, and parameters optimization of the gripper is performed by computational fluid dynamics simulation analysis and performance evaluation. The orthogonal method is used and L16 orthogonal array is selected for experimental design and optimization. The characteristics of the designed gripper are tested from the aspects of pressure distribution and lifting force.FindingsFrom the applications in gripping different objects, the designed non-contact gripper can grip varied shapes and sizes of soft, rough-surfaced, fragile and sliced objects with little effect of torque.Originality/valueIn this paper, a non-contact gripper is designed for handling soft, rough-surfaced and fragile objects based on the Bernoulli principle. A systematic approach, which consists of modeling, simulation, optimization and measurement is provided for the non-contact gripper design and tests.

Performance Evaluation of Mini Single Board Computer in Hadoop Big Data Cluster

Hadoop is a Java-based and open source software framework that serves to process big size data in a distributed manner. Hadoop uses a framework for applications and programming called MapReduce. Along with the development of Hadoop, big data research using Hadoop is running using a single board computer (SBC). SBC is a mini computer which has a specification similar to a conventional computer but has lower specifications and power usage. However, the use of storage used on a single board computer has constraints, namely low bandwidth and limited local storage, therefore NAS is used because it has large storage and bandwidth speeds is upto1 Gbps. It uses as storage that is carefully used on Hadoop. Based on testing by running Hadoop in a single node and multi nodes using Micro SD storage and NAS does have a significant difference. When running Hadoop on a single node the difference in data access speed generated by Micro SD and NAS has a difference of 2 seconds. On multi nodes (clusters) the ratio is 4 seconds. The difference in NAS bandwidth that is 2 times faster than Micro SD show a significant difference if it is run on Hadoop.