System Performance Metrics Research Articles

Mathematical analysis and flux-based radius of influence for radon/VOC vapor intrusion mitigation systems

Volatile organic compounds (VOCs) and radon progeny pose potential health risks to occupants of certain buildings via subsurface vapor intrusion (VI) to indoor air. VI mitigation is usually performed using systems that extract gas from below the building, and the system performance is typically evaluated by measuring the distribution of applied vacuum below the floor. This article provides a new approach to assessing the radius of influence (ROI) for subslab venting systems based on mass flux instead of static vacuum distribution and includes an analyses of 121 pneumatic tests performed at 65 different suction points in 16 different buildings. The mathematical model represents a two-layer system with horizontal radial flow through transmissive material below the floor slab and vertical flow through discontinuities in the floor slab (which is simplified to approximate an equivalent porous medium). The analysis includes comparisons of the flux-based ROI to values calculated using the two-layer model for 1) vacuum, 2) velocity, and 3) travel time, which may be useful as alternative performance metrics for mitigation systems.

Requirements in performance measurement systems of construction projects from the lean production perspective

Performance measurement (PM) generates useful data for process control, facilitates communication between different sectors, and helps to align efforts on the most important aspects of the business. Thus, PM plays a key role in the management of projects and organizations. PM is also important in the implementation of lean production principles and methods, such as reducing the share of nonvalue-adding activities, increasing process transparency, building continuous improvement into the process, and benchmarking. Moreover, the adoption of the lean production philosophy requires changes in PM. Despite its importance, limited studies have been conducted on the use of PM systems for assessing the impact of lean production programs in construction projects. In addition, studies on how lean companies (or projects) use performance measurement and to what extent the indicators adopted reflect the result of actions that have been undertaken are limited. This study proposes a set of requirements in PM systems of construction projects from the perspective of lean production and a taxonomy of performance metrics for lean production systems. Five empirical studies have been carried out on construction companies from South America involved in the implementation of lean production systems. The scope of this investigation is limited to the construction projects as production systems rather than PM at the level of construction organizations.

Count Models to Represent the Impacts of Weather and Infrastructure on Flight Disruptions

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

DC Component Recovery in Kramers-Kronig Receiver Utilizing AC-Coupled Photo-Detector

The Kramers-Kronig (KK) receiver has recently drawn a great deal of attention due to its capability to extract the complex-valued electric field of optical signal from the directly detected signal. One practical issue is the recovery of the DC component lost upon the reception using an AC-coupled photo-detector. Due to the nonlinear operation of KK algorithm, it is necessary to recover the DC value of the signal before executing the algorithm. A common method to estimate the DC component was to sweep the DC value added to the detected signal, while measuring the system performance metrics such as bit-error ratio (BER). In this article, we propose and demonstrate a simple one-step DC recovery method for KK receiver. By inserting a zero-padded preamble in the modulation data and measuring a couple of statistical averages of the detected AC-coupled signal, we can estimate the DC component accurately even when the carrier-to-signal power ratio (CSPR) is low. Thus, this method estimates the DC component rapidly without time-consuming and power-hungry signal processing such as demodulation and BER calculation. We carry out a series of computer simulations and experiments to measure the accuracy of the proposed method. The results show that the proposed method incurs negligible sensitivity penalties compared with the conventional DC-sweep method. Also, provided in this paper is the design guideline on the proposed method.

Premature Mortality in Canada

Introduction: Premature mortality (deaths before age 75) is a well-established metric of population health and health system performance. In Canada, underlying differences between provinces/territories present a need for stratified mortality trends.
 Methods: Using data from the Canadian Vital Statistics Database, a descriptive analysis of sex-specific adult premature deaths over 1992-2015 was conducted by province, census divisions (CD), socioeconomic status (SES), age, and underlying cause of death. Premature mortality rates were calculated as the number of deaths per 100,000 individuals aged 18 to 74, per 8-year era. SES was measured using the income quintile of the neighbourhood of residence. Absolute and relative inequalities were respectively summarized using slope and relative indices of inequality, produced via unadjusted linear regression of the mortality rate on income rank.
 Results: Premature mortality in Canada declined by 21% for males and 13% for females between 1992-1999 and 2008-2015. The greatest reductions were in Central Canada, while Newfoundland saw notable increases. CD-level improvements appeared mostly in the southern half of Canada. As of 2008-2015, Newfoundland, Nova Scotia, and Nunavut had the highest mortality rates. Low area-level income was associated with higher mortality. SES inequalities grew over time. Newfoundland’s between-quintile differences rose from 1292 to 2389 deaths per 100k males, or 1.33 to 2.12-fold, and 586 to 1586 per 100k females, or 1.24 to 1.74-fold. In 2008-2015, mortality rates of the bottom quintile in Manitoba and Saskatchewan were more than 2.5 times those of the top. Mortality increased with age, and varied regionally. Low mortality in Central Canada and BC, and high mortality in the Territories were consistent across eras and sexes. Cause of death distributions shifted with age and sex, with more external deaths in younger males.
 Conclusion: Improvements were seen in adult premature mortality rates over time, but were unequal across geographies. Evidence exists for growing socioeconomic disparities in mortality.

Transmission of Classical Information Over Noisy Quantum Channels–A Spectrum Approach

Based upon the Fluctuation-Dissipation Theorem of Nyquist-Callen-Welton and the spectrum balancing relationship of Kubo-Martin-Schwinger for operator-valued stochastic noise processes, a two-parameter Quantum Noise-Energy Spectral Density (QN-ESD) is constructed and Fourier-Laplace transformed to obtain its associated Quantum Noise-Autocorrelation Function (QN-ACF). By means of a Taylor series expansion, the quantum thermal noise correlations are linked to Number Theory's Reimann-Zeta function. From this perspective, a panoply of spectral components and their QN correlations are characterized for three disjoint regions located across the Electromagnetic Spectrum (EMS). The second-moment characterizations and frequency boundaries for each region are specified as a function of two frequency dependent design parameters, viz., a normalized temperature dependent frequency and a quantum receiver cutoff frequency. These boundaries define where the quadrature noise correlation projections of the QN-ESD become noticeably asymmetric-in-frequency. For the operator-valued white quantum noise region, various communication system performance metrics are characterized and their limits are presented and compared via interconnecting parameter relations to their classical communications (CC) counterparts. These metrics include Shannon's non-additive, but regularized, zero-error capacity in bits/photon-pair, bits/photon, bits/channel-use and bits/sec, the spectral efficiency in bits/sec/Hz and the error-correcting code rate. For long coded messages, Shannon's capacity quadrature approaches 2π√5/ ln(2) ≈ 20 bits/photon-pair or (10 bits/photon) while for single-photon per channel use transmission it approaches 3 bits/photon-pair. Results are graphically illustrated for the quantum noise spectral density, quantum noise correlations, Shannon's Î-Q̂ capacities and spectral efficiency.

Impact of Channel Correlation on Different Performance Metrics of OSSK-Based FSO Systems

In this paper, we study the impact of correlation on the bit error rate (BER) and the channel capacity of a free-space optical (FSO) multiple-input-multiple-output (MIMO) system employing optical space shift keying (OSSK) over a fading channel. In order to study a practical correlated channel, we consider the effect of channel correlation due to both small-and large-scale eddies and show that the use of OSSK over correlated FSO channel can lead to an improved system performance with increasing correlation level of upto 0.9. In this work, we first develop an analytical framework for different performance metrics of the OSSK multiple-input single-output system with correlation and then extend our investigation by proposing an asymptotically accurate mathematical framework for MIMO. We also validate all the analytical results using MATLAB simulations. Finally, we develop an experimental setup of FSO with two correlated links to study the throughput and latency of the links at different turbulence levels.

Model-Based Safety Analysis for the Fly-by-Wire System by Using Monte Carlo Simulation

Safety analysis is one of the important means to show compliance with airworthiness requirements. The traditional safety analysis methods are significantly dependent on analysts’ skills and experiences. A model-based safety analysis approach is proposed for typical fly-by-wire (FBW) systems based on the system development model built via Simulink, by which the response of system performances can be simulated. The safety requirements of the FBW system are defined by presenting the thresholds of system performance metrics, and the effects of failure conditions on aircraft safety are determined according to the system response simulation by injecting failures or failure combinations into the Simulink model. The Monte Carlo simulation method is used to calculate the probability of unsafe conditions, whose effects are determined by the system response simulation with fault injections. Finally, a case study is used to illustrate the effectiveness and advantages of our proposed approach.

Sensing for aerospace combustor health monitoring

Purpose This paper proposes new methods of fault detection for fuel systems in order to improve system availability. Novel fault systems are required for environmentally friendly lean burn combustion, but can carry high risk failure modes particularly through their control valves. The purpose of the developed technology is the rapid detection of these failure modes, such as valve sticking or impending sticking, and therefore to reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which results in a low reliability for conventional position sensing. Design/methodology/approach Starting with the business needs elicited from stakeholders, a quality functional deployment process is performed to derive sensing system requirements. The process acknowledges the difference between test-bed and in-service aerospace needs through weightings on requirements and maps these customer requirements to systems performance metrics. The design of the system must therefore optimise the sensor suite, on- and off-board signal processing and acquisition strategy. Findings Against this systems engineering process, two sensing strategies are outlined which illustrate the span of solutions, from conventional gas path sensing with advanced signal processing to novel non-invasive sensing concepts. While conventional sensing may be feasible within a test cell, the constraints of aerospace in-service operation may necessitate more novel alternatives. Acoustic emission (detecting very high frequency surface vibration waves) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution. Through this comparison, the considerations for the end-to-end system design are highlighted to be critical to sensor deployment success in-service. Practical implications The paper provides insight into different means of addressing the important problem of monitoring faults in combustor systems in gas turbines. By casting of the complex design problem within a systems engineering framework, the outline of a toolset for solution evaluation is provided. Originality/value The paper provides three areas of significant contributions: a diversity of methods to diagnosing fuel system malfunctions by measuring changes fuel flow distributions, through novel means, and the combustor exit temperature profiles (cause and effect); the use of analytical methods to support the selection (types and quantities) and placement of sensors to ensure adequate state awareness while minimising their impact on the engine system cost and weight; and an end-to-end data processing approach to provide optimised information for the engine maintainers allowing informed decision-making.

Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition

We investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua's memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the double-diode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNIST-like databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. We show that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities.

Multigroup Multicast Precoding for Energy Optimization in SWIPT Systems With Heterogeneous Users

The key to developing future generations of wireless communication systems lies in the expansion of extant methodologies, which ensures the coexistence of a variety of devices within a system. In this paper, we assume several multicasting (MC) groups comprising three types of heterogeneous users including Information Decoding (ID), Energy Harvesting (EH) and both ID and EH. We present a novel framework to investigate the multi-group (MG) - MC precoder designs for three different scenarios, namely, Separate Multicast and Energy Precoding Design (SMEP), Joint Multicast and Energy Precoding Design (JMEP), and Per-User Information and/or Energy Precoding Design (PIEP). In the considered system, a multi-antenna source transmits the relevant information and/or energy to the groups of corresponding receivers using more than one MC streams. The data processing users employ the conventional ID receiver architectures, the EH users make use of a non-linear EH module for energy acquisition, while the users capable of performing both ID and EH utilize the separated architecture with disparate ID and non-linear EH units. Our contribution is threefold. Firstly, we propose an optimization framework to i) minimize the total transmit power and ii) to maximize the sum harvested energy, the two key performance metrics of MG-MC systems. The proposed framework allows the analysis of the system under arbitrary given quality of service and harvested energy requirements. Secondly, to deal with the non-convexity of the formulated problems, we transform the original problems respectively into equivalent forms, which can be effectively solved by semi-definite relaxation (SDR) and alternating optimization. The convergence of the proposed algorithms is analytically guaranteed. Thirdly, a comparative study between the proposed schemes is conducted via extensive numerical results, wherein the benefits of adopting SMEP over JMEP and PIEP models are discussed.

Taxi Event Extraction from Surveillance for Surface Performance Evaluation

Estimated unimpeded taxi times can be used to quantify a flight’s taxiing delay when compared with its actual taxi time. Currently, flight unimpeded taxi times are calculated by the Federal Aviation Administration using a regression method where flights in the Aviation System Performance Metrics data are clustered on the season of operation, airline, airport, and calendar year. The method uses the airline-reported gate-Out, wheels-Off, wheels-On, gate-In (OOOI) times rounded to the nearest minute. For nonreporting airlines, these times are estimated from similar flights. The purpose of this paper is to evaluate the unimpeded time using a surveillance-based approach by identifying the time a flight spent waiting in the system and comparing with the total taxi time. This study focuses on analyzing both arrivals and departures for six U.S. airports (Atlanta, Charlotte, Denver, Houston, New York Kennedy, and Chicago O'Hare) during the month of July 2015. The study results show how the non-surveillance-based and surveillance-based metrics compare, with nonsurveillance methods including the current regression method and the 5th-to-15th clustering method described in current literature. The benefits of using surveillance information for surface performance are explored, as spatial analyses allow for quick identification of taxiway locations that were the most susceptible to delays.

AMIR

Service demand burstiness, or serial correlations in resource service demands, has previously been shown to have an adverse impact on system performance metrics such as response time. This article proposes AMIR , an analytic framework to characterize burstiness and identify strategies to reduce its impact on performance. AMIR considers an overtake-free system model consisting of multiple queues that service multiple classes of sessions, i.e., sequences of requests. Given the per-class service demand distributions and number of sessions belonging to each class, AMIR can identify an ordering of sessions, i.e., a schedule, that minimizes burstiness at the bottleneck. Hence, it is likely to improve system responsiveness metrics, including mean session wait time and total schedule processing time. To characterize burstiness, the technique uses an order O schedule burstiness metric β O representing the mean probability that O + 1 consecutive sessions in the schedule have resource demands at the bottleneck greater than the mean bottleneck demand of the schedule. For a given O , AMIR uses Integer Linear Programming (ILP) to identify schedules that progressively minimize β i ∀ i ∈ {1, … O }. We conduct an extensive simulation study to provide insights on the conditions under which such schedules can improve system responsiveness. These results show that schedules derived from AMIR can significantly outperform those derived from baseline policies such as Shortest Job First (SJF) and random scheduling when session classes are dissimilar from one another in terms of their service demand distributions. Furthermore, minimizing for higher orders of schedule burstiness is most critical when the bottleneck is heavily utilized and when the service demands of a workload are highly variable. For the system model that we consider, we are not aware of other techniques that are designed to analytically derive insights on the performance impact of high-order service demand burstiness.

Robust spectrum access for hybrid interweave‐underlay cognitive radio systems using probabilistic spectrum access

The hybrid cognitive radio (CR) system which combines the two classical interweave and underlay approaches has an improved spectrum efficiency compared to conventional CR systems. In this study, the authors apply the joint spectrum access and power control in a hybrid CR system in the presence of threshold estimation errors. They first formulate the performance metrics of the hybrid CR system under the optimal joint spectrum access and power control strategies by taking into account the threshold estimation errors. Then they show that the threshold estimation error decreases the probability of detecting the primary user (PU) while it increases the false-alarm probability. Next, they show that the threshold estimation error degrades the performance of the hybrid CR system in terms of outage probability, average achievable data rate and the average interference introduced to the PU. To overcome these performance degradations, they propose a modified spectrum access formulation based on the probabilistic spectrum access scheme, which reduces the impact of threshold estimation uncertainty on the performance of the hybrid CR system. Simulation results indicate that the proposed spectrum access can compensate the performance degradation of a CR system up to 65% for a detection probability of 0.7.

Image Classification Benchmark (ICB)

During any investigative work, it is necessary to confront our solutions to the already existing ones. This requires much work, namely to try to recreate the competing solutions and to use the same experimental conditions in order to obtain an objective comparison. Benchmarking is an activity of comparing performance metrics of systems in order to rank them or the activity of comparing a specific system to state-of-the-art alternatives. It would be convenient to have an image benchmarking ecosystem, not only to evaluate a personal solution but also to compare it with other previously proposed solutions. Upon designing a new or improved image classification pipeline, a convolution neural network or a specific algorithm modifying some detail, a user can add it to the benchmarking ecosystem and get a report on the performance of the solution, which can be compared to other solutions that were previously benchmarked under the same conditions. In this paper we describe the ICB - “Image Classification Benchmark” -, a prototype of a benchmarking ecosystem created to enable this vision. Besides describing how it works and how it was made flexible to incorporate any algorithm, we apply it to a specific comparison, as a proof-of-concept.

Multiuser Computation Offloading and Downloading for Edge Computing With Virtualization

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of the mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, offloading scheduling algorithms is designed targeting two system performance metrics: sum offloading rate maximization and sum mobile energy consumption minimization. Their designs are formulated as non-convex mixed-integer programming problems, which account for latency due to offloading, result downloading, and parallel computing. A set of low-complexity algorithms are designed based on a decomposition approach and leveraging classic techniques from combinatorial optimization. The resultant algorithms jointly schedule offloading users, control their offloading sizes, and divide time for communication (offloading and downloading) and computation. They are either optimal or can achieve close-to-optimality as shown by simulation. The comprehensive simulation results demonstrate that considering of I/O interference can endow on an offloading controller robustness against the performance-degradation factor.

A study on practical objectives and constraints for heat conduction topology optimization

This paper presents some practical formulations for heat conduction topology optimization problems. In post-optimization analysis, temperature metrics are often used to compare the performance of optimized structures, yet are not used generally as optimization objectives. In this article, SIMP-based topology optimization is used to explore several objective functions related to electronics applications to demonstrate clearly the impact of improper objective selection. Performance variations over 100% were observed when comparing key metrics between optimized structures. Findings here are extended to problems in electronics domains, where temperature optimization may be used in unconventional ways to capture more realistic design considerations. This includes an investigation in the combinatorial use of objectives and constraints to satisfy electronics requirements. Four case studies are presented where topology optimization methods are used to maximize system performance metrics while satisfying temperature constraints.

Performance of low-enthalpy geothermal systems: Interplay of spatially correlated heterogeneity and well-doublet spacings

The low-enthalpy geothermal systems are commonly deployed in sedimentary geological settings that feature significant levels of deposition-induced heterogeneity. In this paper, realistic levels of heterogeneity in the form of varying porosity variance and spatial correlation lengths are considered for a 3D geothermal system. Using 2600 computationally intensive numerical simulations of two doublets placed in a checkboard pattern, the influence of well and doublet spacings on performance metrics of low-enthalpy geothermal systems are investigated. The simulations strongly support that in varyingly heterogeneous systems, the lifetimes of operation are shorter, and depending on isotropicity or anisotropicity of correlated heterogeneity, the lifetimes vary. Most notably the anisotropically correlated heterogeneity can lead to either positive impact (by diverting the cold water plume) or negative impact (by facilitating an early breakthrough of cold water plume) on the lifetime of the operation compared to isotropically correlated heterogeneity. We also calculate the boundary of the region around the wells designated as the “license area” (where the cold water front reaches to or where a threshold temperature drop of 1 °C occurs). By doing so, it is found that the operator can assume larger extents (of up to 50%) for the license areas of the aquifer than the ones conventionally assumed. To minimize the impact of heterogeneity on operation, the best practice was found to place the doublets in the same spacings as of the wells. Moreover, it is found that the well distance can be significantly shorter than what is commonly realised for heterogeneous geothermal aquifers.

Cost minimization of real-time mission for software systems with rejuvenation

This paper considers the optimal rejuvenation policy problem for software systems performing real-time tasks. Due to software aging, the system performance deteriorates with time eventually leading to the system crash, which can be catastrophic for critical applications. To prevent the system crash or minimize its occurrence probability, software rejuvenation has been widely adopted for counteracting the software aging effect but at the cost of extra system overhead and downtime. We derive the optimal state-based rejuvenation policy minimizing the total expected mission cost for software aging systems subject to multiple performance degradation states. The solution encompasses an event transition-based numerical method that assesses the total expected mission cost of a real-time task, covering penalty cost from the mission failure, operation cost of running the mission software and the expected rejuvenation cost. The proposed cost evaluation model can accommodate arbitrary types of state transition time distributions. The suggested model also allows simultaneous evaluations of other system performance metrics including the probability of the successful task completion (i.e., mission reliability) and conditional expected mission completion time given a successful mission task. Examples are presented to demonstrate the proposed methodology and optimizations. Effects of different parameters on the rejuvenation optimization solution are also investigated.

Adaptive beamforming and user association in heterogeneous cloud radio access networks: A mobility-aware performance-cost trade-off

Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios.11This is an extended version of the conference paper presented at IEEE PIMRC 2017 [1].