Operational Performance Requirements Research Articles

Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study.

The aim of this study was to independently evaluate the diagnostic accuracy of three artificial intelligence (AI)-based computer aided detection (CAD) systems for detecting pulmonary tuberculosis (TB) on global migrants screening chest x-ray (CXR) cases when compared against both microbiological and radiological reference standards (MRS and RadRS, respectively). Retrospective clinical data and CXR images were collected from the International Organization for Migration (IOM) pre-migration health assessment TB screening global database for US-bound migrants. A total of 2,812 participants were included in the dataset used for analysis against RadRS, of which 1,769 (62.9%) had accompanying microbiological test results and were included against MRS. All CXRs were interpreted by three CAD systems (CAD4TB v6, Lunit INSIGHT v4.9.0, and qXR v2) in offline setting, and re-interpreted by two expert radiologists in a blinded fashion. The performance was evaluated using receiver operating characteristics curve (ROC), estimates of sensitivity and specificity at different CAD thresholds against both microbiological and radiological reference standards (MRS and RadRS, respectively), and was compared with that of the expert radiologists. The area under the curve against MRS was highest for Lunit (0.85; 95% CI 0.83-0.87), followed by qXR (0.75; 95% CI 0.72-0.77) and then CAD4TB (0.71; 95% CI 0.68-0.73). At a set specificity of 70%, Lunit had the highest sensitivity (81.4%; 95% CI 77.9-84.6); at a set sensitivity of 90%, specificity was also highest for Lunit (54.5%; 95% CI 51.7-57.3). The CAD systems performed comparable to the sensitivity (98.3%), and except CAD4TB, to specificity (13.7%) of the expert radiologists. Similar trends were observed when using RadRS. Area under the curve against RadRS was highest for CAD4TB (0.87; 95% CI 0.86-0.89) and Lunit (0.87; 95% CI 0.85-0.88) followed by qXR (0.81; 95% CI 0.80-0.83). At a set specificity of 70%, CAD4TB had highest sensitivity (84.1%; 95% CI 82.3-85.8) followed by Lunit (80.9%; 95% CI 78.9-82.7); and at a set sensitivity of 90%, specificity was also highest for CAD4TB (54.6%; 95% CI 51.3-57.8). In conclusion, the study demonstrated that the three CAD systems had broadly similar diagnostic accuracy with regard to TB screening and comparable accuracy to an expert radiologist against MRS. Compared with different reference standards, Lunit performed better than both qXR and CAD4TB against MRS, and CAD4TB and Lunit better than qXR against RadRS. Moreover, the performance of the CADs can be impacted by characteristics of subgroup of population. The main limitation was that our study relied on retrospective data and MRS was not routinely done in individuals with a low suspicion of TB and a normal CXR. Our findings suggest that CAD systems could be a useful tool for TB screening programs in remote, high TB prevalent places where access to expert radiologists may be limited. However, further large-scale prospective studies are needed to address outstanding questions around the operational performance and technical requirements of the CAD systems.

Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning

The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements.

DEM Simulation and Experimental Validation of the Performance of an Orchard Deep Applicator for Manure

Green, healthy and sustainable development is the development direction of China’s agriculture, and stable fertilizer has become the first choice for orchard fertilizer. In this paper, in order to improve the working performance of the combined fertilizer application device of 2KF-30 type orchard stable fertilizer deep applicator, the matching degree of the combined operation of scraper and auger was taken as the research objective, and theoretical analysis, numerical simulation, and experimental verification were combined to carry out the research. Applying fuzzy theory, the comprehensive qualified rate weighted by the uniformity of fertilization and the qualified rate of fertilization was proposed as the evaluation index. The scraper shaft speed, scraper space, spiral conveyor speed, and spiral conveyor pitch were selected as the test factors, and the comprehensive qualified rate of fertilization for the combined fertilization device was taken as the evaluation index. The test design was carried out by single-factor and response surface method tests, and numerical simulations were conducted by discrete element software to analyze the optimal combination of fertilizer application parameters. The test results were analyzed based on the numerical simulation of discrete element software. The testbed was set up to verify the above optimal results. The results showed that the maximum error between the physical test and the numerical simulation was 8.67%, the minimum error was 1.75% and the overall mean error between the physical test value of the combined fertilizer application pass rate and the predicted value of the quadratic regression equation was 5.99%, which can meet the operational performance requirements of the fertilization device. The research results can provide a theoretical basis for the design and improvement of the deep fertilizing machine for orchard manure.

A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework

With the increase in system complexity and operational performance requirements, nuclear energy systems are developing in the direction of intelligence and unmanned, which also requires a higher demand for its safety so that intelligent fault diagnosis and prediction have become a technology that nuclear power plants need to develop at present. At the same time, due to the rapid development of deep learning technology, it has become a meaningful development direction to predict the fault state of nuclear power plants within the framework of supervised deep learning. Usually, the network structure model used in fault diagnosis and prediction requires professional design, which may cost a lot of time and make it difficult to achieve optimal results. For this purpose, we present an end-to-end deep network for nuclear power system prediction (EDN-NPSP), which can automatically mine the transient features of various detection data in the NPS at the current moment through heterogeneous convolution kernels that can increase the receptive field and then predict the feature evolution results of the NPS in the future through a special deep CNN. The results provide an assessment of the future state of NPS. Based on EDN-NPSP presented in this work, we can avoid complicated manual feature extraction and provide the predicted state directly and rapidly. It will provide operators with useful prediction information and enhance the nuclear energy system fault prediction capabilities.

Maritime search and rescue in canada and the use of emergency radio beacons

Successful and early distress communication at sea is critically important for improving the survivability of marine accidents. A wide range of communication methods is available for seafarers to make distress calls. In particular, emergency radio beacons (i.e. emergency position indicating radio beacons (EPIRBs) and personal locator beacons (PLBs)) provide a reliable, commercially available means by which distress notification and incident location can be provided, particularly if time is not available to establish communication by radio or telephone. Given the importance of these devices, the International Maritime Organization (IMO) provides guidance for flag states to incorporate radio distress beacons within legal and search and rescue (SAR) frameworks. National regulations in Canada stipulate additional performance requirements for radio frequency transmission, operation and use. The governance structure for emergency radio beacons in Canada is comprehensive and complex, and worldwide accident statistics show that when EPIRBs and PLBs are used, the rescue rate increases. Still, in Canada, a significant proportion of maritime activities (e.g., fish harvesting) do not require the use of these devices, and it is in these sectors where the greatest number of fatalities is occurring. Our paper explores the governance structure for the development and use of EPIRBs and PLBs in Canada, identifying where challenges and gaps exist and considering ways to close these gaps. Discussion is provided on the flexibility and adaptability of the governance system to incorporating new information and changes in technology.

Product Interfaces for Older Adult Users With Cognitive Impairment

The aim of this study is to explore the usability of multiple forms of television remote control interfaces for older adult users and users with mild cognitive impairment (MCI) to clarify the operation performance requirements of these two user groups. The test interfaces were constructed through programming and were capable of recording participants’ task completion time, operation errors, and the locations of their taps. The results show that users with MCI could not perform their initial action quickly and accurately; buttons with larger sizes provided greater operation performance for older adult users and users with MCI.

Three-Dimensional Particle-in-Cell Analysis of Mechanical Design Tolerances of the Scorpius Injector With Warp

This article examines the mechanical design tolerances for the Scorpius injector using a 3-D particle-in-cell (PIC) code. In order to meet its objective as a high-fidelity, multipulse flash X-ray source, the accelerator has requirements for the electron beam in order to minimize the spot size of the beam focused onto a foil target to create high fluence X-rays at energies greater than 10 MeV. The requirements for alignment of the cathode, anode beam pipe, focusing solenoids, and transport solenoids specified using Trak and AMBER are independently verified using a 3-D electrostatic/magnetostatic model of the Scorpius injector, including transverse magnetic field errors that arise from mechanical alignment errors. It is predicted that the injector will meet the performance requirements for radiographic operation and has a design margin to correct for potentially unforeseen operating conditions.

Comparison between Performance Requirements for Certification and Performance Requirements for Operation of Transport Category Rotorcraft

In order to achieve aviation safety, integrity of two pillars is essential: product and operations. Regarding product integrity of transport category rotorcraft, its requirements are established in USA by the Federal Aviation Administration (FAA) Part 29, as Categories A and B. Regarding rotorcraft operations, the International Civil Aviation Organization (ICAO) has established Performance Classes 1, 2 and 3 in its Annex 6 Part III, determining they shall occur under a Code of Performance established by the State of the Operator. Should operations neglect certification requirements, particularly certification performance operating limitations, the intended protection (i.e., aviation safety) remains, therefore, unassured. This is possibly the case with offshore operations, pushed by a supposed commercial viability (increased payload) for oil and gas industry. Aiming to clarify it, the authors critically peruse through an exploratory and qualitative research of historical and bibliographic documentation of both rotorcraft certification and operation issued by ICAO and FAA and, in doing so, enhance understanding of the overlaps and complementarity (or otherwise) of certification and operational performance requirements for transport category rotorcraft.

Automated operational logging plan considering multi-criteria optimization

Multiple goals should be considered while planning and executing forest management. This study proposes a new multidimensional framework for a precision forestry approach assisted by airborne laser scanning data (ALS). Therefore, successful management operations become a key element in the process of promoting protection through sustainable development. Thirteen relevant variables were derived from ALS data, such as: canopy height, terrain topography, relative vegetation density, forest gaps, slope restrictions, skidder restrictions, load truck restrictions, topographic wetness, flow accumulation, horizontal distance to drainage, vertical elevation from drainage, stream and headspring restrictions. Four different scenarios for the management plan optimization were studied: shortest distance, forest conservation, soil conservation and all combined. Results showed that the detailed forest information from ALS point clouds is useful to indicate regions not suitable for forest operations. Failure to properly consider the different factors involved may result in inadequate infrastructures, lower operational performance and constant re-planning requirements.

Influence of sediment on the hydrological performance of a permeable pavement

Permeable pavements play an essential role in urban drainage systems, making them a subject of great interest to both researchers and practitioners. The majority of studies, however, have demonstrated a significant degree of uncertainty regarding both the operational performance and maintenance requirements of this type of pavement. This paper describes a laboratory-based experimental study investigating the influence of sediment on the hydrological performance of a permeable pavement. The experimental results show that, under sediment and rainfall loading typical for a 10 year period within the UK, partial clogging of the pavement voids with sediment led to a 6·4% decrease in total outflow, a 6·41% decrease in outflow rate, a 9·5% increase in outflow start time, a 20·7% increase in total outflow duration and no significant change in the concentration of suspended solids. However, no surface ponding was observed and it was therefore concluded that an appropriately designed permeable pavement system, exposed to typical UK rainfall and sediment loadings, should be able to operate efficiently for at least 10 years without the need for any post-construction maintenance. Hence, permeable pavements continue to represent an excellent form of source control for both surface runoff and pollutants.

Evaluation of the Enhanced LEU Fuel (ELF) Design for Conversion of the Advanced Test Reactor to a Low-Enrichment Fuel Cycle

A conceptual low-enrichment uranium (LEU) fuel design has been developed for the Advanced Test Reactor (ATR) at Idaho National Laboratory. The ATR is currently fueled with a high-enrichment fuel but is slated to be converted to LEU under programs led by the National Nuclear Security Administration of the U.S. Department of Energy. A conceptual LEU fuel design, the Enhanced LEU Fuel (ELF), has been developed assuming power peaking control through the use of variable fuel meat thicknesses and no use of burnable poison. In initial work, this design was shown to satisfy performance requirements for ATR operation. Following these design calculations, a safety analysis process was initiated to demonstrate that the ELF design would successfully meet safety limits for postulated accident conditions. Those calculations, performed using RELAP5 and ATR-SINDA, require physics analysis to provide spatial power distributions and kinetics parameters for various core operations configurations. This article describes the findings of the physics analysis and provides predictions for the behavior of a LEU-fueled version of ATR, and compares these to calculations of the performance of the current high-enrichment uranium fuel.

INS/GPS Integrated Smoothing Algorithm for Synthetic Aperture Radar Motion Compensation Using an Extended Kalman Filter with a Position Damping Loop

In this study, we propose a real time inertial navigation system/global positioning system (INS/GPS) integrated smoothing algorithm based on an extended Kalman filter (EKF) and a position damping loop (PDL) for synthetic aperture radar (SAR). Integrated navigation algorithms usually induce discontinuities due to error correction update by the Kalman filter, which are as detrimental to the performance of SAR as the relative position error. The proposed smoothing algorithm suppresses these discontinuities and also reduces the relative position error in real time. An EKF estimates the navigation errors and sensor biases, and all the errors except for the position error are corrected directly and instantly. A PDL activated during SAR operation period imposes damping effects on the position error estimates, where the estimated position error is corrected smoothly and gradually, which contributes to the real time smoothing and small relative position errors. The residual errors are re-estimated by the EKF to maintain the estimation performance and the stability of the overall loop. The performance improvements were confirmed by Monte Carlo simulations. The simulation results showed that the discontinuities were reduced by 99.8% and the relative position error by 48% compared with a conventional EKF without a smoothing loop, thereby satisfying the basic performance requirements for SAR operation. The proposed algorithm may be applicable to low cost SAR systems which use a conventional INS/GPS without changing their hardware configurations.

Staggered-PRT Sequences for Doppler Weather Radars. Part II: Ground Clutter Mitigation on the NEXRAD Network Using the CLEAN-AP Filter

AbstractThe staggered–pulse repetition time (SPRT) technique has been shown to be effective at mitigating range and velocity ambiguities; however, mitigation of ground clutter contamination for SPRT sequences has proven to be more challenging. Using the properties of the autocorrelation spectral density, the Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter is extended to SPRT sequences for its implementation on the U.S. Next Generation Weather Radar (NEXRAD) network. The performance of the CLEAN-AP filter for SPRT sequences is characterized and illustrated with simulations and real data. The study shows that the proposed ground clutter filter meets NEXRAD operational performance requirements for ground clutter mitigation.

Sustaining supply chains

This paper considers the importance of, and conditions for, a vibrant supply chain within the UK rail infrastructure sector. It explores issues that underpin supply chain innovation, sustainable growth within the supplier base and the benefits that the nation and informed clients gain through adoption of supply chain sustainability. Procurement practices have changed in recent years, and, particularly during the post-2012 Olympics period, there is a greater awareness now of the conditions required to deliver national economic infrastructure and the commercial conditions that promote efficient delivery and enable supply chain innovation. This paper includes some findings in 2015 of the initiative termed the ‘Sustainable Supply Chain’, steered by Robert Mallett (Royal Institution of Chartered Surveyors), Chris Williams-Lilly (Rail Champions) and Richard Graham (CH2M). This work highlights the need for greater client focus on infrastructure operational requirements and outcomes, for the development of operational asset performance and functional requirements for the underlying projects that then inform the supply chain and its engagement in effective delivery of those outcomes. Within this early definition lies the client establishment of effective vertical supply chain relationships, their promotion of small to medium-sized enterprise investment and capabilities to achieve the clients’ defined end-state requirements.

OPTIMIZING BUILDING UPGRADES TO MINIMIZE ENERGY AND WATER CONSUMPTION

Building sector in the United States is responsible for 41% of energy consumption, 73% of electricity consumption, and 14% of water consumption. Energy and water consumption of buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets especially in aging buildings which represent 70% of existing buildings in the United States. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy and water consumption of existing buildings while complying with limited upgrade budgets and building operational performance requirements. This optimization model is designed to estimate building energy consumption using energy simulation software packages, is integrated with databases of building products, and performs analysis of replacing existing building fixtures/equipment and installing renewable energy systems during optimization computations to identify the replacement of building products that minimizes energy and water consumption. The model is designed to provide detailed results for building owners, which include specifications for the recommended upgrade measures and their location in the building; required upgrade cost; expected energy and water, operational, and life-cycle cost savings; and expected payback period.

An assessment of radiation damage in space-based germanium detectors due to solar proton events

Radiation effects caused by solar proton events will be a common problem for many types of sensors on missions to the inner solar system because of the long cruise phases coupled with the inverse square scaling of solar particle events. As part of a study in support of the BepiColombo mission to Mercury we have undertaken a comprehensive series of tests to assess these effects on a wide range of sensors. In this paper, we report on the measurements on a large volume coaxial Ge detector which was exposed to simulated solar proton spectra of integrated fluences 8 × 10 8 , 6 × 10 9 and 6 × 10 10 protons cm - 2 . After each irradiation the detectors performance was accessed in terms of energy resolution, efficiency and activation. The detector was then annealed and the measurements repeated before the next irradiation. The minimum operational performance criteria were based on the resolution and efficiency requirements necessary to detect and separate specific radioisotope emission lines from a planetary regolith. Specifically that the energy resolution be restored to 5 keV FWHM at 1332 keV and the detection efficiency be degraded to no more than 10% of its pre-irradiation value. The key conclusion of this study is that even after a modest solar proton event the detector requires extensive annealing. After exposure to an event of integral fluence ∼ 8 × 10 8 protons cm - 2 this amounts to ∼ 1 week duration at 100 ∘ C , whereas for a fluence of ∼ 6 × 10 10 protons cm - 2 , the detector requires 3.5 months of annealing to satisfy the minimum operational performance requirements and 4.5 months to return the energy resolution to < 3 keV FWHM at 1332 keV. As a consequence such an instrument will require constant, planned and active management throughout its operational lifetime. The impact on spacecraft operations including resource management therefore needs careful consideration.

Scenario-based assessment of nonfunctional requirements

This paper describes a method and a tool for validating nonfunctional requirements in complex socio-technical systems. The system requirements analyzer (SRA) tool validates system reliability and operational performance requirements using scenario-based testing. Scenarios are transformed into sequences of task steps and the reliability of human agents performing tasks with computerized technology is assessed using Bayesian belief network (BN) models. The tool tests system performance within an envelope of environmental variations and reports the number of tests that pass a benchmark threshold. The tool diagnoses problematic areas in scenarios representing pathways through system models, assists in the identification of their causes, and supports comparison of alternative requirements specifications and system designs. It is suitable for testing socio-technical systems where operational scenarios are sequential and deterministic, in domains where designs are incrementally modified so set up costs of the BNs can be defrayed over multiple tests.

Relating building attributes to end user's needs: “the owners‐designers‐end users” equation

This paper reports on the results of a research conducted in 2002 on two owner‐occupied purpose‐built office buildings with similar concerns on how to make the buildings and facilities work for the organisation in its life cycle. The research focused on the two different approaches to demand‐supply‐end user chain from two companies and investigated innovative processes by which client organisations (also owners) consider robust solutions of employee performance and user manageability. Using the case study material, processes by which the owners and designers or facilities managers translate user needs into operational performance requirements were appraised. The project investigated aspects of building‐in‐use, with walk‐through evaluation and workshops to draw out lessons and conclusions for clients, facilities managers and designers.

Channel access algorithm design for automatic identification system

The Automatic Identification System (AIS) is a maritime equipment to allow an efficient exchange of the navigational data between ships and between ships and shore stations. It utilizes a channel access algorithm which can quickly resolve conflicts without any intervention from control stations. In this paper, a design of channel access algorithm for the AIS is presented. The input/output relationship of each access algorithm module is defined by drawing the state transition diagram, dataflow diagram and flowchart based on the technical standard, ITU-R M.1371. In order to verify the designed channel access algorithm, the simulator was developed using the C/C++ programming language. The results show that the proposed channel access algorithm can properly allocate transmission slots and meet the operational performance requirements specified by the technical standard.

Surface micromachined solenoid inductors for high frequency applications

As operation frequencies and performance requirements of wireless devices increase, the resultant demands on the performance of passive components also increase. Miniaturization of inductive components for high frequency has been a key research area to address this issue; however, in general, miniaturized integrated inductors can suffer from low Q factors and/or self-resonant frequencies when compared to their discrete counterparts. In this research, a modified geometry of a solenoid type inductor using a surface micromachining technique is proposed. This inductor has an air core and an electroplated copper coil to reduce the series resistance, and its low temperature process is suitable for various packaging applications. An important feature of the proposed inductor geometry is the introduction of an air gap between the substrate and the conductor coil in order to reduce the effects of the substrate dielectric constant. This air gap can be realized using a polyimide sacrificial layer and a surface micromachining technique. Therefore, the resulting inductor can have less substrate-dependent magnetic properties, less stray capacitance, and higher Q-factor. The measurement result shows that this inductor has high Q-factor and stable inductance over a wide range of operating frequency. Also, various effects of geometrical factors have been investigated. Various inductors with the inductance varying from 1 to 20 nH and maximum Q-factor from 7 to 60 have been fabricated and measured.