PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

The Collation and Use of Data from Continuous Remote Monitoring
Systems for the Control of Sound Emissions from a Large Industrial
Noise Source
Timothy G PROCTER1; Stephen LYONS2
1

Griffith School of Environment, Griffith University, Australia
2

Umwelt (Australia) Pty Limited, Australia

ABSTRACT
Using traditional closed-loop control theory, this paper shows how the analysis of the sound emissions fromthe constantly changing environs of a large open-cut mine identified the source of a time lag in theimplementation of appropriate control strategies. As a result, a new control strategy based on SMART
technology has been developed to reduce the error signal time lag and help improve the control of noise
emanating from the mine. Key elements of the new control system discussed in this paper include: thecollation, analysis and reporting of the continuous real-time noise and meteorological monitoring data;
confirmation of the source’s contribution in a multi-source environment; and identification of high-risk
operational activities associated with the noise source. The SMART technology presents this information
in a format that: enhances the mining supervisor’s perception of the current environment; improves thecomprehension of the data; reduces the uncertainty associated with identifying the mine’s contribution to theacoustic environment; and enables potential future actions and outcomes to be identified. This paper
demonstrates that enhancing a user’s perception and awareness of the situation enables pre-emptive rather
than reactive decision-making that results in reduced noise impacts and improved productivity.
Keywords: Sound Emissions, Control, Industrial, SMART technology

1. INTRODUCTION
SMART 3 technology, described as the next industrial revolution, is widely used across many
sectors with intuitive graphic user interfaces integrating commercial data with end-user experiences.
To name but a few of the well-integrated applications, SMART technology allows for the review of
weather, access to healthcare, online and in-store shopping, organising travel, route selection basedon traffic conditions and the management of household energy use and generation. As such, SMART
technology applications are emerging in numerous sectors, from healthcare and education to
manufacturing and industry (1,2,3).
SMART technologies are based on the collection, collation and interpretation of data. Sensors of
one form or another gather data in real time for immediate use or store the data for further analysis.
If appropriate algorithms or artificial intelligence have not been developed to automate the analysis
and interpretation of the data, the collated data is delivered for user interpretation. A social
commentary in the UK Saga Magazine (4) reported that the long-term success of SMART products
and services would depend on how well the technology satisfied the user’s desire for practicalsolutions that made tangible differences. While SMART technology is highly developed forindividual users, the application of the technology for the industrial sector is not as advanced.
1
2

3

tim.procter@griffithuni.edu.auslyons@umwelt.com.au

Originally an acronym for computer hard drive “Self-Monitoring, Analysis and Reporting Technology”,
SMART is a description for inanimate objects that can talk to the user to guide behaviour, is associated with
network connectivity and often implies artificial intelligence.

528

However, this is not to say that there isn’t a wealth of data available for the collection, collation andinterpretation in many industrial arenas.
In particular, the mining industry in Australia has deployed a myriad of sensors to collect data fora diverse range of applications. These sensors track changes in groundwater depth and production
rates, and record data on highwall stability, weather conditions and mining machine diagnostics. As
technology advances, the collection of the data from the systems deployed is steadily being automated,enabling real time reporting from individual sensors and the collation and aggregation of the data to
produce information that is easier to comprehend and promote understanding (5).
Over the last 18 years the coal mining industry in New South Wales, Australia, has installed over
60 continuous noise monitoring units to record and report on the acoustic environment in the region
local to each monitoring unit. The continuous noise monitoring units have been installed either
individually or as part of continuous noise monitoring networks. The continuous noise monitors
report in several different proprietary formats using a range of different media and access protocols
and can generate in excess of 330 Mbytes of data each per day. However, the different brand units
have one primary metric of interest in common: the intrusive LAeq,15minute sound pressure levelfrom the source-of-interest.
The original continuous noise monitors were designed and installed at a time when environmental
noise standards and legislation were structured around principles of after-the-fact noise reporting
rather than noise management (6). Installation of continuous noise monitoring units by mining
operations has facilitated the development and implementation of a range of pro-active noise
management techniques. The developments initiated by the mining operations are now reflected indevelopment consents 4 that require the mining operations to implement comprehensive noise
management systems to ensure compliance with noise immission limits specified in respective
consents. The prescriptive requirements of the development consents require the noise management
systems to use a combination of predictive meteorological forecasting, real-time meteorological data
and real-time noise monitoring data to guide the mining operations. The mining operations also use
predictive noise modelling to identify potential operational constraints and mine plan design
alternatives.
Operationally, the day-to-day management of an open-cut mine struggles to assimilate and respond,in real-time, to the data from the continuous noise monitors. The management of the mine is the
responsibility of the Mining Supervisor and is dictated by statutory rules and laws that govern thehealth and safety of the people working in and around the mine. Part of the role of the Mining
Supervisor is to manage the noise emitted from the mine in accordance with the requirements of thedevelopment consent. To facilitate the management of the noise emitted by the mining operation, thenoise management systems need to firstly, present the information in a format that enhances the user’s
perception of the current environment; secondly, improve the user’s comprehension of the data in
relation to the stated aim, objective or goal of their role; and finally, enable the user to identify
potential future actions and outcomes (7,8,9).

2. Motivation for the Development of a New Noise Management System
Noise modelling indicated there would be a significant increase in the noise levels in a ruralcommunity in the Hunter Valley, New South Wales resulting from the approved re-orientation of the
open-cut pit of a coal mine between 2015 to 2017. It was predicted the sound pressure level 5
attributed to the mine in the region to the south-east of the re-orientated open-cut pit would gradually
increase by 4 to 5 dB. During the re-orientation, the change in the acoustic environment wasmeasured by a continuous noise monitor located 2500m to the south-east of the site. As predicted,
the results from the noise monitor for winter evening/night periods in 2015 and 2016 showed anincrease in immission level attributed to the mine of 3 dB. Figure 1 shows the variation in thefrequency distribution of measured LAeq,15minute sound pressure level attributed to the minemeasured during the winter evening/night periods of 2015 to 2016.

4

A formal notice of approval issued by a regulatory authority for the implementation of a specificdevelopment proposal described in a development application. A development consent typically includes
a detailed list of conditions under which the development can be operated.
5
Reported as the immission level at the monitoring location under weather conditions that enhance thepropagation of the sound from the source to the receiver.

529

Figure 1 – Variation in the frequency distribution from 2015 to 2016 of the measured winterevening/night time noise immission level attributed to the mine. The raw data has been filtered tominimise the effect of temporal and spatial differences between 2015 and 2016. This enables theimpact of changes in the mine plan between 2015 and 2016 to be observed.
The shift in the frequency distribution from the 30 to 38 dB(A) range to greater than 38 dB(A) in
Figure 1 can be linked to an increase in the number of machines operating in exposed locations within
the mine. Figure 1 also shows an increase in the frequency of noise immission levels less than
30 dB(A) which is attributed to the number and duration of machine shutdowns as the mine’s primary
form of noise control. The increase in the average measured sound pressure level from winter 2015
to winter 2016 attributable to the re-orientation of the open cut pit was 3 dB. This increase wasaccompanied by an 85% increase in the noise immission levels above 40 dB(A) at that monitoring
location and a 16% increase in the number of complaints. Predictive noise modelling indicated there
would be a further 2 dB increase in the noise immission level at the monitoring locations as the minetransitioned through the re-orientation of the open-cut pit.

3. The Response
A phone/tablet-based SMART application was developed as a tool to improve the Mining
Supervisor’s access to relevant information. To achieve this, the SMART application integrated
research into the application of traditional control theory to the control of noise from open-cut mining
and research into situation awareness and information visualisation.
In traditional control theory, a closed-loop control system (or feedback control system) uses a 'set
point' as a reference value, to characterise the desired output response of a process. The measured
difference between the set point and the output response of the process can be due to a change in theset point or due to a disturbance to the system. A well-tuned control system is characterised by its
stability, its ability to respond to external disturbances and its reliable and repeatable performance.
Representing the noise management system used by the Mining Supervisors as a closed-loop control
system enables the response and action of each element of the system to be studied.
Endsley’s (10) theory of situation awareness comprises three levels: the perception of theenvironment, both element and event, with respect to time and space; the comprehension of themeaning of these elements and events as it relates to a specific goal; and the projection or anticipationof what is to occur. Endsley (10) states that situation awareness is distinguished as a state ofknowledge as opposed to the process that is used to achieve that state. The research into datavisualisation indicated that a user’s perception and awareness of a situation is enhanced when they
are provided with appropriately visualised data and that increased situation awareness results indecision-making that can be pre-emptive rather than reactive. Understanding the theory of situation
awareness and concepts behind effective data visualisation enable the feedback element in the closedloop control system to be studied in detail.
The goal of the SMART application was to develop a tool incorporating SMART technology andvisualisation techniques that enhanced the Mining Supervisor’s situation awareness of the noise
impacts and associated management requirements for the mine. The investigations into the existing
noise management system and the subsequent development of the SMART application to replace theexisting feedback element in the closed-loop control system are described below.

530

4. Development of the Smart Technology Application
4.1 Data Analysis and Noise Control
The collection, collation and use of continuous noise monitoring data from remote noisemonitoring units to control noise emitted by the mine is represented as a closed-loop feedback control
system in Figure 2.

Figure 2 – Block diagram of feedback control system with input, output and measurement
disturbance used to represent the control of the noise emitted by an open-cut mining operation
Representing the noise management system as a closed-loop feedback control system modified theapproach taken to analysing the collated datasets. The key to this is the definition of each element.
Starting with the process 𝐺 (𝑡) the elements in Figure 2 can be described as follows: the process 𝐺 (𝑡) is the emission of sound by the mining processes and would include a predefined
set of conditions that govern the propagation of the sound to the sensor location. This includes
meteorological conditions, ground properties, terrain features (both natural and man-made), thesource sound power and directivity associated with the operating/control strategy of the mine, andreceiver geometry (11,12).
 𝐷 (𝑡) is the disturbance that modifies the output signal from the process to produce the noise
immission level 𝑌(𝑡). 𝐷 (𝑡) is associated with the spatial variability of the propagation of thesound and would be described as the aleatory uncertainty of the meteorological conditions andground conditions.
 𝑌(𝑡) is the noise immission level or sound pressure level from the source-of-interest at thereceiving location.
 𝐷 (𝑡) is the measurement disturbances that hinder the source sound signal identification.
Masking effects from other sources is a typical example but 𝐷 (𝑡) may also include limitations
associated with the configuration of the sensor array.
 𝐻(𝑡) is the measurement device incorporating one or more continuous noise monitors and 𝑃𝑉 is
the measurement device’s representation of 𝑌(𝑡) taking into account 𝐷 (𝑡).
 the error 𝑒(𝑡) is the difference between the desired outcome (the set point) 𝑅(𝑡) and the measured
outcome (or process variable) 𝑃𝑉, of the actual output 𝑌(𝑡).
 the control device 𝐺 (𝑡), which can be described as predominantly human, implements a control
strategy 𝑈(𝑡) based on magnitude and sign of the error 𝑒(𝑡). The primary objective of the noise
management system represented by Figure 2 is to maintain the noise immission attributable to themining operation 𝑌(𝑡) at a level that is equal to or less than the noise immission limit 𝑅(𝑡) at thelocation of the sensor array. A secondary objective is to minimise the disruption to the mining
operation through the implementation of unnecessary control strategies when 𝑌(𝑡) < 𝑅(𝑡). The
control strategy 𝑈(𝑡) can be represented by the following set of transfer functions:𝑈(𝑡) =0
𝐺 (𝑡)𝑌(𝑡) ≤ 𝑅(𝑡)𝑌(𝑡) > 𝑅(𝑡)(1)

 𝐷 (𝑡) is the disturbance that modifies the response of the process to the signal/instruction from thecontrol device 𝑈(𝑡) . 𝐷 (𝑡) is associated with the deviation away from the predefined set of
conditions associated with the process 𝐺 (𝑡). This would include the temporal deviation of themeteorological conditions and changes in the operations that deviate from the agreed mining
operating/control strategy.

531

In traditional control theory the overall objective is to minimise the tracking error:𝑒(𝑡) = 𝑅(𝑡) − 𝑌(𝑡)(2)
6
Dorf and Bishop (13) show that by manipulating the block diagram in Figure 2 the tracking error
can be re-written as:𝑒(𝑡) =

1
𝐺 (𝑡)
𝐺 (𝑡)𝐺 (𝑡)𝑅(𝑡) −
𝐷 (𝑡) +𝐷 (𝑡)1 + 𝐺 (𝑡)𝐺 (𝑡)1 + 𝐺 (𝑡)𝐺 (𝑡)1 + 𝐺 (𝑡)𝐺 (𝑡)The loop gain 𝐿(𝑡) of the control system is written as:
𝐿(𝑡) = 𝐺 (𝑡)𝐺 (𝑡)and the sensitivity 𝑆(𝑡) of the control system is written as:
𝑆(𝑡) =

1
1 + 𝐿(𝑡)

(3)

(4)

(5)

Dorf and Bishop (2011) define a second complementary sensitivity function 𝐶(𝑡) as:𝐶(𝑡) =𝐿(𝑡)1 + 𝐿(𝑡)(6)

The tracking error can now be written in terms of the system sensitivity, the input disturbance𝐷 (𝑡) and the measurement disturbance 𝐷 (𝑡):𝑒(𝑡) = 𝑆(𝑡)𝑅(𝑡) − 𝑆(𝑡)𝐺 (𝑡)𝐷 (𝑡) + 𝐶(𝑡)𝐷 (𝑡)(7)When analysing a control system to minimise the tracking error 𝑒(𝑡), both 𝑆(𝑡) and 𝐶(𝑡) need tobe small but this cannot happen simultaneously as 𝑆(𝑡) + 𝐶(𝑡) = 1. However, traditional control
theory tells us that to reduce the influence of the input disturbance 𝐷 (𝑡) the loop gain 𝐿(𝑡) should
be large. The resulting function 𝑆(𝑡)𝐺 (𝑡), also written as 𝐺 (𝑡)/(1 + 𝐿(𝑡)), will be small if 𝐿(𝑡)is large thereby reducing the influence of 𝐷 (𝑡) . For the loop gain 𝐿(𝑡) to be large the control
function 𝐺 (𝑡) also needs to be large which can equate to an over-reaction to the error 𝑒(𝑡).
To reduce the influence of the measurement disturbance 𝐷 (𝑡) the complementary sensitivity
function 𝐶(𝑡) needs to be small. To achieve this, the control function 𝐺 (𝑡) needs to be small sothat the loop gain 𝐿(𝑡) is small which makes the system response sluggish.
This results in a conflict between wanting the control function 𝐺 (𝑡) to be large to reduce theinfluence of the input disturbance 𝐷 (𝑡) and small to reduce the influence of the measurement
disturbance 𝐷 (𝑡). The monitoring results in Figure 1 indicated both situations were present in theexisting noise management system. The existing system was sluggish and slow to respond to theerror when 𝑌(𝑡) > 𝑅(𝑡) and when the system did respond the control function 𝐺 (𝑡) over-reacted.
4.2 Data Collection and Collation
The next step in the investigative process involved the manual collection, collation and analysis ofdaily monitoring data from primary continuous noise monitors (1500m and 2500m from the activemining area), one supplementary noise monitor (500m from the active mining area) andmeteorological data from sensors on three 10-metre towers and from sensors on two supplementary
3-metre towers.
Through manual analysis, a complementary program of fieldwork to validate the monitoring dataand predictive noise modelling it was possible to interpret the trends in the relationships between thetwo primary noise monitors to quantify 𝐺 (𝑡), the measurement device’s representation of 𝑌(𝑡) (𝑃𝑉)and the response of the control function 𝐺 (𝑡). The relationship between the two primary noisemonitors needed to account for the dynamic nature of the mining operation, the complex acoustic
environment at the monitoring locations and the temporal variance of the meteorological conditions;remembering a predefined set of meteorological conditions was considered as part of the process
𝐺 (𝑡) but the temporal deviation of the meteorological conditions away from the predefinedconditions would be considered part of the output disturbance modifier 𝐷 (𝑡). The analysis alsoneeded to consider how the tracking error 𝑒(𝑡) was being calculated and how it was being
communicated to the control function 𝐺 (𝑡).
6

To simplify the analysis, the output disturbances 𝐷 (𝑡) associated with spatial differences inmeteorological conditions that enhance or retard the propagation of the sound signal for a predefined set of
meteorological conditions have been ignored.

532

4.3 Data Analysis
Analysis of each element of the closed-loop feedback control system using the collated data
discussed above identified several shortcomings in the existing control strategy. This included thereliance on a single noise monitor to quantify, with certainty, the noise immission level 𝑌(𝑡) from thesource-of-interest at the receiver location 2500m from the source, uncertainty in determination of
atmospheric stability conditions and the relationship between level 𝑌(𝑡) and 𝑃𝑉 as a result of thefunction of the measurement device 𝐻(𝑡).
The first two items have resulted in modifications to the monitoring equipment. Ongoing researchis investigating the use of paired monitors to determine the noise immission level 𝑌(𝑡) and additionaltemperature sensors have been added to the weather stations to improve the reporting of the output
disturbance modifier 𝐷 (𝑡).
The most significant shortcoming of the existing noise management system was identified as theoperation of the measurement device 𝐻(𝑡) and the communication of the tracking error 𝑒(𝑡) to thecontrol function 𝐺 (𝑡). The analysis of the monitoring data found that a time lag in the 𝑃𝑉 was theprimary source of the tracking error 𝑒(𝑡) when 𝑌(𝑡) > 𝑅(𝑡).
Figure 3 shows a 210-minute sample of the sound pressure level 𝐿 , where 𝐿 , = 𝑌(𝑡), attributed
to the mine at the noise monitor located 2500m from the active mining area. The change in the soundpressure level 𝐿 in Figure 3 is attributed to an input disturbance 𝐷 (𝑡) in the meteorologicalconditions that resulted in enhancement in the propagation of the sound emitted by the machines
operating in the mine towards the noise monitor location. The trace of the reported sound pressurelevel 𝐿 resembles a process reaction response curve with the control system off-line following a
disturbance to the process input. In this instance the control system is not off-line but its action,
shown in Figure 3(a), is delayed because the control actions are only initiated following a system
alarm, the actual form of 𝑃𝑉.

(a)Figure 3 – Analysis of sound pressure level 𝐿 response curve showing the dead-time 𝜏 of theprocess reaction, the system time constant 𝜏 and reaction rate R of 𝐿 following an input
disturbance 𝐷 (𝑡) (a) before and (b) after the implementation of the SMART technology,
Figure 3(a) shows that the PV sensor lag, which includes a system dead-time 𝜏 of 66 minutes
has a detrimental impact on the control of the system resulting in the system being sluggish in response
to input disturbances. In Figure 3(b) the stem dead-time has been reduced to 36 minutes.
4.4 SMART Technology Applications
A WEB based prototype of the noise management system feed-back loop was developed toautomate the collection, collation, analysis and reporting of the monitoring data at 5-minute intervals.
The success of the proof-of-concept prototype resulted in the development of a SMART technology
application that provided unrestricted access to the information by the Mining Supervisors.
The primary objective of the SMART technology application was to reduce the error signal timelag and facilitate the control of noise emanating from the mine by the Mining Supervisors. Throughdata aggregation and the tailoring of the data visualisation the SMART technology application has
improved the Mining Supervisors’ comprehension of the monitoring data and their533

understanding/perception of the operation’s impact on the acoustic environment.
The effectiveness of the SMART technology application is shown in Figures 4 and 5.

Figure 4 – 𝐿 and 𝑃𝑉 response curves using an updated 𝑃𝑉 sensor reporting system following
implementation of the SMART technology sensor reporting system. The control objective is:𝑌(𝑡) ≤ 𝑅(𝑡) where 𝑅(𝑡) = 40𝑑𝐵(𝐴).

Figure 5 – Variation in the frequency distribution from 2016 to 2017 of the measured winterevening/night period noise immission level attributed to the mine. The raw data has been filtered tominimise the effect of temporal and spatial differences between 2016 and 2017.
Figure 4 shows that the reduction in the 𝑃𝑉 sensor lag has enabled Mining Supervisors to maintaincontrol of the noise emissions from the mining operation. In comparison to Figure 3(a), Figure 4
shows a reduction in the overshot of the 𝐿 response curve for 𝑅(𝑡) = 40 𝑑𝐵(𝐴) plus improved
control of the 𝐿 response curve. The improvement in the 𝐿 response curve can be attributed to
the reduction in the 𝑃𝑉 sensor lag, an increase in the 𝑃𝑉 sensor updates from 15 minutes to 5 minutes
and the improved accessibility to the data through the SMART technology.
Figure 5 shows the variation in the frequency distribution of measured LAeq,15minute soundpressure level attributed to the mine during the winter evening/night periods of 2016 to 2017 following
the implementation of the SMART technology application. In comparison to Figure 1, Figure 5 showsa decrease in the frequency of occurrence of noise emission levels over 40 dB(A) and a corresponding
increase in the frequency of occurrence of noise emission levels in the 31 to 40 dB(A) range. This
shift in the frequency distribution from above 40 dB(A) to below 40 dB(A) is consistent with theestablished goal for the control system, that 𝑌(𝑡) ≤ 𝑅(𝑡) where 𝑅(𝑡) = 40 𝑑𝐵(𝐴) . Using the
SMART technology has also enabled the Mining Supervisors to target high-risk operational activities
that contribute to the acoustic environment during noise enhancing meteorological conditions. Thisis demonstrated in Figure 5 as a decrease in the frequency of noise levels less than 31 dB(A). Thisis consistent with the second objective of the control system: to minimise the disruption to the mining534

operation through the implementation of unnecessary control strategies when 𝑌(𝑡) < 𝑅(𝑡). This has
been achieved through the implementation of noise control strategies that were designed to reduce thesound output from machines in exposed locations without shutting the machines down.

5. Conclusion
Analysis of the noise management system as a closed-loop feedback control system identified
several deficiencies in the existing control strategy. The most significant shortcoming was a time lagin the control system resulting in a sluggish response to high noise immission levels at the receiverlocation.
The analysis of the existing control strategy lead to the understanding that the installation ofcontinuous noise monitoring networks must be coupled with systems that enhance the situation
awareness of the Mining Supervisors. Based on the theory of situation awareness, a SMART
technology application was developed to enhance the Mining Supervisors’ perception and awarenessof the noise immission levels at the receiver location. The SMART technology enabled pre-emptive
decision-making that resulted in reduced noise impacts and improved productivity. The change in
the average measured sound pressure level at the monitoring location from winter 2016 to winter 2017attributable to the SMART technology application equated to a gross decrease of 3 dB. This was
accompanied by a 40% decrease in the number of hours the noise immission levels were reported
above 40 dB(A), a 25% decrease in the number of complaints and a 65% decrease in equipment
shutdown resulting in increased productivity.
The use of SMART technology to present meaningful data pertinent to the Mining Supervisors
situation awareness has resulted in a measurable improvement in noise impacts.

ACKNOWLEDGEMENTS
The material contained in this paper was developed as part of a research project funded by theAustralian Coal Association Research Program (ACARP). The authors would also like to express
their appreciation to the industry personnel who contributed to this paper.

REFERENCES
1. Brougham D, Haar J. Smart technology, artificial intelligence, robotics, and algorithms (STARA):
employees’ perceptions of our future workplace. Journal of Management & Organization. 2018
Mar;24(2):239-57.
2. Dumka A, Sah A. Smart ambulance system using concept of big data and internet of things. In Healthcare
Data Analytics and Management 2019 Jan 1 (pp. 155-176). Academic Press.
3. Foroudi P, Gupta S, Sivarajah U, Broderick A. Investigating the effects of smart technology on customer
dynamics and customer experience. Computers in Human Behavior. 2018 Mar 1;80:271-82.
4. https://www.saga.co.uk/magazine/technology/home-technology/gadgets/the-rise-of-smart-technology
5. Endsley MR. Situation awareness: operationally necessary and scientifically grounded. Cognition,
Technology & Work. 2015 May 1;17(2):163-7.
6. Manvell, D. From noise monitoring to noise management–a better way to deal with noise issues. Proc
INTER-NOISE August 2015, San Francisco USA p2473-2484.
7. Endsley MR. Situation awareness misconceptions and misunderstandings. Journal of Cognitive
Engineering and Decision Making. 2015 Mar;9(1):4-32.
8. Franklin A, Gantela S, Shifarraw S, Johnson TR, Robinson DJ, King BR, Mehta AM, Maddow CL, HootNR, Nguyen V, Rubio A. Dashboard visualizations: Supporting real-time throughput decision-making.
Journal of biomedical informatics. 2017 Jul 1;71:211-21.
9. Procter TG, Tomerini D, Brown AL. The Collation and Use of Data from Remote Monitoring Systems.
Proc AAS Conference November 2018, Adelaide Australia.
10. Endsley MR. Toward a theory of situation awareness in dynamic systems. Human factors. 1995
Mar;37(1):32-64.
11. Marsh KJ. The CONCAWE model for calculating the propagation of noise from open-air industrial plants.
Applied acoustics. 1982 Nov 1;15(6):411-28.
12. Wilson DK, Pettit CL, Ostashev VE, Vecherin SN. Description and quantification of uncertainty inoutdoor sound propagation calculations. The Journal of the Acoustical Society of America. 2014
Sep;136(3):1013-28.
13. Dorf RC, Bishop RH. Modern control systems. Pearson; 2011.

535