International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Logic coverage criteria are central aspect of programs and specifications in the testing of software. A
Boolean expression with n variables in expression 2n distinct test cases is required for exhaustive testing
.This is expensive even when n is modestly large. The possible solution is to select a small subset of all
possible test cases which can effectively detect most of the common faults. Test case prioritization is one of
the key techniques for making testing cost effective. In present study performance index of test suite is
calculated for two Boolean specification testing techniques MUMCUT and Minimal-MUMCUT.
Performance index helps to measure the efficiency and determine when testing can be stopped in case of
limited resources. This paper evaluates the testability of generated single faults according to the number of
test cases used to detect them. Test cases are generated from logic expressions in irredundant normal form
(IDNF) derived from specifications or source code. The efficiency of prioritization techniques has been
validated by an empirical study done on bench mark expressions using Performance Index (PI) metric.
Advanced DOE with Minitab (presentation in Costa Rica)Blackberry&Cross
DOE:Diseño de Experimentos
Esta presentación fue dada por Minitab Inc., en Costa Rica, en el año 2007, como parte del trabajo de Blackberry&Cross, socio de Minitab Inc., para América Central, en la promoción y difusión de temas STEM, y de la comercialización de Minitab Statisitical Software.
Logic coverage criteria are central aspect of programs and specifications in the testing of software. A
Boolean expression with n variables in expression 2n distinct test cases is required for exhaustive testing
.This is expensive even when n is modestly large. The possible solution is to select a small subset of all
possible test cases which can effectively detect most of the common faults. Test case prioritization is one of
the key techniques for making testing cost effective. In present study performance index of test suite is
calculated for two Boolean specification testing techniques MUMCUT and Minimal-MUMCUT.
Performance index helps to measure the efficiency and determine when testing can be stopped in case of
limited resources. This paper evaluates the testability of generated single faults according to the number of
test cases used to detect them. Test cases are generated from logic expressions in irredundant normal form
(IDNF) derived from specifications or source code. The efficiency of prioritization techniques has been
validated by an empirical study done on bench mark expressions using Performance Index (PI) metric.
Advanced DOE with Minitab (presentation in Costa Rica)Blackberry&Cross
DOE:Diseño de Experimentos
Esta presentación fue dada por Minitab Inc., en Costa Rica, en el año 2007, como parte del trabajo de Blackberry&Cross, socio de Minitab Inc., para América Central, en la promoción y difusión de temas STEM, y de la comercialización de Minitab Statisitical Software.
Study of environmental factors at machining workstation a methodologyIJECSJournal
This is an approach for formulation of generalized field based data model for the process of tractor axle drilling workstation. Field based data modeling is applicable for any type of man-machine system. It forms the relationship between input and output variables. This type of modeling is used for improving the performance of system by suggesting or modifying the inputs for improving output. The process of axle drilling at Asha Industries Pvt. Ltd. is a man-machine system. The mathematical model will be useful in selecting the input variables so as to reduce human energy consumption and to improve the productivity. The quality of environment in workplace may simply determine the level of employee’s motivation, performance and productivity.The Tractor axle drilling process which is considered for study is a complex phenomenon & hence studies of environmental factors and its assessment while performing axle drilling is main objective of this paper.
Thermal-Acoustic Comfort Index for Workers of Poultry Houses Using Fuzzy Mode...IJERA Editor
Thermal-acoustic comfort is considered an essential factor for the performance of industrial activities. As well
as harm the health of the workers, environments outside the adequate conditions provoke losses in productivity.
The objective of this work was to develop a system capable of evaluating and classifying the working
environment in poultry houses. A working regime of 8 hours per day in a poultry house was simulated and the
results provide support for the classification of the comfort level based on different climate and noise conditions.
Two input variables were used: wet bulb globe temperature (WBGT) and noise level (dB), and the
correspondent output variable was the human welfare index (HWI). The results indicate that the proposed
methodology is a promising technique for the determination of the level of thermal comfort endured by poultry
house workers, capable of assisting in making decisions on control of the working environment.
Theoretical heat conduction model development of a Cold storage using Taguch...IJMER
In this project work a mathematical heat conduction model of a cold storage (with the help of
computer program; and multiple regression analysis) has been proposed which can be used for further
development of cold storages in the upcoming future. Taguchi L27 orthogonal array (OA) has been used as
a design of experiments (D.O.E). Heat gain (Q) in the cold room taken as the output variable of the study.
With the help of a computer program several data sets have been generated on the basis of the proposed
model. From the graphical interpretation, the critical values of the predictor variables also proposed so
as the heat flow from the outside ambience to the inside of the cold room will be minimum. Insulation
thickness of the side walls (TW), area of the wall (AW), and insulation thickness of the roof(TR) have been
chosen as predictor variables of the study.
Application of Taguchi Parametric Technique for the Decrease in Productivity...IJMER
Now a day, working in industry is not as comfortable as in earlier times. The man power is
decreasing day by day and new recruitment is not in same ratio as the persons leaving industries. This is
turn increase the work load on employees which causes physical problems to employees and also
decreases the productivity of worker. The purpose is to determine the optimum combination of three
parameters like Worker weight, component weight and worker age for achieving minimum loss (in
percentage) in productivity of worker. In order to meet the purpose in terms of productivity study will
utilize Taguchi parameter optimization methodology. The study includes selection of parameters
utilizing an orthogonal array, conducting experimental runs, data analysis, determining the optimum
combination, verification finally the modeling of input parameters (worker weight, component weight
and worker age), for a particular job work, for particular machine to minimize fatigue of worker and
loss (in percentage) of productivity in industry.
EFFECT OF PROCESS PARAMETERS ON FLATNESS OF PLASTIC COMPONENT IAEME Publication
Dimensional changes because of shrinkage is one of the most important problem in production of plastic parts using plastic injection molding(PIM). In this study, effect of injection
molding parameters on surface flatness of plastic component is investigated and achieving the flatness according to customer requirement is the big task, for that this work is carried out.
OPTIMIZATION OF PROCESS PARAMETERS OF PLASTIC INJECTION MOLDING FOR POLYPROPY...IAEME Publication
The injection molding process itself is a complex mix of time, temperature and pressure variables with a multitude of manufacturing defects that can occur without the right combination of processing parameters and design components. In this analysis input processing parameters are melt temperature (MT), Injection pressure(IP), holding pressure(HP) and cooling time(Cool Time) and
responses considered for investigation of plastic injection molding process are cycle time and tensile strength. The material used for experimentation is polypropylene.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Study of environmental factors at machining workstation a methodologyIJECSJournal
This is an approach for formulation of generalized field based data model for the process of tractor axle drilling workstation. Field based data modeling is applicable for any type of man-machine system. It forms the relationship between input and output variables. This type of modeling is used for improving the performance of system by suggesting or modifying the inputs for improving output. The process of axle drilling at Asha Industries Pvt. Ltd. is a man-machine system. The mathematical model will be useful in selecting the input variables so as to reduce human energy consumption and to improve the productivity. The quality of environment in workplace may simply determine the level of employee’s motivation, performance and productivity.The Tractor axle drilling process which is considered for study is a complex phenomenon & hence studies of environmental factors and its assessment while performing axle drilling is main objective of this paper.
Thermal-Acoustic Comfort Index for Workers of Poultry Houses Using Fuzzy Mode...IJERA Editor
Thermal-acoustic comfort is considered an essential factor for the performance of industrial activities. As well
as harm the health of the workers, environments outside the adequate conditions provoke losses in productivity.
The objective of this work was to develop a system capable of evaluating and classifying the working
environment in poultry houses. A working regime of 8 hours per day in a poultry house was simulated and the
results provide support for the classification of the comfort level based on different climate and noise conditions.
Two input variables were used: wet bulb globe temperature (WBGT) and noise level (dB), and the
correspondent output variable was the human welfare index (HWI). The results indicate that the proposed
methodology is a promising technique for the determination of the level of thermal comfort endured by poultry
house workers, capable of assisting in making decisions on control of the working environment.
Theoretical heat conduction model development of a Cold storage using Taguch...IJMER
In this project work a mathematical heat conduction model of a cold storage (with the help of
computer program; and multiple regression analysis) has been proposed which can be used for further
development of cold storages in the upcoming future. Taguchi L27 orthogonal array (OA) has been used as
a design of experiments (D.O.E). Heat gain (Q) in the cold room taken as the output variable of the study.
With the help of a computer program several data sets have been generated on the basis of the proposed
model. From the graphical interpretation, the critical values of the predictor variables also proposed so
as the heat flow from the outside ambience to the inside of the cold room will be minimum. Insulation
thickness of the side walls (TW), area of the wall (AW), and insulation thickness of the roof(TR) have been
chosen as predictor variables of the study.
Application of Taguchi Parametric Technique for the Decrease in Productivity...IJMER
Now a day, working in industry is not as comfortable as in earlier times. The man power is
decreasing day by day and new recruitment is not in same ratio as the persons leaving industries. This is
turn increase the work load on employees which causes physical problems to employees and also
decreases the productivity of worker. The purpose is to determine the optimum combination of three
parameters like Worker weight, component weight and worker age for achieving minimum loss (in
percentage) in productivity of worker. In order to meet the purpose in terms of productivity study will
utilize Taguchi parameter optimization methodology. The study includes selection of parameters
utilizing an orthogonal array, conducting experimental runs, data analysis, determining the optimum
combination, verification finally the modeling of input parameters (worker weight, component weight
and worker age), for a particular job work, for particular machine to minimize fatigue of worker and
loss (in percentage) of productivity in industry.
EFFECT OF PROCESS PARAMETERS ON FLATNESS OF PLASTIC COMPONENT IAEME Publication
Dimensional changes because of shrinkage is one of the most important problem in production of plastic parts using plastic injection molding(PIM). In this study, effect of injection
molding parameters on surface flatness of plastic component is investigated and achieving the flatness according to customer requirement is the big task, for that this work is carried out.
OPTIMIZATION OF PROCESS PARAMETERS OF PLASTIC INJECTION MOLDING FOR POLYPROPY...IAEME Publication
The injection molding process itself is a complex mix of time, temperature and pressure variables with a multitude of manufacturing defects that can occur without the right combination of processing parameters and design components. In this analysis input processing parameters are melt temperature (MT), Injection pressure(IP), holding pressure(HP) and cooling time(Cool Time) and
responses considered for investigation of plastic injection molding process are cycle time and tensile strength. The material used for experimentation is polypropylene.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Monitoring Java Application Security with JDK Tools and JFR Events
H45023744
1. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 37 | P a g e
Application of Taguchi Method In Health And Safety (Fire
Extinguishing Experiment)
Alharthi A. A. & Yang Q.
School of Engineering , Brunel University, London
ABSTRACT
The traditional Taguchi method is widely used for optimizing the process parameters of a single response
problem. In this paper, Taguchi method is applied to study the effects of five control variables – training,
experience, response to alarm, age, and qualification on extinguishing time and percent damage. An L16
orthogonal array (OA) was used to accommodate the experiment. ANOVA and F-tests and regression are used
to analyze the results. The study indicated that training and experience have the largest effect on the on
extinguishing time and percent damage
Key words: ANOVA analyze, Extinguishing time, Fire extinguishing experiment, orthogonal array (OA),
percent damage and Taguchi method.
I. Introduction
Taguchi Method or Robust Engineering,
developed by Genichi Taguchi, is an approach to
Design of Experiments (DOE) for designing products
or processes so that they are robust to environmental
conditions such temperature and humidity. The
objective of Taguchi method is to model responses
(and variance) as a function of controllable (and
uncontrollable) factor levels, then choose levels of
controllable factors to reduce variation transmitted to
the response from variation of the controllable factors
and of the uncontrollable factors, in another word
reduce product variation by choosing levels of the
control factors that dampen the effect of the
uncontrollable or noise factors. Quality is improved
without controlling or removing the cause of
variation, instead, we make the product (or process)
robust to variation in the noise factors [4], [5], [6].
Noise factor is measured by signal to noise ratio and
it is calculated depends on the objective of the
experiment. There are three ways the response could
be optimized[6]:
Objective Signal to noise ratio
Minimize response -10*log(Sy2
/n)
(smaller the better)
Maximize the response -10*log(S(1/y2)
/n)
(larger the better)
Nominal is best -10*log(s2
)
Multiplied by 10 to put into “deci” “bel” metric, a
terminology used in Electrical Engineering. Taguchi
suggested that “quality” should be thought of, not as
a product being inside or outside of specifications,
but as the variation from the target. He defines
quality as the losses a product imparts to the society
from the time the product is shipped. To quantify
quality loss, write T for the target value and Y for the
measured value [5], [6]. We want E (Y) = T. Write
L(Y) for the loss (in dollars, reputation, customer
satisfaction, ……) for deviation of Y from T. The loss
function is L(Y) = K(Y-T)2
Where, K is some constant. If E (Y) really is T, then
E(L(Y)) = Kσ2
, where σ2
= Var (Y).
If the product is off target, so that E (Y) = T +d, then
E(L(Y )) = k(σ2
+d2
).
Figure 1. Quality loss function
A Taguchi design, or an orthogonal array, is
a method of designing experiments that usually
requires only a fraction of the full factorial
combinations. An orthogonal array means the design
is balanced so that factor levels are weighted equally.
Because of the orthogonality, each factor can be
evaluated independently of all the other factors, so
RESEARCH ARTICLE OPEN ACCESS
2. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 38 | P a g e
the effect of one factor does not influence the
estimation of another factor.
The Steps followed for Taguchi design are:
1. State your problem and objective
2. List responses, control parameters, and sources
of noise
3. Plan the experiment
4. Run experiment and
5. Analyze the experiment and predict improved
parameter settings
II. Problem Identification
Fire accidents are common and play an
important role amongst major accidents, not only
because of their relatively high frequency but also
because of their dangerous effects. This makes the
control and protection from the fire accidents a vital
issue that needs to be studied. Our objective is to
determine the effect of employee training,
experience, his or her response to alarm, his or her
age and qualification and their interactions on how
long it takes to extinguish a fire and asses time the
percent damage resulted from the fire.
III. Experimental Details
A series of experimental tests were designed
to accomplish these objectives and develop baseline
for future research. The experiment is conducted by
running it at various levels of the factors.
3.1 Determining factors for the study
It was determined the human factors that
need to be studies which influence the performance
of an employee in using the extinguisher are:
a) Training: it is expected that there is difference
between the performance of trained employee
and untrained employee.
b) Experience: the employee with more experience
may perform better in the extinguishing process.
It is worthy to say that the expert employee is
already a trained employee.
c) The response to the alarm: the fast response to
the alarm may lead us to the best scenarios.
d) Age: the employee age directly affects the
physical and mental behavior of the employee in
which it affects his performance.
e) Qualification: higher qualification is predicted to
lead to better performance.
The response variables to be measured and
improved are:
a) Extinguishing time: it is a measure of the
employee performance and measured in seconds.
b) Percentage of damage: it will be used to study
the relation between the extinguishing time and
the damage percentage. It is believed longer
extinguisher time leads to more damage.
3.2 Determining the levels of the factors
The Process parameters and their levels used
in the experiment summarized in table 1
Table 1. Factors and their levels
Factors code Level 0 Level 1
Training T Untrained Trained
Experience X without
experience
with
experience
Response to
the alarm
R >30 sec.
(Slow
response)
<30 sec.
(Fast
response)
Age G >40 <40 years
Qualification Q <Bachelor >Bachelor
3.3 Taguchi Design
Taguchi Orthogonal Array L16 design is
used (table 2) which included 5 factors and 16 runs.
Columns of L16(2**15) Array are chosen that are 1,
2, 4, 8 and 15. The L16 is a resolution III design
which means the main effect is confounding with two
factor interaction. The alias structure for L16 is
summarized below:
[A] = A - BC - DE - FG - HJ -KL - MN - OP
[B] = B - AC - DF - EG - HK -JL - MO - NP
[C] = C - AB - DG - EF - HL - JK - MP - NO
[D] = D - AE - BF - CG - HM - JN - KO - LP
[E] = E - AD - BG - CF - HN - JM - KP - LO
[F] = F - AG - BD - CE - HO - JP - KM - LN
[G] = G - AF - BE - CD - HP - JO - KN - LM
[H] = H - AJ - BK - CL - DM - EN - FO - GP
[J] = J - AH - BL - CK - DN - EM - FP - GO
[K] = K - AL - BH - CJ - DO - EP - FM – GN
[L] = L - AK - BJ - CH - DP - EO - FN - GM
[M] = M - AN - BO - CP - DH - EJ - FK – GL
[N] = N - AM -BP - CO - DJ - EH - FL – GK
[O] = O - AP - BM - CN - DK - EL - FH - GJ
[P] = P - AO - BN - CM - DL -EK - FJ - GH
Based on the alias structure, factor C is
confounded with AB interaction, Factor E is
confounded with AD interaction, factor F is
confounded with BD interaction and so on.
The first linear graph for L16 (figure 2)
assisted in matching factors and column and possible
interaction in the experimental matrix [3].
Figure 2. First Linear Graph for L16 Array assigns
the variables and interactions
3. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 39 | P a g e
No replicates of treatment combinations were done,
also the experiment was run in random order to
minimize the effect of extraneous variables that
might influence the results. Alpha (α) or type I error
of 0.05 is used. Alpha is the maximum acceptable
level of risk for rejecting a true null hypothesis.
Run # Factors Extinguishing
Time
Damage
percentage
T X R G Q (seconds) %
1 2 4 8 15
1 0 0 0 0 0 253 84
2 0 0 0 1 1 229 82
3 0 0 1 0 1 219 75
4 0 0 1 1 0 171 73
5 0 1 0 0 1 150 52
6 0 1 0 1 0 125 49
7 0 1 1 0 0 115 18
8 0 1 1 1 1 107 16
9 1 0 0 0 1 160 51
10 1 0 0 1 0 120 26
11 1 0 1 0 0 116 15
12 1 0 1 1 1 96 12
13 1 1 0 0 0 110 20
14 1 1 0 1 1 108 13
15 1 1 1 0 1 96 12
16 1 1 1 1 0 92 8
Table 2 L16 orthogonal array design matrix and the results
4. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 40 | P a g e
3.4 Equipment
Extinguishers: Class A&B extinguishers will
be used. (Foam Extinguisher)
The burned material: Crib of wooden sticks
will be used as a burned material.
Tray containing heptane to light the fire.
Personal protective equipment.
3.5 Experiment Assumptions
The experiments are conducted in the same
conditions, which mean that the place; the burning
material and the extinguishing method are the same
in all the trials. In addition to that, the time for
starting the extinguishing is the same.
3.6 Experiment Conditions
The test room where the experiment was
conducted was closed with the exception of a small
opening at the base of the door (Provided for
ventilation). The wood cribs description is shown in
Table 3.
Table 3: Wood Description
CLASS DIMENSIONS (m)
White Wood 0.5*0.5*0.5
The wood cribs are fixed at 50 cm above
floor level. A properly sized tray is placed beneath
the crib at 30 faraway from the wood cribs (see
Figure 3 and Figure 4). The appropriate heptane
starter charge is poured into the tray. The heptane
charge is ignited and allowed to ignite the wood crib
above [2]. The wood crib is allowed to burn for a
period of 30 seconds before extinguishing (see Figure
5).
For each run, the following steps should be
followed:
Put the wood crib at the center of the
experiment room, and place a tray full of
heptane under it to light the fire.
Ignite the heptane.
Start the extinguishing process after 30
seconds from removing the tray.
Write down the time spent in the
extinguishing process, and the percentage of
damage in the wood for each trial.
IV. Experimental Analysis and Discussion
4.1 Graphical Analysis
4.1.1 Graphical Analysis for Extinguishing Time
The main effects plot for extinguishing time
(figure 6) shows that extinguishing time decreases for
a trained and experienced employee, also it decreases
for a response to alarm for less than 30 seconds,
concluding that training, experience and response to
alarm variables are significant factors while age and
qualification factors are not as significant.
Qualification could be ignored and it may not be used
as factor to improve extinguishing time.
5. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 41 | P a g e
10
168
156
144
132
120
10 10
10
168
156
144
132
120
10
Training
Mean
Experience Response to Alarm
Age Qualification
Main Effects Plot for Extinguishing Time
Data Means
Figure 6. The main effect plot for extinguisher time.
The interactions plot for extinguishing time
(figure 7) shows that there is a significant interaction
between training and experience. The plot indicates
that there is no other interaction exists. We will only
include the Training * Experience interaction as a
term in the statistical analysis.
10 10 10 10
200
150
100
200
150
100
200
150
100
200
150
100
Training
Experience
Response to Alarm
Age
Qualification
0
1
Training
0
1
Experience
0
1
to Alarm
Response
0
1
Age
Interaction Plot for Extinguishing Time
Data Means
Figure 7. The interaction plot for extinguisher time
4.1.2 Graphical Analysis for Percent Damage
The main effects plot for the percent damage
(figure 8) shows % damage decreases for a trained
and experienced employee, also it decreases for a
response to alarm for less than 30 seconds,
concluding that training, experience and response to
alarm variables are significant factors while age and
qualification factors are not statistically significant.
10
60
50
40
30
20
10 10
10
60
50
40
30
20
10
Training
Mean
Experience Response to Alarm
Age Qualification
Main Effects Plot for % Damage
Data Means
Figure 8. Main Effects Plot for Percent Damage.
The interaction plot (figure 9) shows that
there is a significant interaction between training and
experience, and between age and qualification. We
will include the training * experience and age
qualification interactions as terms in the statistical
analysis.
10 10 10 10
70
45
20
70
45
20
70
45
20
70
45
20
Training
Experience
Response to Alarm
Age
Qualification
0
1
Training
0
1
Experience
0
1
to Alarm
Response
0
1
Age
Interaction Plot for % Damage
Data Means
Figure 9. Interactions plot for percent damage
4.2 Statistical Analysis
ANOVA or analysis of variance and
multiple regression statistical methods were used to
analyze the data generated by our experiment.
ANOVA is useful for determining the influence of
any giving input parameter from a series of
experimental results for the fire experiment. In
general, ANOVA compares the variation between
groups and the variation within samples by analyzing
their variances. It partitions the total variation into its
appropriate components[1].
Total variance = between groups variance +
variance due to the errors
Where SST = Total Sum of Squares; SSG =
Treatment Sum of Squares between the groups; SSE
= Sum of Squares of Errors. Just think of 'sums of
squares' as being a measure of variation. The method
of measuring this variation is variance, which is
standard deviation squared.
There are 3 assumptions for the ANOVA Statistical
F-test to be valid which involve the εij’s (the error
terms) and are summarized below:
1. The εij’s are normally distributed.
2. The εij’s have mean zero and a common
variance, σ2
.
3. The εij’s are independent across
observations.
Similar to Analysis of variance (ANOVA),
multiple linear regression is used to model the
relationship between response variables and one or
more independent variables and variables. The βi are
the regression parameters and ε is an error. The least
square regression method is used for fitting model.
Similar to Analysis of variance (ANOVA), multiple
linear regression is used to model the relationship
between response variables and one or more
6. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 42 | P a g e
independent variables and their relevant interactions
[1]. The model for the multiple regression equation
is: Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ………βnXn +
ε
Where y is the response or the dependent variable
and are the independent X1, X2, X3, X4 ...........and Xn
are independent variables. The βi are the regression
parameters and ε is an error. The least square
regression method is used for fitting model.
4.2.1 Statistical Analysis for Extinguishing Time
Statistical outputs for extinguishing time are
summarized in table 4 and 5. Regression analysis
provides the coefficients for the factors and their p-
values and an analysis of variance table. The order of
the coefficients by absolute value indicates the
relative importance of each factor to the response; the
factor with the biggest coefficient has the greatest
impact. The sequential sums of squares in the
analysis of variance table also indicate the relative
importance of each factor; the factor with the biggest
sum of squares has the greatest impact.
Table 4. Regression analysis for Extinguishing
Time .
The regression equation is:
Extinguishing Time = 240 - 95.0 Training - 93.8
Experience - 30.4 Response to Alarm - 21.4 Age +
7.87 Qualification + 72.3 T*X
Predictor Coef SE Coef T P
Constant 239.938 7.725 31.06 0.000
Training -95.000 8.258 -11.50 0.000
Experience -93.750 8.258 -11.35 0.000
Response to Alarm -30.375 5.839 -5.20 0.001
Age -21.375 5.839 -3.66 0.005
Qualification 7.875 5.839 1.35 0.210
T*X 72.25 11.68 6.19 0.000
S = 11.6789 R-Sq = 96.9% R-Sq(adj) = 94.8%
Table 5. Analysis of Variance for Extinguishing
Time
Analysis of Variance
Source DF SS MS F P
Regression 6 38133.9 6355.6 46.60 0.000
Residual Error 9 1227.6 136.4
Total 15 39361.4
Source DF Seq SS
Training 1 13865.1
Experience 1 13282.6
Response to Alarm 1 3690.6
Age 1 1827.6
Qualification 1 248.1
T*X 1 5220.1
The regression analysis shows that the P-
value for Training, Experience, Response to Alarm,
Age, and the Training*Experience interaction are 0,
0, 0.001, 0.005, and 0 respectively which are much
smaller than Alpha of 0.05, indicating statistical
significance, while the P-value for Qualification is
0.21 which is greater than Alpha of 0.05, indicating
non statistical significance.
The prediction equation is:
YTime = 240 – 95XT – 93.8XX – 30.4XR – 21.4XG+
7.87 XQ + 72.3XTXX… (Equation (1))
The residual plot (figure 10) indicates that there is no
violation of the analysis of variance assumptions.
The residuals are normally distributed, the residuals
have equal variances, and the residual are
independent. This concludes that our model is valid.
4.2.2 Statistical Analysis for Percent Damage
Statistical outputs for percent damage are
summarized in table 6 and 7. The regression analysis
shows that the P-value for training, experience,
response to alarm, training*experience interaction,
and age*qualification interaction are 0, 0, 0, 0, and
0.004 respectively which are much smaller than
Alpha of 0.05, indicating statistical significance,
while the P-value for age, and qualification are 0.056
and 0.379 respectively which are greater than Alpha
of 0.05, indicating non statistical significance.
Table 6. Regression analysis for % Damage.
The regression equation is
% Damage = 84.1 - 52.5 Training - 44.8 Experience
- 18.5 Response to Alarm + 4.75 Age + 13.3
Qualification + 32.0 T*X - 21.5 G*Q
Predictor Coef SE Coef T P
Constant 84.125 3.793 22.18 0.000
Training -52.500 3.793 -13.84 0.000
Experience -44.750 3.793 -11.80 0.000
Response to Alarm -18.500 2.682 -6.90 0.000
Age 4.750 3.793 1.25 0.246
Qualification 13.250 3.793 3.49 0.008
T*X 32.000 5.365 5.96 0.000
G*Q -21.500 5.365 -4.01 0.004
S = 5.36482 R-Sq = 98.1% R-Sq(adj) = 96.4%
Table 7. Analysis of Variance for % Damage.
Analysis of Variance
Source DF SS MS F P
Regression 7 11659.5 1665.6 57.87 0.000
Residual Error 8 230.2 28.8
Total 15 11889.8
Source DF Seq SS
Training 1 5329.0
Experience 1 3306.3
Response to Alarm 1 1369.0
Age 1 144.0
Qualification 1 25.0
T*X 1 1024.0
G*Q 1 462.3
7. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 43 | P a g e
The prediction equation is:
YDamage =84.1–52.5XT –44.8XX –18.5XR+ 4.75 XG+
13.3 XQ+32XT XX –21.5XGXQ ….. (Equation (3))
The residual plot (figure 11) indicates that
there is no violation of the analysis of variance
assumptions. The residuals are normally distributed,
the residuals have equal variances, and the residuals
are independent. This concludes that our model is
valid.
20100-10-20
99
90
50
10
1
Residual
Percent
25020015010050
20
10
0
-10
-20
Fitted ValueResidual
20151050-5-10-15
4.8
3.6
2.4
1.2
0.0
Residual
Frequency
16151413121110987654321
20
10
0
-10
-20
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Extinguishing Time
Figure 10. Residual analysis for extinguishing time
1050-5-10
99
90
50
10
1
Residual
Percent
806040200
5
0
-5
-10
Fitted Value
Residual
5.02.50.0-2.5-5.0-7.5-10.0
4.8
3.6
2.4
1.2
0.0
Residual
Frequency
16151413121110987654321
5
0
-5
-10
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for % Damage
Figure 11. Residual analysis for % damage model
8. Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44
www.ijera.com 44 | P a g e
4.3 Relation between the time of extinguishing
and the damage :
Correlations: Extinguishing Time,% Damage
Pearson correlation of Extiguishing Time and %
Damage = 0.945
P-Value = 0.000
260240220200180160140120100
90
80
70
60
50
40
30
20
10
0
Extiguishing Time
%Damage
Scatterplot of % Damage vs Extiguishing Time
Figure 1 : Extinguishing Time vs Damage
Percentage
Figure 12 clarifies the relation between the
time of extinguishing and the damage percentage. It
is obvious that the damage percentage increase with
increasing the time. From the results in the last
section, it is concluded that the training, the
experience , the response to alarm and the interaction
between the training and the experience have the
most influence in decreasing the percentage of
damage, the age has small influence and the
qualification approximately has no influence.
V. Conclusions
Our goal is to decrease extinguishing time
and percent damage, the factor levels should be set to
that produce the lowest mean. Examining the main
effects plots and interaction, the factor levels that
decrease extinguishing time and percent damage are
summarized in table below
Factors Level
Training 1
Experience 1
Response to Alarm 1
Age 1
Qualification 1
In conclusion:
a) The training has the highest effect in regard of
the performance of the employees in fire
extinguishing.
b) The experience factor is ranked in the second
stage according to its effect in the performance
of the employee in fire extinguishing.
c) The interaction between the training and the
experience was also a significant factor.
d) The age has small affect in the performance of
the employees in fire extinguishing.
e) The qualification’s effect on the performance of
the employees in fire extinguishing can also be
neglected.
A trained employee, less than 40 years of
age, with a bachelor degree and experience, and with
fast response leads to the best result in extinguishing
time and percent damage.
References
[1] Bala MG, Biswanath M & Sukamal G,
Taguchi Method and ANOVA: An approach
for process parameters optimization of hard
machining while machining hardened steel,
Journal of Scientific & Industrial Research,
Vol. 68, August 2009, pp. 686-695
[2] Carey, W.M., National Class A Foam
Research Project Technical Report:
Knockdown, exposure and Retention Tests.
National Fire Protection Research
Foundation,1993
[3] Local Government Group (2011) Fire safety
in purpose-built blocks of flats,
http://www.local.gov.uk/c/document_library
/get_file?uuid=71e152a6-9e0a-4810-aee6-
498167664f79&groupId=10171
[4] Peace, G. S., Taguchi Methods: a Hands-on
Approach, Addison-Wesley Publishing
Company, Inc., Massachusetts, 1992.
[5] Ross, R. J., Taguchi Techniques for Quality
Engineering, McGraw-Hill, New York,
1989.
[6] Under writers Laboratories Inc., Report of
Class A Foam Tests, Department of the
Army, Fort Belvoir, 1994.