IntelligentAlarmClassificationBasedonDSmTAlbenaTchamovaJeanDezertAbstractInthis
paper the critical issue of alarms classi-cation and prioritization
(in terms of degree of danger) isconsideredandrealizedonthe base of
Proportional ConictRedistribution rule no.5, dened in
Dezert-Smarandache Theoryof plausible and paradoxical reasoning.
The results obtained showthestrongabilityof this
ruletotakecareinacoherent andstablewayfortheevolutionof all
possibledegreesof danger,relating to a set of a priori dened, out
of the ordinary dangerousdirections.
AcomparisonwithDempstersruleperformanceisalso provided. Dempsters
rule shows weakness in resolving thecases examined.
InEmergencycaseDempsters ruledoes notrespond to the level of
conicts between sound sources, leadingthat
waytoungroundeddecisions. Incaseof lowest dangerspriority
(perturbed Warning mode), Dempsters rule could causea false alarm
and can deect the attention from the existing realdangerous source
by assigning a wrong steering direction to thesurveillance
camera.KeywordsAlarm classication; DSmT; DST; data fusion.I.
INTRODUCTIONThe alarms classication and prioritization is a
verychallenginganddifcult task. Theencounteredoverowingamount of
alarms could become a serious source of confusionespecially in
dangerous cases, when one needs to take a properimmediateresponse.
Theproblemisreallycritical, becausethe information available for
performing alarms processing isuncertain,imprecise, evenconicting.
Therearecases, whensome of the alarms generated could be
incorrectly interpretedasfalse, increasingthechancetobeignored,
incasewhenthey are really signicant and dangerous. That way the
criticaldelay of the proper response could cause signicant
damages.Alot of work was done during the years, because
theimportance of this problem was recognized since the 1960s,
inwide world cases of surveillance: in industry (powerplants,
oilreneries), the clinical alarms in medicine, civilian and
mili-tary monitoring. Nowadays surveillance (military and
civilian)and environmental monitoring systems are characterized
withasmartoperationalcontrol, basedontheintelligentanalysisand
interpretation of alarms coming from a variety of sensorsinstalled
in the observation area. Many approaches have
beenadoptedandapplied, addressingtheproblemincommon. In[1] a
generic neuro-expert systemarchitecture for trainingneural networks
in alarmprocessing is developed, whichis satisfactory when the
training set covers enough rangeof scenarios. Anexpert
systemwithtemporal reasoningforalarm processing is proposed in [2].
Fault detection and alarmprocessing in a loop system using a fault
detection system ispresentedin[3].
In[4]theauthorsconsideramethodology,basedonbotharticial neural
networksandfuzzylogicforalarm identication. The tasks of alarm
processing, fault diag-nosis and comprehensive validation of
protection performanceare discussed and resolved in [5] using
knowledge-basedsystems andmodel-basedreasoningapproach. In[6]
alarmprioritization, using fuzzy logic is developed to prioritize
thealarmsduringalarmoodswhichwouldeasetheburdenofoperators with
meaningless or false alarms. In case of multiplesuspicious signals,
generated from a number of sensors in theobservedarea, the
problemof alarmclassicationrequiresthemost
dangerousamongthemtobecorrectlyrecognized,inorder
todecideproperlywherethevideocamerashouldbe oriented. Because of
uncertainty and conicts encounteredin signals data, one needs to
process, analyze and inter-pret correctlyintimelymannerall
suspicioussoundsignalsseparatelyat particular
sensorslevelsintheobservedarea.Such kind of conicts could weaken or
even mistake thedecisionabout the degree of danger ina critical
situation.That is why a strategy for an intelligent, scan by
scan,combination/updating of sounds data generated by each sensoris
neededinorder toprovidethesurveillancesystemwithameaningfuloutput.
Therearevariouswellknownmethodsfor combining information, which
could be applied. The mostuseduntil
nowDempster-ShaferTheory(DST)[9]proposesa suitable mathematical
model for uncertainty representation,but its weak point in
applications relates to the normalizationfactor,
whichyieldstonon-adequateresultswhensourcestocombine are highly
conicting. To overcome such drawback,weapplytheProportional Conict
RedistributionRuleno.5(PCR5), denedinDezert-SmarandacheTheory(DSmT)
ofplausibleandparadoxical reasoning[7]. It proposesapow-erful
andefcient wayfor combiningandutilizingall theavailable
information, allowing the possibility for conicts andparadoxes
between the elements of the frame of
discernment.AcomparisonwithDSTperformancebasedonDempstersrule of
combination1is alsoprovidedinorder toevaluatetheabilityof
DSmTtoassureawarenessabout
thealarmsclassicationandprioritizationincaseofsoundsourcedatadiscrepanciesandtoimprovedecision-makingprocessaboutthe
degree of danger. In section II we recall basics of DST and1This
rule is also called Dempster-Shafer rule, and denoted DS for
short.Originally published as Tchamova A., Dezert J., Intelligent
Alarm Prioritization based on DSmT, IEEE Intelligent Systems
IS2012, Sofia, Bulgaria, Sept. 6-8, 2012, and reprinted with
permission.Advances and Applications of DSmT for Information
Fusion. Collected Works. Volume 4381Dempstersrule.
BasicsofPCR5fusionruleareoutlinedinsection III. Section IV relates
to the decision making supportused in order to decide which sound
source is most dangerous.In section V, we present the problem of
alarms classicationandexaminetwosolutions tosolveit
byusingPCR5andDempsters rule. In section VI, the evaluation and
comparativeanalysis of both solutions are provided on a given
simulationscenario, that includes three sensors, generating three
types ofsignals (warning, alarm and emergency). Concluding
remarksare given in section VII.II. BASICS OF DSTDST[9]
proposesasuitablemathematical model for
un-certaintyrepresentationLet = {1, 2, . . . , n}beaframeof
discernment of a problem under consideration containing
ndistinctelementsi, i=1, . . . , n. Abasicbeliefassignment(bba,
alsocalledabeliefmassfunction) m(.): 2[0, 1]is a mapping from the
power set of (i.e. the set of subsetsof ), denoted2, to[0, 1], that
must satisfythefollowingconditions: 1) m() = 0, i.e. the mass of
empty set (impossibleevent)iszero;2)
X2 m(X)=1,i.e.themassofbeliefis normalizedtoone. m(X) represents
the mass of beliefexactly committed toX. The vacuous bba
characterizing fullignorance is dened by mv(.) : 2[0; 1] such
thatmv(X) =0if X=, andmv() =1.
Fromanybbam(.),thebelieffunctionBel(.)andtheplausibilityfunctionPl(.)aredenedas
X 2: Bel(X)=
Y |Y X m(Y )and Pl(X) =
Y |XY = m(Y ). Bel(X) and Pl(X) areclassicallyseenaslower
andupper boundsof anunknownprobabilityP(X)ofX.
Dempster-Shafer(DS)ruleofcom-bination[9] isamathematical operation,
denoted , whichcorresponds to the normalized conjunctive fusion
rule. Basedon Shafers model of the frame, the combination of
twoindependent and distinct sources of evidences characterized
bytheirbbam1(.)andm2(.)andrelatedtothesameframeofdiscernment is
dened by mDS() = 0, and X 2\{}bymDS(X) = [m1 m2](X) =m12(X)1
K12(1)wherem12(X)
X1,X22X1X2=Xm1(X1)m2(X2)
(2)correspondstotheconjunctiveconsensusonXbetweenthetwosourcesof
evidence. K12isthetotal degreeof conictbetween the two sources of
evidence dened byK12m12() =
X1,X22X1X2=m1(X1)m2(X2) (3)DSruleis commutativeandassociative.
Theweakpointof this ruleis its behavior whenK121becauseit
cangenerate unexpected(at least verydisputable) results [11].When
K12=m12() =1, the two sources are said tobeintotal conict
andtheircombinationcannot beappliedsinceDSruleismathematicallynot
denedbecauseof 0/0indeterminacy [9].III. BASICS OF PCR5 FUSION
RULEThe idea behindthe Proportional Conict Redistributionruleno.
5(see[7], Vol. 3) istotransfer conictingmasses(total or partial)
proportionally to non-empty sets involved inthemodel accordingtoall
integrityconstraints. Thegeneralprinciple of PCR rules is then to:
1) calculate the conjunctiveconsensus between the sources of
evidences; 2) calculatethe total or partial conicting masses; 3)
redistribute theconicting mass (total or partial) proportionally on
non-emptysets involved in the model according to all integrity
constraints.Under Shafers model assumption of the frame, the
PCR5combination rule for only two sources of information isdened
as:mPCR5() = 0 and X 2\ {}mPCR5(X) = m12(X)+
Y 2\{X}XY =[m1(X)2m2(Y )m1(X) + m2(Y )+m2(X)2m1(Y )m2(X) + m1(Y
)]
(4)wherem12(X)correspondstotheconjunctiveconsensusonXbetweenthetwosourcesandwhereall
denominatorsaredifferent fromzero. All
setsinvolvedintheformulaareincanonical form. All denominators are
different from zero. If adenominator is zero, that fraction is
discarded. No matter howbigor small theconictingmass is,
PCR5mathematicallydoesabetter redistributionof
theconictingmassthanDSsince PCR5 goes backwards on the tracks of
the conjunctiverule and redistributes the partial conicting masses
only to thesets involved in the conict and proportionally to their
massesput intheconict, consideringtheconjunctivenormal formof the
partial conict. PCR5 is quasi-associative and preservesthe neutral
impact of the vacuous belief assignment.IV. DECISION-MAKING
SUPPORTInthiswork, weassumeShafersmodel andweusetheclassical
Pignistic Transformation[7], [10] totake a deci-sion about the mode
of danger. The pignistic probability(Pign.Proba), also called the
betting probability (BetP) isdened for A 2byBetP(A) =
XD|X A||X| m(X) (5)where |X| denotes the cardinality ofX.V.
ALARMS CLASSIFICATION APPROACHOur approach for alarms classication
assumes all the local-ized sound sources to be subjects of
attention and investigationfor being indication of dangerous
situations. The specicattributes of input sounds, emitted by each
source, are sensorslevel processed and evaluated in timely manner
for theircontribution towards correct alarms classication (in term
ofdegreeofdanger). Theinput soundsattributesgeneratedbyeach sensor,
at each time moment (scan) concern the frequencyof intermittence,
fintand sound signal duration, Tsig. Aparticular relationship
between the specic values offintandAdvances and Applications of
DSmT for Information Fusion. Collected Works. Volume
4382associatedcorrespondingdegreeofdangerisestablished, i.eto map
input specic sensor level data into the frame ofdiscernments,
concerningthelevel of abstractionDegreeofDanger= {Emergency, Alarm,
Warning}. Thenthe processconsists intemporal sensors level
soundsignals attributeupdating on the base of PCR5 fusion rule. Our
motivation forattribute fusion is inspired from the necessity to
ascertain thedegree of danger, associated with all localized sound
sourcesseparately, inordertoquicklyfocusonthemost dangerousalarm
information and to take immediate and correct feedbackactions to
decide properly where the video camera should beoriented. The
applied algorithm considers the following
steps:Wedenetheframeofexpectedhypothesesaccordingtothe respective
degree of danger associated with the
attributessspecicvaluesasfollows: = {1=(E)mergency, 2=(A)larm,
3=(W)arning}. Thehypothesiswithahighestpriorityis Emergency,
followingbyAlarmandthenWarn-ing. These hypotheses are exclusive
andexhaustive, henceShafers model holds and we work on power-set:
2={, E, A, W, E A, E W, A W, E A W}. A rule-base is dened in order
to establish the relationshipsbetweenthesounds attributes
associatedwithall localizedsources and corresponding degrees of
danger, in the form:Rule 1: if attributes-type 1 then EmergencyRule
2: if attributes-type 2 then AlarmRule 3: if attributes-type 3 then
Warningwhere attributes types 1, 2 and 3 could be specic sounds
at-tributes values, which are informative enough to be
processedandevaluatedfor their contributiontowardscorrect
alarmsclassication.
Inthisrulebaseattributes-type1isasoundsattribute, whichistypical
for degreeof danger Emergency,attributes-type 2is typical for
Alarm, attributes-type 3forWarning.
Inourcasethefrequencyofintermittencies(ifthesignal is intermittent)
fint, associated with the localized soundsources is utilized.
Thenthefollowingspecicrule-baseisusedasaninput
interfacetomapthesoundsattributes(socalledobservations)obtainedfromall
localizedsourcesintonon-Bayesian basic belief
assignmentsmobs(.):Rule1:if fint 1Hzthenmobs(E)=0.9andmobs(E A) =
0.1.Rule 2: iffint 5Hzthenmobs(A) = 0.7,mobs(A E) =0.2 andmobs(A W)
= 0.1.Rule 3: iffint 0Hzthenmobs(W) = 0.6 andmobs(W A E) = 0.4.If
thevalueof thesoundattributereceivedis closetotheparticular sound
signal parameter for Emergency, our bbais constructed in way that
it will consider the hypothesisEmergency andalsothe reasonable
inthis case compositeproposition (EA), representing a possible
partial uncertainty.If thevalueobtainedisclosetotheparticular
soundsignalparameter for Alarm, our bba is constructed in way that
it willconsider the hypothesis Alarm itself and also the reasonable
inthat case composite propositions AE and AW.
AssigningahighermassofbelieftoA EthantoA Wistotakecareabout
thepossibilityfor Emergencycase. If thevalueobtained is close to
the particular sound signal parameter forFig. 1. Scenario.Warning,
ourbbaisconstructedinwaythat it will considerthehypothesis
WarningandalsothecompositepropositionE A W, representing the case
of full ignorance, in ordertotakecareabout possibilityfor
AlarmandespeciallyforEmergency case. All the belief masses not
already assigned tosingletons (E, A or W) are assigned to the
reasonable partialuncertainties reecting the possible noise
perturbations in theobserved information. At the very rst time
moment k =0 we start with apriori basicbelief assignment (history)
set tobeavacuousbelief assignment mhist(E A W) = 1 , since there is
noinformation about the rst detected degree of danger accordingto
sound sources. Combination of currently received measurements
bbamobs(.) (for eachof locatedsoundsources), basedontheinput
interface mapping, with a historys bba, in order toobtain estimated
bba relating to the current degree of dangerm(.) =[mhist mobs](.).
PCR5andDSaretestedintheprocess of temporal datafusiontoupdatebbas
associatedwith each sound emitter.Flagfor anespeciallyhighdegree of
danger has tobetaken, when during the a priori dened scanning
period,themaximumPignisticProbability[7]isassociatedwiththehypothesis
Emergency.For securitypurpose, it isveryimportant
tokeepupdatingsequentiallytheestimationonehasonthestateof
thetruemodes of sound emitters, even if they are in the
lowestpriority mode (i.e. in warning mode only) in order to
preventunexpected alarms changes.VI. SIMULATION SCENARIO AND
RESULTSInour simulationscenario(Fig. 1) aset of
threesensorslocatedat different
distancesfromthemicrophonearrayareinstalled in an observed area for
protection purposes, togetherwith a video camera [8]. It is
assumed, that sensors areassembled with alarmdevices, as follows:
Sensor 1 withSonitron, Sensor 2 with E2S, and Sensor 3 with System
Sensorcompanies alarmdevices. Incase of alarmevents (smoke,ame,
intrusion, etc.) the alarm devices emit powerful
soundsignalswithvariousdurationandfrequencyofintermittencedepending
on the nature of the event. dangerous signal source.These sensors
are used for the purpose of estimation
thelevelofdanger/threatforeachplacewheretheyarelocated.Data,obtainedfromeachsourceareprocessedandanalyzedat
particular sensors level independently, in consecutive timeAdvances
and Applications of DSmT for Information Fusion. Collected Works.
Volume 4383Fig. 2. Sonitron, E2S, System Sensor Sound
Characteristics.moments, with regard to all possible degrees of
danger:1= (E)mergency, 2= (A)larm, and3= (W)arning.Doing this one
could nd the rst suspicious moment, whenTable 1 Sound signal
parameters.Continuous Intermittent-I Intermittent-II(Warning)
(Alarm) (Emergency)fint= 0Hz fint= 5Hz fint= 1HzTsig= 10s Tsig= 30s
Tsig= 60sthe situation could become eventually dangerous.The sound
signals representing Warning, Alarm and Emer-gency, emitted from
alarm devices, produced by Sonitron,
E2SandSystemSensorcompaniesusedinoursimulation(Table1) are shown on
Fig. 2. The rst (left) column of Fig. 2 relatestoSonitron,
thesecondcolumntoE2S, andthethird(right)columnrelates
toSystemSensor devices. Therst rowofthisgurerepresentsthesignal
1for Warning, secondrowrepresents signal 2, for Alarm, and the last
third row representssignal 3, for Emergency case. The Alarm signal
is intermittentwith a frequency of intermittencefint= 5Hzand a
durationTsig=30s, so called type I. The Emergency sound signal
isintermittent with a frequency of intermittence fint= 1Hz
anddurationTsig= 60s, so called type II. The Warning signal
iscontinuous withfint= 0HzandTsig=
10s.Oursimulationscenarioconsidersatruedegreeofdangerassociated
with the sound sources as follows: Emergency modefortherst
soundemitter, Alarmmodeforthesecond, andWarning mode - for the
third one. The three sources are pro-cessed in parallel and because
of possible sound perturbationswe assume that possible
randomchanges canbe observedover the scans for a given mode. We
therefore introducesomeswitchesbetweenthethreemodes Emergency,
AlarmandWarningtosimulatewhat canhappeninpractice(whatwecall
groundtruthanddisplayedwithblackplotsonournext gures 3 and 4.
According to this, three main cases areestimated: The most
interesting for us it is the estimation of dangerlevel bysensor1,
associatedwithEmergencymode. Inour simulation, the The
GroundTruthassociatedwithSensor 1 considers that during scans 13
the observationsgenerated support the Emergency mode (the highest
levelof danger). Fromscan 4 to scan 6 the
observationsgeneratedsupport theWarningmode(thelowest levelof
danger). Fromscan 7toscan 30the observationsgenerated support again
Emergency mode. Such kind ofscenariois important inthe real
worldcases
becausesourcesdatacanbedeterioratedbynoiseperturbationsandthereforesomepossibleconictsarisebetweenob-servationsfromscantoscan.WeassumethataconictoccursinsoundsdatabetweenEmergencyandWarningmodes,
because it couldweakenstronglythedecisiontaken. It could become a
reason to ignore the signicanceof out of ordinary, dangerous
situation.
Thesecondinterestingcaseconcernstheestimationofprobabilities of
modes, associated with the sound emitter2workinginAlarmmode.
TheGroundTruthhasbeena little bit changed with respect to the
ground truthsimulated for sensor 1. We assume that during scans13
the observations generated support correctly theAlarmmode. Fromscan
4toscan 8theobservationsgenerated support the Emergency mode
because of noiseperturbations.
Fromscan9toscan30theobservationsgenerated support again correctly
the Alarm mode. The third interesting case concerns the estimation
of theprobabilityof modes, associatedwiththethirdemitterworking in
Warning mode. In our simulation of thiscase, we considers that
during scans 12 the observationsgenerated support correctly the
Warning mode. Fromscan 3 to scan 5 the observations generated
supportthe Emergency mode because of some possible
noiseperturbations. Fromscan6toscan30theobservationsgenerated
support again correctly the Warning mode.As a result of processing
and analyzing sounds data,obtainedfromthethreesources,
processedinparallel, oneestablishes at each scan, for each source
the Pignistic probabil-ities, associated with all the considered
modes of danger. Thedecisions should be governed at the video
camera level, takenperiodically, depending on: 1) specicities of
the video camera(timeneededtosteer
thevideocameratowardalocalizeddirection);
2)timedurationneededtoanalyzecorrectlyandreliablythesequentiallygatheredinformation.
Wechooseasareasonablesamplingperiodfor cameradecisions Tdec=20sec,
i.e. at every 10th scan, we should establish the decisionabout the
most probable mode of danger, associated with eachsoundsource, that
waytodeclaredirectionsforsteeringthevideocamera. For our scenario,
thedecisivescans will be10th, 20th, and30th. In the next two
subsections we analyzethe performances of PCR5 and DS to conclude
on their ability(or inability) tocorrectlyidentifythealarmmodes for
theprioritization purpose.A. PCR5 rule performance for danger level
estimation.Figure 3 shows the values of Pignistic Probabilities
ofeach mode (Emergency, Alarm, Warning) associated with threesound
emitters (1st source in Emergency mode, (subplot on thetop), 2nd
source in Alarm mode (subplot in the middle), and3rd source in
Warning mode, (subplot in the bottom)) duringthe all 30 scans. Each
source has been perturbed with noises inAdvances and Applications
of DSmT for Information Fusion. Collected Works. Volume 43840 5 10
15 20 25 3000.51SOURCE 1 EMERGENCY mode:PCR5 Rule Performance
Ground TruthEmergencyAlarmWarning0 5 10 15 20 25 3000.51SOURCE 2
ALARM mode:PCR5 Rule Performance Ground TruthEmergencyAlarmWarning0
5 10 15 20 25 3000.51Scan numberPignistic ProbabilitiesSOURCE 3
WARNING mode:PCR5 Rule Performance Ground
TruthEmergencyAlarmWarningFig. 3. PCR5 rule Performance for danger
level estimation.accordance with the simulated Ground Truth,
associated withparticularsoundsource.
Theseprobabilitiesareobtainedforeach source independently as a
result of sequential data fusionof mobs(.) sequence using PCR5
combinational rule. For eachsource, we analyze the probabilities of
its modes
obtainedwithBetPcomputedfromPCR5ruleandthecorrespondingdecisions
for steering the camera at scans no. 10, 20, and 30.Decision taken
by PCR5 rule at scan10:For source 1, associated with Emergency mode
(Fig. 3, top-subplot), Pign.ProbaestablishedbyPCR5at
scan10areasfollows:BetP(E) = 1.0,BetP(A) = 0, andBetP(W) = 0.During
the rst scans one hasBetP(E)