ahmad haj mosa phd defence final

Cellular Neural Networks (CNN) Based Robust Classification under Difficult Conditions with Selected Applications in Intelligent Transportation Systems

by Dipl.-‐Ing Ahmad Haj Mosa (Univ.-‐Ass.) Advisor: Univ.-‐Prof. Dr. Ing. Kyandoghere Kyamakya

ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ

Institute of Smart Systems Technologies, Transportation Informatics Group Alpen

Adria Universität Klagenfurt

Outline

• Background and Motivation

• General Objectives

• State-‐of-‐the-‐art

• CNN Framework 1: Soft Radial Basis CNN (SRB-‐CNN)

• Case study 1: Truck detection using a single presence detector

• CNN Framework 2: Support Vector Machine SRB-‐CNN (SVM-‐SRB-‐CNN)

• Case study 2: Rain drops detection

• CNN Framework 3: Online Self Adaptive CNN (OSA-‐CNN)

• Case study 3: Traffic flow time series forecast

• Case study 4: Business time series forecast

• Quality and significance of results


Ahmad Haj Mosa 04/10/2016 2

Background and Motivation

• Futures Trends of Big Data [1]:

๏ Starting from 2016, Machine Learning will have a growth rate of 65%

• It can be defined as training the machine to act without being explicitly programmed

• The main machine learning techniques are:

๏ Clustering

๏ Classification

๏ Time series forecast

๏ Regression



Machine Learning Big Data

Google Trends Interest Over Time %-‐Big Data-‐Machine Learning

2012 2013 2014 2016

100

75

50

25

0

[1] BIG DATA Present and Future: BBVA Inovation Center

• Time Series Forecast

Background and MotivationALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


• Object Classification

Feature 1 (i.e Color of an object)

Feature 2 (i.e Size of a

n ob

ject)

Class AClass B

Time

Ground truthPredicted Current Time

Observatio

ns (i.e Tem

perature)Classifier



• The high cost of data acquisition, the uncertainty in sensors and the lack of informative features bring together the following challenges:

• Classes Imbalance (CI) • Classes Overlap or nonlinearity in case of forecast (CO) • Outliers and Noise (ON) • Learning Complexity (LC) • The presence of a Stochastic Process (SP) • Low Generalisation (LG)

• These challenges are often simultaneously faced in the frame of intelligent transportation systems (ITS)

Outline














General objectives



• Propose a reliable and robust framework to solve hard “classification and prediction challenges” and validate it in the frame of four real-‐world case studies:

•Case study 1: Classification in the context of a “car/truck detection” task by involving a single presence sensor in the frame of advanced traffic management systems (ATMS) in ITS

•Case study 2: Classification in the context of a “drops detection” task in the frame of a low-‐level image processing for advanced driver assistance systems (ADAS)

•Case study 3: Prediction in the context of a “traffic flow” related time series forecast in the frame of an Adaptive Traffic Control in ITS

•Case study 4: Prediction in the context of various “business” time series forecast

Classification

Prediction

Outline
















General machine learning system architecture

Raw Input Data

Data Preprocessing

Decision Function

Output

Features Extraction

State-‐of-‐the-‐art ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Strategy 1 to cope with the challenges: Manipulation of the feature space

Related works Target challenge

• Unsupervised feature learning using Stacked Sparse Auto Encoders (SSAN) Hinton [2006]; Lee [2006] or K-‐means clustering Adam [2011]

• Features de-‐correlagon and whitening methods such as Principle Component Analysis (PCA) Le [2013] and Reconstruc\on Independent Component Analysis (RICA) Le [2011]

• Hybrid methods consider separagon of feature space into: non-‐overlapped, purely overlapped and uncertainty region Varraboot [2015]

• Manipulagng the class distribugon to overcome the imbalance problem Drummond [2003]; Laurikkala [2011]

• CO; LG; ON

• CO; LG; ON

• CO

• CI

CI: Class Imbalance, CO: Class Overlap, LG: Low Generalisation, ON: Outliers and Noise

State-‐of-‐the-‐art ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Strategy 2 for address the challenges: Adapt the Decision function

Related work Target challenge

• Probabilisgc decision funcgons such as Naive Bayes classifier Rish [2001]; Probabilis\c Neural Networks Mao [2000]

• Margin classifiers such as Support Vector Machine (SVM) Mark [2010]

• Ensemble classificagon algorithms Stefan [2016]

• Kernel Methods such as Radial Basis Func\on (RBF) Shuling [2016]

• Recurrent Neural Network (RNN) such as CNN Chua [2005] and Long Short Term Memory LSTM Graves[2016]

• Extreme Learning Machines (ELM) Yang [2016] and Echo State Network (ESN) Olga [2015]

• SP

• LG; CO

• LG; CO

• LG; CO; SP

• LG; CO; SP

• CO; SP; LC

CO: Class Overlap, LG: Low Generalisation, ON: Outliers and Noise, SP: Stochasticity, LC: Learning Complexity

Outline














CNN Background



• Cellular Neural Network (CNN) is selected to be the core of the used classifica\on pla`orm because:

๏ CNN provides easy and flexible implementability in both solware and hardware (analog and digital) Roska[1993]

๏ CNN has a huge parallelism inherent property Ho[2008]

๏ Models dynamic temporal behaviour through its internal memory Graves[2009]

๏ CNN applicagons in classificagon show a remarkable performance according to published related works Milanova[2000]; Tang[2009]; Perfem[2007]

• However, there are two limita\ons:

๏ In tradigonal CNN architectures, state and output spaces generally have idengcal dimensions Chua[2002]

๏ The single nonlinear part is the output term in the state equagons

๏ Generally only one single linear funcgon in tradigonal CNN architectures

CNN Background (traditional CNN)



+ ∫ ( )

-1Input from Neighborhood

Feedback from Neighborhood

Bias

Control Te mplate

Feedback Template

Cell OutputLocal Input• CNN is idengfied by:

๏Feedback weights ๏Control weights ๏Bias

Soft Radial Basis Cellular Neural Network (SRB-‐CNN)



Input layer

Inner layer

Output layer

+ ∫

∑

∑

∑

–1

∑

Tansig∑ sigmoid

-2 0 2

01-2 0 2

0

1

co

ro

v1

vjyi–1, j–1

y1,1y1,2

yro,co

yi+1, j+1

xi, j

yi, j

yi, j

Di.jλi.j

Ai,j

Bi.j

Ci.j

u1u2

un

v2 vg

Wi.j

∈

GRB

Soft Radial Basis Cellular Neural Network (SRB-‐CNN)



Soft Radial Basis CNN

Nonlinearity through RBF

Input layer

Inner layer

Output layer

+ ∫

∑

∑

∑

–1

∑

Tansig∑ sigmoid

-2 0 2

01-2 0 2

0

1

co

ro

v1

vjyi–1, j–1

y1,1y1,2

yro,co

yi+1, j+1

xi, j

yi, j

yi, j

Di.jλi.j

Ai,j

Bi.j

Ci.j

u1u2

un

v2 vg

Wi.j

∈

GRB

Outline














Case study 1: Truck detection using a single presence detector



Single Inductive Loops Dual Inductive Loops

Distance

TimeOccupancy Time

Time

Case study 1: Truck detection using a single presence detector



Presence Detector

Head Way

Green Light Phase Red Light Phase

Case study 1: Features Design ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


• Considering three consecugve vehicle signals, a total of four features are determined:

1.Vehicle occupancy gme

2.First derivagves and second derivagves of the occupancy gme series

Truck Pattern Car Pattern

3. Linear divergence ld

0 1 2 3 4 5

12345678

Vehicle Order of Occurrence

Occ

upan

cy D

urat

ion

driving direction

single inductive loop

α1

α2

(a) (b)

Figure 3.5: Figure (a) shows the occupancy di↵erence pattern of a car-in-the-middle pat-tern. Figure (b) shows the occupancy di↵erence pattern of a truck-in-the-middle pattern.a1 and a2 indicate the related foreword and backward slopes which are correlated to thefirst and second derivatives.

using the finite di↵erence approximation Thomas [1995].

fd =occ

tar

� occpred

tstar

� tspred

(3.1)

sd =occ

pred

� 2occtar

+ occsucc

(tstar

� tspred

) ⇤ (tssucc

� tstar

)(3.2)

where occtar

, occpred

and occsucc

indicate the occupancy time of the target, predecessor and

successor vehicles respectively, tstar

, tspred

and tssucc

indicate the time-stamp of target,

predecessor and successor vehicles respectively.

3.4.3 Linear divergence ld

This feature measures the occupancy time orthogonal divergence of the target vehicle

from the interpolation line between the successor and predecessor occupancy; see Fig. 3.6

and Fig. 3.7. The formula given in (3.3) determines the value of this feature:

ld = occtar

� [occtar

(3.3)

where

[occtar

=occ

succ

� occpred

2(3.4)

is the predicted occupancy time, lays in the middle of the connected line between the

successor and predecessor occupancy.

27

3. Linear divergence ld

(a)

0 1 2 3 4 5

12345678

Vehicle Order of OccurrenceO

ccup

ancy

Dur

atio

n

driving direction


α1

α2

(b)

Figure 3.5: Figure (a) shows the occupancy di↵erence pattern of a car-in-the-middle pat-tern. Figure (b) shows the occupancy di↵erence pattern of a truck-in-the-middle pattern.a1 and a2 indicate the related foreword and backward slopes which are correlated to thefirst and second derivatives.

using the finite di↵erence approximation Thomas [1995].

fd =occ

tar

� occpred

tstar

� tspred

(3.1)

sd =occ

pred

� 2occtar

+ occsucc

(tstar

� tspred

) ⇤ (tssucc

� tstar

)(3.2)

where occtar

, occpred

and occsucc

indicate the occupancy time of the target, predecessor and

successor vehicles respectively, tstar

, tspred

and tssucc

indicate the time-stamp of target,

predecessor and successor vehicles respectively.

3.4.3 Linear divergence ld

This feature measures the occupancy time orthogonal divergence of the target vehicle

from the interpolation line between the successor and predecessor occupancy; see Fig. 3.6

and Fig. 3.7. The formula given in (3.3) determines the value of this feature:

ld = occtar

� [occtar

(3.3)

where

[occtar

=occ

succ

� occpred

2(3.4)

is the predicted occupancy time, lays in the middle of the connected line between the

successor and predecessor occupancy.

27

Case study 1: Features Design ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


The fourth feature is:

• Linear divergence

0 1 2 3 4 5

12345678


Occ

upan

cy D

urat

ion

driving direction


0 1 2 3 4 5

12345678


Occ

upan

cy D

urat

ion

driving direction


Truck Pattern Car Pattern

/ Transportation Research Part C 00 (2016) 1–26 9

(a) (b)

Figure 6. Figure (a) shows the occupancy time orthogonal divergence ld of a car in the middle pattern. Figures (b) shows the ld of a truck in themiddle pattern.

0 1 3 4 6 7

123


Occ

upan

cy D

urat

ion

45678910111213

2 5 8 9 10 11 12 13 14

Figure 7. Linear divergence scenario of a truck-like (bigger ld values) versus passenger car (smaller ld values).

5. THE NOVEL CELLULAR NEURAL NETWORKS BASED CLASSIFICATION CONCEPT

5.1. Some general basics on CNNA cellular neural network (CNN) is modeled by a system of di↵erential equations that express the coupling be-

tween adjacent nonlinear processing units. Each unit is called a cell, similarly to its counterpart in the brain. CNNwas first proposed by Chua and Yang (1988) [34]. CNN does integrate the advantages (or strong points) and featuresof both artificial neural networks (ANN) and cellular automata (CA). CNN does however di↵ers from CA by its non-linear dynamical modeling of the relation between cells and from ANN by its local connectivity property. CNN isalso a real-time processor due to its huge parallelism capability and it can be realized/implemented either in software(some form of virtual machine, i.e. a software emulation) or in hardware and thereby either in dedicated analog cir-cuits (see CNN chips [35, 36]) or emulated on top of digital platforms like FPGA and GPU [37, 38]. Recently, somereconfigurable analog platforms like FPAA (field programmable analog array) do o↵er a further CNN implementationalternative [39]. The generally proposed state equation of a 3⇥ 3 neighborhood (or radius=1) CNN cell is given in (5)

9

Case study 1: Data Collection ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Data accusation0

0,14

0,28

0,42

0,56

0,7

4%14%19%

63%

Car (Green Phase) Truck (Green Phase)Car (Red Phase) Truck (Red Phase)

• 30 loops in one Austrian city

•A Video reference tool to verify and label the type of the vehicle

•Over 30000 samples

CI

CI: Class Imbalance

Case study 1: Data DistributionALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Feature 1 (occupancy time)

Feature 2 (First d

erivative)

Observed Challenges: CI CO SP ON LC LG

CI: Class Imbalance, CO: Class Overlap, LG: Low Generalisation, ON: Outliers and Noise, LC: Learning Complexity, SP: Stochasticity

Case study 1: Classification ArchitectureALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Vehicle in Green Phase Vehicle in Red Phase

Classifier 1 distinguish between a vehicle

in Red or Green phase

Classifier 2 distinguish between a car or

truck in a Green Phase

Classifier 3 distinguish between a car or

truck in a Red Phase

Car Truck TruckCar

Case study 1: Evaluation ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


•Benchmarking of SRB-‐CNN with a set of well known classifiers: •Radial Basis Support Vector Machine (RBF-‐SVM) •Naive Bayes (NBayes) •Decision Tree •Argficial Neural Network (ANN)

•Evaluagon metrics: Accuracy and Receiver Operator Characteris\c (ROC)

False positive rate0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

True

pos

itive

rate

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1ROC for Classifier 3 (Testing Data)

SRB-CNNRBSVMNBayesDescion TreeANN

Classifier 2 (Green Phase) Accuracy

0,91

0,917

0,924

0,931

0,938

0,945

0,952

0,959

0,966

0,973

0,98

95%95%

92%94%

98%

SRB-‐CNN RBF-‐SVM NBayesDecision Tree ANN

Classifier 3 (Red Phase) Accuracy

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1 81,4%74,3%47,1%

62,9%95,7%

SRB-‐CNN RBF-‐SVM NBayesDecision Tree ANN

ROC curve for Classifier 3 (Red Phase)

Case study 1: Concluding RemarksALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


• Highlights: ๏ A novel CNN based architecture (SRB-‐CNN) ๏ Design of a novel Truck detection framework based on SRB-‐CNN while addressing the six challenges ๏ Novel features for Truck detection using a single presence detector ๏ Benchmark: it outperforms the state-‐of-‐the-‐art classification methods

• SRB-‐CNN Framework 1: How it copes with the six classification Challenges: ๏ RICA outliers removal (ON) ๏ The novel multilayers CNN that consider both linear and nonlinear mapping (using Radial Basis Functions) (CI, CO, LG) ๏ Cascade of classifiers (LG, CO) ๏ Oscillation behaviour of CNN (CI, CO, LG, SP) ๏ Training based on Particle Swarm Optimisation (PSO) (LC)

Case study 1 has been described in a paper accepted for publication (minor review) in the journal Transportation Research Part C (Class A+)


Outline














ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZSupport Vector Machine SRB-‐CNN (SVM-‐SRB-‐

CNN)

0 0.2 0.4 0.6 0.8 1Feature 1

0.5

0.6

0.7

0.8

0.9

1

Feat

ure

2

Class1Class2SVs

0 0.2 0.4 0.6 0.8 1Feature 1

0.5

0.6

0.7

0.8

0.9

1

Feat

ure

2

Class1Class2Kmeans

Support Vector Machine SRB-‐CNN (SVM-‐SRB-‐CNN)



Image Plan

CNN

+ ∫∑

-1

∑

RBF-SVM

Saturion

-2 0 2

0

1

0 0.2 0.4 0.6 0.8 1Feature 1

0.5

0.6

0.7

0.8

0.9

1

Feat

ure

2

Class1Class2SVs

0 0.2 0.4 0.6 0.8 1Feature 1

0.5

0.6

0.7

0.8

0.9

1

Feat

ure

2

Class1Class2Kmeans

Outline














Case study 2: Rain drops detection ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Input Image Rain drops detected

CNN based Image enhancement

Schwarzlmuller [2010]

CNN based median filter Chua [1988]

Features extraction

SVM-‐CNN based rain drops detection

• The edge features Katoluas [2005]

• Hue, Saturagon and Value (HSV) color Archarya [2005]

• The histograms of oriented gradient (HOG) Dalal[2005]

• Wavelets features Tong[2004]

CI CO ON LC LGChallenges:

WU [2010] Eigen [2013]


Case study 2: Evaluation ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Process Time on GPU (510 x 585 pixels)

0

70ms

140ms

210ms

280ms

350ms

420ms

490ms

560ms

630ms

700ms 563ms676ms684ms

488ms565ms

HOG HSV HSV & HOG WaveletEdges

Accuracy rate

0,92

0,927

0,934

0,941

0,948

0,955

0,962

0,969

0,976

0,983

0,99

93,54%93,23%

96,1%95,66%

98,25%

HOG HSV HSV & HOG WaveletEdges

Case study 2: Concluding RemarksALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


• Highlights: ๏ A novel CNN based architecture (SVM-‐CNN) ๏ Design of a novel rain drops detection framework based on SVM-‐CNN while addressing the six challenges ๏ High performance detection in real time ๏ We reach an accuracy of 98% (in competing literature they reach approx. max. 85% accuracy Sugimoto [2012])

• SVM-‐CNN Framework 2 vs Classification Challenges: ๏ CNN base image filtering (ON, CO) ๏ SVM-‐CNN (CI, CO, LG) ๏ SVM training based on quadratic programming Cottle [2009] (LC) ๏ Oscillation behaviour of CNN (CI, CO, LG, SP)

Case study 2 has been published in a paper in the journal of Real Time Image Processing (April 2016) (Class A)


http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.M.%20Sugimoto.QT.&newsearch=true

Outline














Case studies 2 & 3: Time Series Forecast ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Weekend day Working day

Observed Challenges: CO SP ON LC LG

Traffic flow

Traffic flow

Weekend day Working day


The two-‐systems model of cognitive processesALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


• The decision making process of the human mind uses two different systems

• These two systems are the following Kahneman[2011]:

๏ Cogni\ve System 1: is a fast, intuigve, unconscious and emogon-‐based decision-‐making

๏ Cogni\ve System 2: is a slow, conscious and calculagon-‐based decision-‐making system

•We mimic the two-‐systems of human psychological system and design the Online Self Adapgve Cellular Neural Network (OSA-‐CNN)

•OSA-‐CNN has two interconnected sub-‐systems:

๏ System 1: is the process model (Intuigve-‐System)

๏ System 2: is the controller model (Controller-‐System)

5. MRNNAC

Controller System Intuitive

System

Real Historical Values

Predicted Values

Time Delay

-

Recent Prediction

Error

Control Action

Figure 5.1: The OSA-CNN Framework. The Intuitive-System takes the historical valuesto predict the future value of a time series. The Controller-System reads and compares thehistorical values with the corresponding prediction values and manipulate the Intuitive-System output to improve the future performance

– The current and delayed plant input, along with the delayed output, are the

inputs to this network.

– A training set containing plant input-output measurements is used to train this

network.

• Controller Network:

– It is used to drive the process plant to a target set-point following a reference

model, which models a particular trajectory that the plant output should follow

to converge to the targeted set-point.

– The target set-points input (the reference value that the plant output should

follow), along with the delayed output and inputs of the plant, are considered

to be inputs to this network.

– Once the process model network is trained, the controller network is trained by

connecting it with the process model network. Then, a time-series of a variant

set-point value is valued with its corresponding reference model to train the

controller. In this training phase, only the controller network weights need to

be learned while the process network weights are fixed from the first training

phase (training of the proceed network).

55

Online Self Adaptive Cellular Neural NetworkALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


C-CNN

I-CNN

++

-

PSO

I-LR

C-LR

Online Operating ModeOffline Training Mode

+Linear

+

++Linear

+Linear

∫Satlins

-1

+

+

∫Satlins

-1

+

+

C-LR model I-LR model

C-CNN cell state model I-CNN cell state model

Outline














Case study 3: Traffic Flow Prediction ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Description: •Dataset used for benchmarking by Caltrans Performance Measurement System (PeMS) —online available benchmark

•This database contains traffic flow data measured every 30 seconds from about 15000 traffic detectors

•The goal is to predict the next traffic flow measurement based on different gme intervals

State-‐of-‐the-‐art methods:

•Auto Regressive Moving Average

Model (ARMA) Fuller [2009]

•SVM Regression Härdle [2012]

•Bayesian Network Darwiche

[2009]

•Radial Basis Neural Network Yang

et al. [2010]

MRE for 3 min Time Interval11,33%11,3%11,29%11,23%

7,15%5,34%

OSA-‐CNN I-‐CNN ARIMA Radial Basis ANN SVM regression Bayesian Network

MRE for 5 min Time Interval

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0,1 9,05%9,15%9,07%9,01%8,83%

4,83%

MRE: Mean Relative Error

Case study 3: Traffic Flow Prediction ALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ



0

0,014

0,028

0,042

0,056

0,076,9%6,94%6,98%6,97%

5,4%

3,52%

OSA-‐CNN I-‐CNN ARIMA Radial Basis ANN SVM regression Bayesian Network


0

0,014

0,028

0,042

0,056

0,07 5,9%5,96%5,97%6,12%5,46%4,6%

OSA-‐CNN vs I-‐CNN (one step predic\on)

MRE

0%

2,25%

4,5%

6,75%

9%

3-‐min 5-‐min 10-‐min 15-‐min 30-‐min 60-‐min

3,8%3,95%4,6%

3,52%4,83%5,3% 5,51%5,01%5,46%5,4%

8,83%

7,15%

I-‐CNN OSA-‐CNN

The Mul\-‐Steps Predic\on Performance OSA-‐CNN

0%

1,75%

3,5%

5,25%

7%

2 3 4 5 6

3-‐min 5-‐min 10-‐min 15-‐min 60-‐minAggregation Level

Number of steps

Outline














Case study 4: NN3 Business time series competition



Description: •It consists of 111 \me series with a one-‐month gme interval. Represents different pracgcal business case studies .

• The objecgve of this compeggon (NN3) is making a mulg-‐step predicgon of the next 18 observa\ons (18 month)

•A variety of researchers invesggated their proposed method using NN3

Kamel (Gaussian Process)Dyakonov (KNN)

Chen (ERT)Flores (Genegc Algorithem)

Yan (GRANN)Illies (ESN)

I-‐CNNRahman (LENN)

OSA-‐CNN 13,18%14,56%

14,95%15,18%

15,8%

16,13%

16,55%

16,57%

16,92%

SMAPE performance over NN3 time series

State-‐of-‐the-‐art methods:

• Layered Ensemble of multi-‐layers

Neural Networks (LENN) Rahman

[2016]

• Gaussian Process Kamel [2007]

• K Nearest Neighbours (KNN)

Dyakonov [2007]

• Ensemble Regression Tree (ERT) Chen

[2007]

• Automated Linear Model with Self

Adaptive Genetic Algorithm Flores

[2007]

• Generalised Regression Neural

Network (GRANN) YAN [2012]

• Echo State Network (ESN) Ilies [2007]

SMAPE: Symmetric Mean Absolute Percentage Error

—Target —OSA-‐CNN

Case study 4: NN3 Business time series competition



Case study 3 & 4: Concluding RemarksALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


• Highlights: ๏ A novel CNN based architecture is developed (OSA-‐CNN) ๏ Time series forecast framework based on OSA-‐CNN is designed while addressing the six challenges ๏ OSA-‐CNN clearly outperforms the benchmark from the state-‐of-‐the-‐art methods (NN3 competition)

• OSA-‐CNN Framework 3 vs Forecast Challenges: ๏ RICA whitening (ON) ๏ OSA-‐CNN (LC, LG) ๏ OSA-‐CNN based on ESN (LC, CO, LG) ๏ Oscillation behaviour of CNN (CO, LG, SP)

Case study 3 has been submitted in a paper for the journal of IEEE Transactions on Neural Networks and Learning Systems (under review since May 2016) (Class A+)

Outline














Quality and significance of the resultsALPEN-ADRIA UNIVERSITÄT KLAGENFURT| WIEN GRAZ


Case Study Challenge Framework RICA

whitening (ON)

The novel multilayers CNN (CI, CO, LG)

RBF (CO, LG, SP)

K-‐means (CO, LG)

Support Vectors (CO, LG, LC)

PSO(LC, CI) ESN(LC)

Two-‐Systems (LC, LG)

Limitations

Truck Detection

CI,CO,ON LC,LG,SP SRB-‐CNN ° ° ° ° ° Not self

adaptive

Rain Drops Detection

CI,CO,ON LC,LG SVM-‐CNN ° ° ° ° °

Not self adaptive + Just for

classification

Traffic Flow Forecast

CO,ON LC,LG,SP OSA-‐CNN ° ° ° ° ° Just for

Forecast

Business Time Series Forecast

CO,ON LC,LG,SP OSA-‐CNN ° ° ° ° ° Just for

Forecast


Dissemination and publication of the results: Patents and Journal Papers



Publication Type Submitted at Process

Case Study Title

Patent EU Patent Granted 1Quality determination in data acquisition Patent No. EP2790165

Patent German Patent Submitted 3Steuervorrichtung und Steuerverfahren für eine Verkehrssteuer-‐

Anlage, und Steuersystem

Journal (Class A)

Journal of Real-‐time Image Processing Published 2

Real-‐Time Raindrop Detection based on Cellular Neural Networks for ADAS

Journal (Class A+)

Transportation Research Part C Minor revision 1

Soft Radial Basis Cellular Neural Network (SRB-‐CNN) based Robust Low-‐Cost Truck Detection using a Single Presence

Detection Sensor

Journal (Class A+)

IEEE Transactions on Neural Network and Learning systems

Under review since 5 months

3Online Self-‐Adaptive Cellular Neural Network Architecture for

Robust Time-‐Series Forecast

Dissemination and publication of the results: Conference Papers and book chapters



• Fadi Al Machot, Ahmad Haj Mosa, Kusai Dabbour, Alireza Fasih, Christoph Scharzmuller, Mouhannad Ali, Kyandoghere Kyamakya, A novel real-‐gme emogon detecgon system from audio streams based on bayesian quadragc discriminate classifier for ADAS,” Nonlinear Dynamics and Synchronizagon (INDS) 2016, Pages 1:5

• Fadi Al Machot, Ahmad Haj Mosa, Alireza Fasih, Christopher Schwarzlmu l̈ler, Mouhanndad Ali and Kyandoghere Kyamakya, “A Novel Real-‐Time Emogon Detecgon System for Advanced Driver Assistance Systems,” Unger H., Kyamakya K., Kacprzyk (Eds.) J. (Hrsg.): Autonomous Systems: Developments and Trends, Springer Verlag GmbH, Berlin, Heidelberg, New York 2011, Pages 267 -‐ 276.

• Ahmad Haj Mosa, Fadi Al Machot, Alireza Fasih, Fidaa Al Machot, Kyandoghere Kyamakya, “Origin Desgnagon Esgmator Based on Hidden Markov Models for Adapgve Traffic Control,” IFAC Proceedings Volumes, Volume 45, Issue 4, 2012, Pages 92-‐96.

• Ahmad Haj Mosa, Kyandoghere Kyamakya, Mouhannad Ali, Fadi Al Machot, Jean Chamberlain Chedjou, “Neurocompugng-‐based Matrix Inversion Involving Cellular Neural Networks: Black-‐box Training Concept”, Autonomous Systems 2015, Volume 842, Pages 119 -‐ 132.

• Mouhannad Ali, Ahmad Haj Mosa, Fadi Al Machot, Kyandoghere Kyamakya, “A Novel Real-‐Time EEG-‐Based Emogon Recognigon System for Advanced Driver Assistance Systems (ADAS),” Proceedings of the INDS 2015, Pages 9 -‐ 13.

• Ahmad Haj Mosa, Mouhannad Ali, Fadi Al Machot, Kyandoghere Kyamakya, “A Computerized Method to Diagnose Strabismus,” Proceedings of the 7th GI Workshop Autonomous Systems 2014, Volume 835, Pages 209 -‐ 220.

• Mouhannad Ali, Fadi Al Machot, Ahmad Haj Mosa, Patrik Grausberg, Nkiediel Alain Akwir, Baraka Olivier Mushage, Kyandoghere Kyamakya, “A Review of Object Classificagon for Video Surveillance Systems,” Proceedings of the 7th GI Workshop Autonomous Systems 2014, Volume 835, Pages 197 -‐ 208.

• Ahmad Haj Mosa, Mouhannad Ali, Kyandoghere Kyamakya, “A Computerized Method to Diagnose Strabismus based on A Novel Method for Pupil Segmentagon”, Proceedings of the Internagonal Symposium on Theoregcal Electrical Engineering (ISTET 2013), University of West Bohemia in Pilsen, Faculty of Electrical Engineering, Pilsen, Juni 2013, S. 2.

• Ahmad Haj Mosa, Jean Chamberlain Chedjou, Mouhannad Ali, Kyandoghere Kyamakya, “Input Variant Pargcle Swarm Opgmizagon for Solving Ordinary and Pargal Differengal Equagons with Constraints”, Proceedings of the Internagonal Symposium on Theoregcal Electrical Engineering (ISTET 2013), Pages 2.

• Ahmad Haj Mosa, Mouhannad Ali, Jean Chamberlain Chedjou, Kyandoghere Kyamakya, “OD Matrix Esgmagon of a Complex Juncgon based on Pargcle Swarm Opgmizagon," Proceedings of the 13th Internagonal Conference on Innovagve Internet Community Systems and the Internagonal Workshop on Autonomeaous Systems 2013, Volume 826, Pages 282 -‐ 288.

Thank you for your attention !!

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