Tolkning og bruk av Big Data i industrien

Transkript

1 NTVA Kristiansand Tolkning og bruk av Big Data i industrien Harald Martens, dr.techn. Forskningsleder, Idletechs AS, Trondheim Prof. II, Inst. teknisk kybernetikk, NTNU harald.martens@ntnu.no

2 NTVA Kristiansand Tolkning og bruk av Big Data i industrien Harald Martens, dr.techn. Forskningsleder, Idletechs AS, Trondheim Prof. II, Inst. teknisk kybernetikk, NTNU harald.martens@ntnu.no

3 NTVA Kristiansand Tolkning og bruk av Big Data i industrien Big Data Cybernetics in Trondheim: Interpretable Deep Learning, dynamic, open-ended, but with respect for the Laws of Physics Harald Martens, dr.techn. Forskningsleder, Idletechs AS, Trondheim Prof. II, Inst. teknisk kybernetikk, NTNU harald.martens@ntnu.no

4 The Math Gap House of Math House of Everything else Math gap

5 The Math Gap House of Math House of Everything Else Math gap

6 A two-way bridge across the math gap House of Math House of Everything else Math gap

7 Looking at art Photographed in 3 colours (R,G,B)

8 Looking at art Photographed in 3 colours (R,G,B) Hyperspectral image Photographed in several hundred «colours» (wavelength channels) Courtesy of NTNU Gjøvik «Colour lab» and Norweigan Electro-Optics AS

9 Looking at art Photographed in 3 colours (R,G,B) Find the different colour and shape patterns in the hyperspectral image Harald Martens & Joao Fortuna, NTNU/Idletechs AS

10 Looking at art Pixels Wavelength channels

11 Looking at nature Else Tronstad, Leksvika Photographed in 3 colours (R,G,B)

12 Looking at nature Else Tronstad, Leksvika Photographed in 3 colours (R,G,B) Photographed in several hundred «colours» (wavelength channels in vis. & NIR ) Courtesy of NM (Ingunn Burud)

13 Looking at nature Else Tronstad, Leksvika Photographed in 3 colours (R,G,B) Photographed in several hundred «colours» (wavelength channels in vis. & NIR ) Courtesy of NM (Ingunn Burud)

14 Looking at nature Else Tronstad, Leksvika Photographed in 3 colours (R,G,B) Photographed in several hundred «colours» (wavelength channels in vis. & NIR ) Courtesy of NM (Ingunn Burud)

15 Joao Fortuna and Harald Martens 2017 Looking at Nature New method for de-shadowing hyperspectral images

16 Looking at us

17 Looking at us Combine hundreds of NIR wavelength channels between 700 og 1100 nm to a few molecular patterns PCA Principal component analysis Harald Martens, Ingunn Burud & Joao Fortuna: Giving the surgeons molecular eye-sight? A pork chop measured by hyperspectral NIR camera

18 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering

19 A two-way bridge across the math gap House of Math House of Everything else Math gap

20 Two roads from question to answer in science 1. Holistic, practical, curious exploration 2. Atomistic, theoretical hypothesis testing With With permission permission from from prof. prof. Lars Lars Munck Munck

21 Two roads from question to answer in science 1. Holistic, practical, curious exploration 2. Atomistic, theoretical hypothesis testing With permission from prof. Lars Munck

22 Source of knowledge at any given moment Empirical data from the Real World Theoretical knowledge A better world (??)

23 DATA-DRIVEN Induction: holistic, exploratory? Design the observation process Measure MANY properties in MANY samples Data-modelling «Let the data talk»! Define a mechanistic model Measure FEW properties in FEW samples Test the model wrt the data Deduction: atomistic, confirmatory THEORY-DRIVEN Figure 1

24 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering Folk er forskjellige. Vi må overkomme matte-angsten.

25 time A = B C B = C = A causal phenomenon s time-dependent development + D = How Quantitative Big Data are often generated = +

26 time A = B C B = C = A causal phenomenon s time-dependent development + Its multi-channel property profile D = How Quantitative Big Data are often generated = +

27 time Response A = B C B = C Instrument channel # True properties = A causal phenomenon s time-dependent development + Its multi-channel property profile D = How Quantitative Big Data are often generated = +

28 Response time Response A = B C B = C Instrument channel # + A obs A = B C D = How Quantitative Big Data are often generated = +

29 Response time Response A = B C B = C Instrument channel # + A obs A = B C D = CAUSAL MATH MODEL: A obs = B C + D How Quantitative Big Data are often generated = +

30 Response A = B C B = C A obs A = B C + D How Quantitative Big Data are NOT analyzed = A obs +

31 Response A = B C B = C A obs A = B C + D = How Quantitative Big Data are sometimes analyzed today A obs Black box AI +

32 time Response Response Response A = B C A obs A obs = B = A = B C How Quantitative Big Data may be analyzed C PCA, as in e.g. Chemometrics: Instrument channel # + + D

33 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering. Folk er forskjellige. Vi må overkomme matte-angsten. Multivariat datamodellering er ikke vanskelig.

34 Source of knowledge at any given moment Empirical data from the Real World Theoretical knowledge A better world (??)

35 EMSCpretreated Absorbance Mean-centered Absorbance Absorbance log(1/t) Diffuse NIR spectra of powder mixtures Measured vs predicted quality A) A) Input B) Conventional Trad. Univariate calibration calibration C) Multivariate D) Multivariate calibration calibration by PLSR 1. Purely empirical math. model E) Multivariate F) Multivariate calibration preprocessing by OEMSC What is CHEMOMETRICS? A particular science culture & tool box for «soft multivariate data modelling»

36 Absorbance log(1/t) Absorbance Absorbance, best wavelength (916 nm) Example: Multivariate calibration by PLSR NIR absorbance of powders Best uni-variate calibration for analyte (Gluten) 100 Wavelengths (nm) PLS c) God s own regression garbagescores bin: The reveal Near the Infrared main selectivity wavelength problems range: Everything absorbs light everywhere! Predict Gluten content from NIR absorbance log(1/t) in new powder samples 100 Wavelengths (nm)

37 Absorbance log(1/t) Absorbance Absorbance, best wavelength (916 nm) Example: Multivariate calibration by PLSR NIR absorbance of powders Best uni-variate calibration for analyte (Gluten) 100 Wavelengths (nm) PLS c) regression scores reveal the main selectivity problems Predict Gluten content from NIR absorbance log(1/t) in new powder samples 100 Wavelengths (nm)

38 Absorbance log(1/t) Absorbance Example: Multivariate calibration by PLSR NIR absorbance of powders PLSR: Automatic combination of wavelength channels to remove selectivity problems 100 Wavelengths (nm) 100 Wavelengths (nm) PLS c) regression scores reveal the main selectivity problems Predict Gluten content from NIR absorbance log(1/t) in new powder samples

39 Absorbance log(1/t) Absorbance Example: Multivariate calibration by PLSR NIR absorbance of powders PLSR: Automatic combination of wavelength channels to remove selectivity problems 100 Wavelengths (nm) 100 Wavelengths (nm) PLS c) regression model reveals the main selectivity problems Predict Gluten content from NIR absorbance log(1/t) in new powder samples

40 Absorbance log(1/t) Absorbance Example: Multivariate calibration by PLSR NIR absorbance of powders PLSR: Automatic combination of wavelength channels to remove selectivity problems 100 Wavelengths (nm) 100 Wavelengths (nm) PLS c) regression model reveals the main selectivity problems

41 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering. Folk er forskjellige. Vi må overkomme matte-angsten. Multivariat datamodellering er ikke vanskelig. Man trenger ikke måle-selektivitet i rådata mål mange egenskaper og bruk multivariat kalibering istedet!

42 Source of knowledge at any given moment Empirical data from the Real World Theoretical knowledge A better world (??)

43 Source of knowledge at any given moment Empirical data from the Real World Theoretical knowledge A better world (??)

44 A Red Herring? Reflektert lys How does it feel to be a photon hitting the herring? Sukkertøy formet som en liten sild, i nasjonalfargene fargene rødt, hvitt og blått.

45 A Red Herring? Reflektert lys How does it feel to be a photon hitting the herring? Transmittert lys Sukkertøy formet som en liten sild, i nasjonalfargene fargene rødt, hvitt og blått. Legg merke til hvordan det hvite midtpartiet blir mørkest ved gjennomlysning altså samme mekanisme som i tordenskyen.

46 Å se hvor den andre ser How does it feel to be a photon hitting the side of eye? I motsetning til de andre primatene, som har mørkfargede øye-epler, har mennesket hvite øyeepler. Det kan henge sammen med at mennesket dermed lettere kan kommunisere retningsangivelse med bare øynene. Tordensky-mekanismen gjør at det selv hos oss blir mørkt nok inni øyet til at vi kan motta klare bilder gjennom linsa.

47 Why is the thundercloud black? Moses har ledet sitt folk tørrskodd over Rødehavet, og Farao og hans tropper drukner (Exodus 14:23-30). I bakgrunnen en herrens landeplage av et uvær. Maleren viser at tordenskyen er hvit øverst og svart nederst. Forsto han mekanismen, eller gjenga han bare det han så? Veggmaleri i det Sixtinske kapell, av Biagio d Antonio eller Cosimo Rosselli, ca 1482.

48 EMSCpretreated Absorbance Mean-centered Absorbance Absorbance log(1/t) Diffuse NIR spectra of powder mixtures Measured vs predicted quality A) A) Input B) Conventional Trad. Univariate calibration calibration C) Multivariate D) Multivariate calibration calibration by PLSR 1. Purely empirical math. model 2. Semi-causal math. model E) Multivariate F) Multivariate calibration preprocessing by OEMSC What is CHEMOMETRICS? A particular science culture & tool box for «soft multivariate data modelling»

49 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering. Folk er forskjellige. Vi må overkomme matte-angsten. Multivariat datamodellering er ikke vanskelig. Man trenger ikke måle-selektivitet i rådata mål mange egenskaper og bruk multivariat kalibering istedet!

50 New Scientist 2015 The average global temperature, estimated by five different research groups (laboratories) Data Table 130 years Year ( ) Score of component 2 Score of components Year Temp. c) % explained variance Temp. 5 variables # of components e) Correlation to Score of component 2 b) f) 100% variance explained by first 2 PCs Automatic data modelling by Principal component analysis PCA): Main covariation patterns Score of component 1 Correlation to Score of component 1

51 Three of the 5 (or ?) available variables Dots = years

52 First principal component: Mean

53 First principal component: Mean

54 Multivariate soft data-modelling gives fewer, but more sensitive alarms, for three different types of abnormalities: Raw data Many variables (temperatures) Few informative combinations of the many variables An ALIEN An EXTREMIST Færre, bedre alarmer!

55 Data driven multivariate modelling will also reveal unexpected patterns b) c) % explained variance a) Temp. Year Year e) Score of component 1 Correlation to Score of component 2 d) Score of component 2 Score of components # of components f) 100% variance explained by first 2 PCs Correlation to Score of component 1

56 Massive highdimensional computer simulations Combinations of highspeed multichannel sensors QUANTITATIVE BIG DATA In: Process industry Energy production Transportation Tele-communication Bio-medicine Data warehouse Economy/finance

57 QUANTITATIVE BIG DATA Advanced control theory/ Engineering cybernetics

58 QUANTITATIVE BIG DATA Advanced control theory/ Engineering cybernetics

59 QUANTITATIVE BIG DATA BIG DATA CYBERNETICS Advanced control theory/ Engineering cybernetics Chemometrics / multivariate soft modelling

60

61 Massive highdimensional computer simulations Combinations of highspeed multichannel sensors QUANTITATIVE BIG DATA At ITK Sept. 2017: 3 External professors (20%) 2 Full professors funded 1 Post-doc 1 PhD 7 Project students 50 Course students ++ BIG DATA CYBERNETICS Advanced control theory/ Engineering cybernetics Dept. of Engineering Cybernetics & Robotics, NTNU Chemometrics / multivariate soft modelling Metallurgical industry, biomedical etc etc Other software companies

62 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering. Folk er forskjellige. Vi må overkomme matte-angsten. Multivariat datamodellering er ikke vanskelig. Man trenger ikke måle-selektivitet i rådata mål mange egenskaper og bruk multivariat kalibering istedet! Big Data Cybernetics: Kybernetikk + Kjemometri

63 Quantitative Big Data : Many high-dimensional sensors, e.g. thermal cameras, voltage/current monitoring, accelerometers, electromagnetic field detectors, etc? Big Data Cybernetics: Utilize enormeous, «ever-lasting» streams of high-dimensional data: For automatic control For human learning + Residuals Statistical and mechanistic model improvement Comparison to prior mechanistic models

64 Quantitative Big Data: "No One Is Close to Achieving True Level 5 Autonomy" Toyota Research Institute (TRI) is investing over a billion dollars in robotics and artificial intelligence over the next five years TRI is headed by Gill Pratt, who recently gave the audience at the CES in Las Vegas a much needed reality check : (IEEE Spectrum, posted Jan ) What s interesting about this because machine learning systems, and deep learning in particular, are very high performance but don t actually explain how they figure out the answer is that getting the car to explain what it does is actually hard. Deep learning is wonderful, but deep learning doesn t guarantee that over the entire space of possible inputs, the behavior will be correct. Ensuring that that s true is very, very hard to do.

65 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering. Folk er forskjellige. Vi må overkomme matte-angsten. Multivariat datamodellering er ikke vanskelig. Man trenger ikke måle-selektivitet i rådata mål mange egenskaper og bruk multivariat kalibering istedet! Big Data Cybernetics: Kybernetikk + Kjemometri. Big Data: Blind maskinlæring kan være farlig.

66 PCA of ever-lasting data streams From raw streams of data, systematic patterns and relationships are automatically discovered and modelled. The data is stored in a highly compressed format: Sensor data: Recordings of light, sound, vibrations, images, temperatures, moisture, etc. SCADA data Computer simulation outputs Ever-lasting high-dimensional data time = Automatically displaying the essence in data + Noise Idletechs AS provides new software tools for automatic modelling & compression and for human interpretation, of multidimensional, dynamic data.

67 Thermal video A ship engine monitored by a thermal camera Example of Quantitatative Big Data Monitoring a ship engine over time: QUANTITATIVE BIG DATA! From Idletech s on-going IFU with Kongsberg Maritime, on Interpretable Deep Learning, open-ended, but with respect for the Laws of Physics

68 Thermal video New state variable,s and few, but sensitive warnings Monitoring a ship engine over time: QUANTITATIVE BIG DATA! Refolding pixels Unfolding pixels = New state variables for process control + Error warnings

69 Mange-kanals data har mye nyttig informasjon, men trenger matematisk modellering og statistisk evaluering. Folk er forskjellige. Vi må overkomme matte-angsten. Multivariat datamodellering er ikke vanskelig. Man trenger ikke måle-selektivitet i rådata mål mange egenskaper og bruk multivariat kalibering istedet! Big Data Cybernetics: Kybernetikk + Kjemometri. Big Data: Blind maskinlæring kan være farlig. Kombiner teoretisk kunnskap og massive mengder måledata.

70 s visjon: Empirical data from the The Real world World A better world! Theoretical Knowledge knowledge More knowledge

71 Interpretable Deep Learning, dynamic, openended, but with respect for the Laws of Physics

72 Since the Enlightenment: Now, with BIG DATA: Understand more! Understand less? BIG DATA: Keep humans in the loop! i.e. : We need more Mathematical Modelling, more Statistical Assessment and more Learning from Data, but less Macho Mathematics, less Gucci Statistics and less Blind Machine Learning