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Nuclear and Chemical data with Plug-Unplug Systematics [MQ2 MQ3 MQ4 MQ5 MQ6 MQ7 MQ8 MQ9 MQ131 MQ135 MQ136 MQ137 MQ303A MQ309A Geiger Counter] Multi-Purpose that can configure with SQL and PHP, save data, do data science with Python, color scale with Lidar, deep learning with yolov9, objects with Pixy2 and location with GPS system Discovery Vehicle.

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Adjustable with Plug-Unplug Systematics Multi-Purpose Modular Discovery Vehicle with Nuclear and Chemical Data Calculation System

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SpaceRover

Discovery Vehicle

Nuclear and Chemical Data Calculation System with Plug-Unplug Systematics

Adjustable Data System

MQ-135

MQ-135

MQ-2

MQ-2

MQ-3

MQ-3

MQ-4

MQ-4

MQ-5

MQ-5

MQ-6

MQ-6

MQ-7

MQ-7

MQ-8

MQ-8

MQ-9

MQ-9

MQ131

MQ131

MQ-136

MQ-136

MQ-137

MQ-137

MQ303A

MQ303A

MQ307A

MQ307A

MQ309A

MQ309A

Google Maps with Data Calculation System

GoogleMaps

3D Lidar Scale with 3D lidar Data Mapping System

tab 3d-lidar-scale lid4 lid1 lid3 lid6 lid5 lid2

Saving Values to the Database with Plug-Unplug Feature

sqlfordata

SQL for MQ-135

sqlmq135

SQL for 3D lidar Data Mapping System

sqllidar

Control DiscoveryVechile

Real-time Object Tracking With AI Based Pixy2

pixy2images

// The image above is shown as an example

// The robot can be used in both manual and object tracking mode. (motor-control && controller)

Data Science and Regression

Formulla

  1. ppm = a*ratio^b (a: valuea b: valueb)
  2. ppm = 10^[(log10(ratio)-b)/m] (m: logm b: logb)

If R^2 equals 1 :

a*ratio^b = 10^[(log10(ratio)-b)/m]
logm = valueb, logb = log10(valuea)

1]

loghello

[(1,10), (2,4), (3,3)]

loge(b) = ln(b)

(ln(1),ln(10)) for ≈ (0,2.3026)

(ln(2),ln(4)) ≈ (0.6931,1.3863) and

(ln(3),ln(3)) ≈ (1.0986,1.0986)

b = ∑ i=1 n (x i − x ˉ ) 2 ∑ i=1 n (xi − xˉ)(yi−yˉ)

ln(x):(0,0.6931,1.0986)ln(y):(2.3026,1.3863,1.0986)ln(y)ˉ=(2.3026+1.3863+1.0986)/3≈1.5958

ln(x)ˉ=(0+0.6931+1.0986)/3≈0.5972

b = (0−0.5972)(2.3026−1.5958)+(0.6931−0.5972)(1.3863−1.5958)+(1.0986−0.5972)(1.0986−1.5958)/(0−0.5972)^2+(0.6931−0.5972)^2+(1.0986−0.5972)^2 ≈ -1.2

ln(a) = − ln ˉ (y) - b ln ˉ (x) ≈ 1.5958−(−1.2)⋅0.5972≈2.31244

a=e^2.31244 ≈ 9.947

b ≈ -1.2

a ≈ 9.947

2]

y = mx+ n
n = b
log10(y) = m*log10(x) + b

-b = m*log10(x) - log10(y)

last b = log10(y) - m*log10(x)

m = (y - y0) / (x - x0)

m = (log10(y) - log10(y0)) / (log10(x) - log10(x0))

if y= a*x^b:

last m = log10(y/y0) / log10(x/x0)

m = slope of the line

b = intersection point

m = log10(y/y0) / log10(x/x0)

b = log10(y) - m*log10(x)

    if r_squared >= 0.9995:
        print("R-squared value for {gas name} is above 0.9995, plotting against first and last values.")
        
        x0, y0 = x[0], y[0]
        xn, yn = x[-1], y[-1]
        b = np.log10(yn/y0) / np.log10(xn/x0)
        a = 10**(np.log10(yn) - b * np.log10(xn))
        b2 = np.log10(yn) - b * np.log10(xn)
        b2_rounded = round(b2, 4)
        a_rounded = round(a, 4)
        b_rounded = round(b, 4)

The first formula is determined according to all points (OldCurve.py, OldCurve), while the second formula is determined according to the first and last point. Therefore, in order to collect them all in the same formula and to increase the accuracy rate, we used the method in the second formula and took the logarithm (if R^2 = 1 (%100) always: logm = valueb, logb = log10(valuea)) for slopes greater than 99.95% and collected them all in the first formula, thus we increased the accuracy rate without having to use 2 different formulas (Regression.py, NewCurve).

DataScience

MQDataScience

MQ2datascience MQ3datascience MQ4datascience MQ5datascience MQ6datascience MQ7datascience MQ8datascience MQ9datascience MQ131datascience MQ135datascience MQ136datascience MQ137datascience MQ303Adatascience MQ307Adatascience MQ309Adatascience

RadioactivityDataScience

LowGeigerCounterScience MediumGeigerCounterScience HighGeigerCounterScience SpecialGeigerCounterScience

SpaceDataScience

SpaceDataScience

MQ-2

NewCurve:

MQ2curve

OldCurve:

MQ2curve

DataScience:

MQ2Science

MQ-3

NewCurve:

MQ3curve

OldCurve:

MQ3curve

DataScience:

MQ3Science

MQ-4

NewCurve:

MQ4curve

OldCurve:

MQ4curve

DataScience:

MQ4Science

MQ-5

NewCurve:

MQ5curve

OldCurve:

MQ5curve

DataScience:

MQ5Science

MQ-6

NewCurve:

MQ6curve

OldCurve:

MQ6curve

DataScience:

MQ6Science

MQ-7

NewCurve:

MQ7curve

OldCurve:

MQ7curve

DataScience:

MQ7Science

MQ-8

NewCurve:

MQ8curve

OldCurve:

MQ8curve

DataScience:

MQ8Science

MQ-9

NewCurve:

MQ9curve

OldCurve:

MQ9curve

DataScience:

MQ9Science

MQ131

NewCurve:

MQ131curve

OldCurve:

MQ131curve

DataScience:

MQ131Science

MQ-135

NewCurve:

MQ135curve

OldCurve:

MQ135curve

DataScience:

MQ135Science

MQ-136

NewCurve:

MQ136curve

OldCurve:

MQ136curve

DataScience:

MQ136Science

MQ-137

NewCurve:

MQ137curve

OldCurve:

MQ137curve

DataScience:

MQ137Science

MQ303A

NewCurve:

MQ303Acurve

OldCurve:

MQ303Acurve

DataScience:

MQ303AScience

MQ307A

NewCurve:

MQ307AScience

OldCurve:

MQ307AScience

DataScience:

MQ307AScience

MQ309A

NewCurve:

MQ309Acurve

OldCurve:

MQ309Acurve

DataScience:

MQ309AScience

Deep-Learning Platform Flask System with yolov9

flask

Constellation:

space1) space2

Animal:

animal

Isohips:

isohipsanimal

About

Nuclear and Chemical data with Plug-Unplug Systematics [MQ2 MQ3 MQ4 MQ5 MQ6 MQ7 MQ8 MQ9 MQ131 MQ135 MQ136 MQ137 MQ303A MQ309A Geiger Counter] Multi-Purpose that can configure with SQL and PHP, save data, do data science with Python, color scale with Lidar, deep learning with yolov9, objects with Pixy2 and location with GPS system Discovery Vehicle.

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