A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells
Abstract
:1. Introduction
- Determining the vehicle requirements;
- Determining the required number of AGVs;
- Routing the vehicles;
- Optimizing the guide track;
- Minimizing the downtime;
- Managing the battery.
- Improving the battery charging and discharging processes;
- Analyzing battery ageing and degradation and assessing the condition of its mechanisms;
- Investigating the chemical processes within the batteries;
- Combining the use of batteries and super capacitors;
- Optimizing the logistics;
- Improving the utilization of multiple AGVs and the use of their batteries as mobile energy storage;
- Applying machine learning (ML) techniques to the aforementioned problems.
- The newly developed neural network-based segmental AGV battery voltage drop prediction method identifies and recovers any partially lost data, establishes correlations between the parameters, and predicts the battery voltage drop within a single unit using a neural network;
- On the example of the AGV Formica 1, the high efficiency of the proposed methodology for the medium- and short-term forecasting of the battery voltage drop is demonstrated;
- An increase in the accuracy of predicting the voltage drop of batteries for different types of AGVs compared to existing methods is experimentally established.
2. State-of-the-Art
3. Materials and Methods
- Collecting the AGV data;
- Eliminating any spontaneous outliers in a signal;
- Analyzing the data using Pearson’s correlation coefficient;
- Data mining;
- Training and testing the DNN prediction model.
3.1. Experimental Setup
3.2. Collecting and Analyzing the AGV Data
3.2.1. Collecting the AGV Data
- Weight statuses—front left strain gauge weight;
- Weight statuses—front right strain gauge weight;
- Weight statuses—rear left strain gauge weight;
- Weight statuses—rear right strain gauge weight.
3.2.2. Eliminating Any Spontaneous Outliers in the Signals
3.2.3. Data Analysis
- Count 18,424.000000;
- Mean 46,914.052866;
- Std 1901.390125;
- Min 42,830.000000;
- 25% 45,360.000000;
- 50% 47,020.000000;
- 75% 48,520.000000;
- Max 50,200.000000.
- The signal was probably not white noise;
- The values in the lags were correlated with each other;
- The histogram of the Gaussian distribution;
- The mean and variance changed over time.
3.3. Data Mining
- Segment—number of the specified segment;
- Duration—the average duration of the presence of the AGV in a given segment;
- Samples—the average sample count for a given segment;
- Voltage count delta—the mathematical expectation of the battery voltage drop after one passage of a given segment;
- Voltage delta variance—the variance of the battery voltage drop after one passage of a given segment;
- Mass—the mass carried by the AGV on a given segment;
- Start segment voltage—the averaged battery voltage at the beginning of the movement in a given segment.
4. Modeling and Results
4.1. Selecting the Type of Artificial Neural Network
4.2. DNN Prediction Model
5. Comparison and Discussion
- OPC UA with a per-minute averaging;
- With the elimination of spontaneous outliers in signals;
- With the developed data mining methodology.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
B -> A Path through the Nine Segments: 056 020 048 052 016 044 012 036 004 | ||||||
Parameters | Origin Standard Deviation | Origin Dispersion | Origin Min Value | Origin Max Value | Origin Swing | Origin Mean Absolute Linear Deviation |
Battery cell voltage | 291.2154 | 84,806.4533 | 41,670.0000 | 42,680.0000 | 1010.0000 | 251.8642 |
ActualSpeed_L | 174.0312 | 30,286.8838 | −241.0000 | 244.0000 | 485.0000 | 160.4683 |
ActualSpeed_R | 185.5523 | 34,429.6773 | −278.0000 | 282.0000 | 560.0000 | 173.390411 |
Current segment | 21.4530 | 460.2346 | 4.000000 | 63.0000 | 59.0000 | 19.7064 |
Speed | 0.2284 | 0.0522 | −0.3318 | 0.3204 | 0.6522 | 0.2137 |
Cumulative energy consumption | 153.2120 | 23,473.9367 | 887.0000 | 1420.0000 | 533.0000 | 132.8079 |
Momentary current consumption | 505.1302 | 255,156.5790 | 4090.0000 | 8270.0000 | 4180.0000 | 244.0466 |
Momentary energy consumption | 77.6645 | 6031.7775 | 703.0000 | 1377.0000 | 674.0000 | 36.2756 |
Momentary power consumption | 21.1794 | 448.5675 | 174.0000 | 353.0000 | 179.0000 | 10.2396 |
Mass | 0.000 | 0.0000 | 200.0000 | 200.0000 | 0.0000 | 0.0000 |
C -> B Path through the Seven Segments 041 009 019 055 051 023 059 | ||||||
Battery cell voltage | 291.2154 | 84,806.4533 | 41,670.0000 | 42,680.0000 | 1010.0000 | 251.8642 |
ActualSpeed_L | 174.0312 | 30,286.8838 | −241.0000 | 244.0000 | 485.0000 | 160.4683 |
ActualSpeed_R | 185.5523 | 34,429.6773 | −278.0000 | 282.0000 | 560.0000 | 173.3904 |
Current segment | 21.4530 | 460.2346 | 4.0000 | 63.0000 | 59.0000 | 19.7064 |
Speed | 0.2284 | 0.0522 | −0.3318 | 0.3204 | 0.6522 | 0.2137 |
Cumulative energy consumption | 153.2120 | 23,473.9367 | 887.0000 | 1420.0000 | 533.0000 | 132.8079 |
Momentary current consumption | 505.1302 | 255,156.5790 | 4090.0000 | 8270.0000 | 4180.0000 | 244.0466 |
Momentary energy consumption | 77.6645 | 6031.7775 | 703.0000 | 1377.0000 | 674.0000 | 36.2756 |
Momentary power consumption | 21.1794 | 448.5675 | 174.0000 | 353.0000 | 179.0000 | 10.239657 |
Mass | 0.0000 | 0.0000 | 200.0000 | 200.0000 | 0.0000 | 0.0000 |
Battery Cell Voltage | Actual Speed L | Actual Speed R | Speed | Cumulative Energy Consumption | Momentary Current Consumption | Momentary Energy Consumption | Momentary Power Consumption | |
---|---|---|---|---|---|---|---|---|
B -> A Path through the nine segments: 056 020 048 052 016 044 012 036 004 Pearson | ||||||||
Battery cell voltage | 1.0000 | −0.0190 | −0.0187 | −0.0196 | −0.9957 | −0.1614 | −0.0555 | −0.0587 |
ActualSpeed_L | −0.0190 | 1.0000 | 0.9672 | 0.9907 | 0.0184 | −0.0060 | −0.0165 | −0.0082 |
ActualSpeed_R | −0.0187 | 0.9672 | 1.0000 | 0.9883 | 0.0186 | −0.0088 | −0.0199 | −0.0111 |
Speed | −0.0196 | 0.9907 | 0.9883 | 1.0000 | 0.0193 | −0.0072 | −0.0184 | −0.0096 |
Cumulative energy consumption | −0.9957 | 0.0184 | 0.0186 | 0.0193 | 1.0000 | 0.1434 | 0.0387 | 0.0411 |
Momentary current consumption | −0.1614 | −0.0060 | −0.0088 | −0.0072 | 0.1434 | 1.0000 | 0.8907 | 0.9942 |
Momentary energy consumption | −0.0555 | −0.0165 | −0.0199 | −0.0184 | 0.0387 | 0.8907 | 1.0000 | 0.8959 |
Momentary power consumption | −0.0587 | −0.0082 | −0.0111 | −0.0096 | 0.0411 | 0.9942 | 0.8959 | 1.0000 |
Spearman | ||||||||
Battery cell voltage | 1.0000 | −0.0146 | −0.0159 | −0.0156 | −0.9996 | −0.2043 | 0.0876 | 0.0549 |
ActualSpeed_L | −0.0146 | 1.0000 | 0.9114 | 0.9693 | 0.0154 | −0.0153 | −0.0410 | −0.0251 |
ActualSpeed_R | −0.0159 | 0.9114 | 1.0000 | 0.9527 | 0.0168 | −0.0312 | −0.0600 | −0.0400 |
Speed | −0.0156 | 0.9693 | 0.9527 | 1.0000 | 0.0165 | −0.0170 | −0.0474 | −0.0265 |
Cumulative energy consumption | −0.9996 | 0.0154 | 0.0168 | 0.0165 | 1.0000 | 0.1930 | −0.0990 | −0.0669 |
Momentary current consumption | −0.2043 | −0.0153 | −0.0312 | −0.0170 | 0.1930 | 1.0000 | 0.6642 | 0.9523 |
Momentary energy consumption | 0.0876 | −0.0410 | −0.0600 | −0.0474 | −0.0990 | 0.6642 | 1.0000 | 0.7081 |
Momentary power consumption | 0.0549 | −0.0251 | −0.0400 | −0.0265 | −0.0669 | 0.9523 | 0.7081 | 1.0000 |
Kendall | ||||||||
Battery cell voltage | 1.0000 | −0.0109 | −0.0115 | −0.0116 | −0.9895 | −0.1420 | 0.0605 | 0.0381 |
ActualSpeed_L | −0.0109 | 1.0000 | 0.7137 | 0.8447 | 0.0120 | −0.0117 | −0.0285 | −0.0180 |
ActualSpeed_R | −0.0115 | 0.7137 | 1.0000 | 0.7950 | 0.0127 | −0.0188 | −0.0385 | −0.0243 |
Speed | −0.0116 | 0.8447 | 0.7950 | 1.0000 | 0.0125 | −0.0098 | −0.0298 | −0.0160 |
Cumulative energy consumption | −0.9895 | 0.0120 | 0.0127 | 0.0125 | 1.0000 | 0.1331 | −0.0682 | −0.0461 |
Momentary current consumption | −0.1420 | −0.0117 | −0.0188 | −0.0098 | 0.1331 | 1.0000 | 0.5217 | 0.8525 |
Momentary energy consumption | 0.0605 | −0.0285 | −0.0385 | −0.0298 | −0.0682 | 0.5217 | 1.0000 | 0.5635 |
Momentary power consumption | 0.0381 | −0.0180 | −0.0243 | −0.0160 | −0.0461 | 0.8525 | 0.5635 | 1.0000 |
C -> B Path through the seven segments 041 009 019 055 051 023 059 Pearson | ||||||||
Battery cell voltage | 1.0000 | −0.0190 | −0.0187 | −0.0196 | −0.9957 | −0.1614 | −0.0555 | −0.0587 |
ActualSpeed_L | −0.0190 | 1.0000 | 0.9672 | 0.9907 | 0.0184 | −0.0060 | −0.0165 | −0.0082 |
ActualSpeed_R | −0.0187 | 0.9672 | 1.0000 | 0.9883 | 0.0186 | −0.0088 | −0.0199 | −0.0111 |
Speed | −0.0196 | 0.9907 | 0.9883 | 1.0000 | 0.0193 | −0.0072 | −0.0184 | −0.0096 |
Cumulative energy consumption | −0.9957 | 0.0184 | 0.0186 | 0.0193 | 1.0000 | 0.1434 | 0.0387 | 0.0411 |
Momentary current consumption | −0.1614 | −0.0060 | −0.0088 | −0.0072 | 0.1434 | 1.0000 | 0.8907 | 0.9942 |
Momentary energy consumption | −0.0555 | −0.0165 | −0.0199 | −0.0184 | 0.0387 | 0.8907 | 1.0000 | 0.8959 |
Momentary power consumption | −0.0587 | −0.0082 | −0.0111 | −0.0096 | 0.0411 | 0.9942 | 0.8959 | 1.0000 |
Spearman | ||||||||
Battery cell voltage | 1.0000 | −0.0146 | −0.0159 | −0.0156 | −0.9996 | −0.2043 | 0.0876 | 0.0549 |
ActualSpeed_L | −0.0146 | 1.0000 | 0.9114 | 0.9693 | 0.0154 | −0.0153 | −0.0410 | −0.0251 |
ActualSpeed_R | −0.0159 | 0.9114 | 1.0000 | 0.9527 | 0.0168 | −0.0312 | −0.0600 | −0.0400 |
Speed | −0.0156 | 0.9693 | 0.9527 | 1.0000 | 0.0165 | −0.0170 | −0.0474 | −0.0265 |
Cumulative energy consumption | −0.9996 | 0.0154 | 0.0168 | 0.0165 | 1.0000 | 0.1930 | −0.0990 | −0.0669 |
Momentary current consumption | −0.2043 | −0.0153 | −0.0312 | −0.0170 | 0.1930 | 1.0000 | 0.6642 | 0.9523 |
Momentary energy consumption | 0.0876 | −0.0410 | −0.0600 | −0.0474 | −0.0990 | 0.6642 | 1.0000 | 0.7081 |
Momentary power consumption | 0.0549 | −0.0251 | −0.0400 | −0.0265 | −0.0669 | 0.9523 | 0.7081 | 1.0000 |
Kendall | ||||||||
Battery cell voltage | 1.0000 | −0.0109 | −0.0115 | −0.0116 | −0.9895 | −0.1420 | 0.0605 | 0.0381 |
ActualSpeed_L | −0.0109 | 1.0000 | 0.7137 | 0.8447 | 0.0120 | −0.0117 | −0.0285 | −0.0180 |
ActualSpeed_R | −0.0115 | 0.7137 | 1.0000 | 0.7950 | 0.0127 | −0.0188 | −0.0385 | −0.0243 |
Speed | −0.0116 | 0.8447 | 0.7950 | 1.0000 | 0.0125 | −0.0098 | −0.0298 | −0.0160 |
Cumulative energy consumption | −0.9895 | 0.0120 | 0.0127 | 0.0125 | 1.0000 | 0.1331 | −0.0682 | −0.0461 |
Momentary current consumption | −0.1420 | −0.0117 | −0.0188 | −0.0098 | 0.1331 | 1.0000 | 0.5217 | 0.8525 |
Momentary energy consumption | 0.0605 | −0.0285 | −0.0385 | −0.0298 | −0.0682 | 0.5217 | 1.0000 | 0.5635 |
Momentary power consumption | 0.0381 | −0.0180 | −0.0243 | −0.0160 | −0.0461 | 0.8525 | 0.5635 | 1.0000 |
Parameter | Type | Comments |
---|---|---|
Timestamp | Date and Time | DTL Format by Siemens. Set before the TCP frame was sent |
Weight statuses—front left strain gauge weight | UINT | Payload on strain gauge in kgs |
Weight statuses—front right strain gauge weight | UINT | Payload on strain gauge in kgs |
Weight statuses—rear left strain gauge weight | UINT | Payload on strain gauge in kgs |
Weight statuses—rear right strain gauge weight | UINT | Payload on strain gauge in kgs |
SOC—State Of Charge | UINT | Actual state of the battery in % |
Battery cell voltage | UINT | Measured by BMS or voltage divider. mV |
ActualSpeed_L | INT | Actual speed of left motor |
ActualSpeed_R | INT | Actual speed of right motor |
Current segment | UD Int | The ID of the segment on which the AGV is currently located |
Momentary current consumption | UINT | Measured by a BMS or Hall sensor at load output on battery. mA |
Momentary energy consumption | UINT | Calculated by PLC based on momentary power consumption and time. Ws |
Momentary power consumption | UINT | Calculated by PLC based on momentary current and voltage. W |
Cumulative energy consumption | UINT | Calculated by PLC based on momentary energy consumption and time. Wh |
Cumulative distance left | DINT | Traveled distance in mms. calculated from the encoder pulses |
Cumulative distance right | DINT | Traveled distance in mms. calculated from the encoder pulses |
Segment | Duration | Duration Variance | Samples Count | Voltage Delta | Voltage Delta Variance | Mass |
---|---|---|---|---|---|---|
B -> A Path through the nine segments: 056 020 048 052 016 044 012 036 004 | ||||||
42.0 | 26.500000 | 26.500000 | 56.500000 | −1.666667 | 5.527708 | 200.0 |
41.0 | 22.750000 | 22.750000 | 47.916667 | −10.000000 | 7.071068 | 200.0 |
9.0 | 11.000000 | 11.000000 | 24.583333 | 3.333333 | 4.714045 | 200.0 |
19.0 | 3.739130 | 3.739130 | 9.391304 | −0.869565 | 5.032973 | 200.0 |
56.0 | 24.200000 | 24.200000 | 52.228571 | −8.857143 | 12.135292 | 200.0 |
51.0 | 4.000000 | 4.000000 | 10.347826 | −0.434783 | 4.642208 | 200.0 |
23.0 | 9.000000 | 9.000000 | 20.250000 | −3.333333 | 9.428090 | 200.0 |
59.0 | 63.750000 | 63.750000 | 132.833333 | −5.000000 | 9.574271 | 200.0 |
20.0 | 9.083333 | 9.083333 | 20.333333 | 7.500000 | 5.951190 | 200.0 |
48.0 | 4.000000 | 4.000000 | 10.565217 | 0.869565 | 5.833221 | 200.0 |
52.0 | 5.000000 | 5.000000 | 12.869565 | −2.608696 | 5.289359 | 200.0 |
16.0 | 3.739130 | 3.739130 | 9.391304 | 1.739130 | 6.360321 | 200.0 |
44.0 | 12.166667 | 12.166667 | 26.750000 | −6.666667 | 6.236096 | 200.0 |
12.0 | 8.166667 | 8.166667 | 18.916667 | 2.500000 | 4.330127 | 200.0 |
36.0 | 10.000000 | 10.000000 | 22.166667 | −3.333333 | 4.714045 | 200.0 |
4.0 | 57.636364 | 57.636364 | 117.000000 | −9.090909 | 5.142595 | 200.0 |
7.0 | 54.181818 | 54.181818 | 112.909091 | −10.909091 | 6.680427 | 200.0 |
39.0 | 10.545455 | 10.545455 | 23.272727 | −1.818182 | 3.856946 | 200.0 |
15.0 | 8.272727 | 8.272727 | 19.636364 | 3.636364 | 4.810457 | 200.0 |
47.0 | 11.818182 | 11.818182 | 26.363636 | −6.363636 | 6.428243 | 200.0 |
27.0 | 10.181818 | 10.181818 | 22.454545 | −7.272727 | 6.165755 | 200.0 |
63.0 | 18.272727 | 18.272727 | 39.000000 | 5.454545 | 4.979296 | 200.0 |
60.0 | 14.909091 | 14.909091 | 29.636364 | −10.000000 | 6.030227 | 200.0 |
24.0 | 10.000000 | 10.000000 | 23.090909 | −1.818182 | 5.749596 | 200.0 |
10.0 | 16.454545 | 16.454545 | 35.181818 | 0.909091 | 6.680427 | 200.0 |
C -> B Path through the seven segments 041 009 019 055 051 023 059 | ||||||
42.0 | 26.500000 | 26.500000 | 56.500000 | −1.666667 | 5.527708 | 200.0 |
41.0 | 22.750000 | 22.750000 | 47.916667 | −10.000000 | 7.071068 | 200.0 |
9.0 | 11.000000 | 11.000000 | 24.583333 | 3.333333 | 4.714045 | 200.0 |
19.0 | 3.739130 | 3.739130 | 9.391304 | −0.869565 | 5.032973 | 200.0 |
55.0 | 24.200000 | 24.200000 | 52.228571 | −8.857143 | 12.135292 | 200.0 |
51.0 | 4.000000 | 4.000000 | 10.347826 | −0.434783 | 4.642208 | 200.0 |
23.0 | 9.000000 | 9.000000 | 20.250000 | −3.333333 | 9.428090 | 200.0 |
59.0 | 63.750000 | 63.750000 | 132.833333 | −5.000000 | 9.574271 | 200.0 |
20.0 | 9.083333 | 9.083333 | 20.333333 | 7.500000 | 5.951190 | 200.0 |
48.0 | 4.000000 | 4.000000 | 10.565217 | 0.869565 | 5.833221 | 200.0 |
52.0 | 5.000000 | 5.000000 | 12.869565 | −2.608696 | 5.289359 | 200.0 |
16.0 | 3.739130 | 3.739130 | 9.391304 | 1.739130 | 6.360321 | 200.0 |
44.0 | 12.166667 | 12.166667 | 26.750000 | −6.666667 | 6.236096 | 200.0 |
12.0 | 8.166667 | 8.166667 | 18.916667 | 2.500000 | 4.330127 | 200.0 |
36.0 | 10.000000 | 10.000000 | 22.166667 | −3.333333 | 4.714045 | 200.0 |
4.0 | 57.636364 | 57.636364 | 117.000000 | −9.090909 | 5.142595 | 200.0 |
7.0 | 54.181818 | 54.181818 | 112.909091 | −10.909091 | 6.680427 | 200.0 |
39.0 | 10.545455 | 10.545455 | 23.272727 | −1.818182 | 3.856946 | 200.0 |
15.0 | 8.272727 | 8.272727 | 19.636364 | 3.636364 | 4.810457 | 200.0 |
47.0 | 11.818182 | 11.818182 | 26.363636 | −6.363636 | 6.428243 | 200.0 |
27.0 | 10.181818 | 10.181818 | 22.454545 | −7.272727 | 6.165755 | 200.0 |
63.0 | 18.272727 | 18.272727 | 39.000000 | 5.454545 | 4.979296 | 200.0 |
60.0 | 14.909091 | 14.909091 | 29.636364 | −10.000000 | 6.030227 | 200.0 |
24.0 | 10.000000 | 10.000000 | 23.090909 | −1.818182 | 5.749596 | 200.0 |
10.0 | 16.454545 | 16.454545 | 35.181818 | 0.909091 | 6.680427 | 200.0 |
Neurons in the Hidden Layers | MSE | MAPE% | MAE | Training Speed Min | Learn Count Segment | Epoch Count | Batch Size | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TW1 | TW2 | Stat | TW1 | TW2 | Stat | TW1 | TW2 | Stat | |||||
B -> A Path through the nine segments: 056 020 048 052 016 044 012 036 004 | |||||||||||||
2.0 | 2614.41 | 266.58 | 245.99 | 0.06 | 0.03 | 0.04 | 25.54 | 14.27 | 14.69 | 1.39 | 300.0 | 100.0 | 2.0 |
3.0 | 49.38 | 159.66 | 245.99 | 0.01 | 0.03 | 0.04 | 5.76 | 11.37 | 14.69 | 1.27 | 300.0 | 100.0 | 2.0 |
4.0 | 363.24 | 310.57 | 245.99 | 0.04 | 0.04 | 0.04 | 15.03 | 15.62 | 14.69 | 1.06 | 300.0 | 100.0 | 2.0 |
5.0 | 768.61 | 427.56 | 245.99 | 0.05 | 0.04 | 0.04 | 21.43 | 16.94 | 14.69 | 0.91 | 300.0 | 100.0 | 2.0 |
6.0 | 12.96 | 60.37 | 245.99 | 0.01 | 0.02 | 0.04 | 3.07 | 6.32 | 14.69 | 1.06 | 300.0 | 100.0 | 2.0 |
7.0 | 238.46 | 1067.95 | 245.99 | 0.03 | 0.06 | 0.04 | 12.55 | 25.85 | 14.69 | 1.38 | 300.0 | 100.0 | 2.0 |
8.0 | 787.75 | 190.53 | 245.99 | 0.05 | 0.03 | 0.04 | 19.56 | 11.89 | 14.69 | 0.89 | 300.0 | 100.0 | 2.0 |
9.0 | 97.71 | 165.73 | 245.99 | 0.02 | 0.03 | 0.04 | 7.84 | 10.98 | 14.69 | 0.91 | 300.0 | 100.0 | 2.0 |
10.0 | 108.95 | 258.94 | 245.99 | 0.02 | 0.04 | 0.04 | 9.25 | 14.84 | 14.69 | 0.89 | 300.0 | 100.0 | 2.0 |
11.0 | 155.58 | 86.65 | 245.99 | 0.02 | 0.02 | 0.04 | 9.64 | 7.04 | 14.69 | 0.94 | 300.0 | 100.0 | 2.0 |
2.0 | 617.56 | 633.53 | 245.99 | 0.06 | 0.06 | 0.04 | 23.32 | 23.55 | 14.69 | 0.65 | 300.0 | 100.0 | 3. |
3.0 | 700.39 | 747.8 | 245.99 | 0.06 | 0.06 | 0.04 | 25.07 | 25.87 | 14.69 | 0.69 | 300.0 | 100.0 | 3.0 |
4.0 | 32.18 | 144.7 | 245.99 | 0.01 | 0.03 | 0.04 | 4.55 | 10.56 | 14.69 | 0.8 | 300.0 | 100.0 | 3. |
5.0 | 1403.14 | 1463.64 | 245.99 | 0.09 | 0.09 | 0.04 | 36.58 | 35.99 | 14.69 | 0.69 | 300.0 | 100.0 | 3.0 |
6.0 | 665.27 | 844.29 | 245.99 | 0.04 | 0.05 | 0.04 | 17.1 | 22.75 | 14.69 | 0.96 | 300.0 | 100.0 | 3.0 |
7.0 | 1059.0 | 778.0 | 245.99 | 0.06 | 0.06 | 0.04 | 24.33 | 23.48 | 14.69 | 0.83 | 300.0 | 100.0 | 3.0 |
8.0 | 296.36 | 209.15 | 245.99 | 0.03 | 0.03 | 0.04 | 10.58 | 10.96 | 14.69 | 0.71 | 300.0 | 100.0 | 3.0 |
9.0 | 1586.48 | 832.53 | 245.99 | 0.07 | 0.06 | 0.04 | 30.86 | 23.55 | 14.69 | 0.73 | 300.0 | 100.0 | 3.0 |
10.0 | 1556.02 | 1008.44 | 245.99 | 0.06 | 0.06 | 0.04 | 25.23 | 23.97 | 14.69 | 0.74 | 300.0 | 100.0 | 3.0 |
11.0 | 1401.98 | 849.24 | 245.99 | 0.08 | 0.05 | 0.04 | 32.0 | 19.2 | 14.69 | 0.71 | 300.0 | 100.0 | 3.0 |
2.0 | 21.29 | 57.81 | 245.99 | 0.01 | 0.01 | 0.04 | 3.49 | 5.94 | 14.69 | 0.5 | 300.0 | 100.0 | 4.0 |
3.0 | 1917.95 | 1345.89 | 245.99 | 0.09 | 0.07 | 0.04 | 37.33 | 28.07 | 14.69 | 0.66 | 300.0 | 100.0 | 4.0 |
4.0 | 178.6 | 122.42 | 245.99 | 0.02 | 0.02 | 0.04 | 9.46 | 9.85 | 14.69 | 0.59 | 300.0 | 100.0 | 4.0 |
5.0 | 1810.52 | 881.79 | 245.99 | 0.05 | 0.05 | 0.04 | 21.98 | 21.92 | 14.69 | 0.57 | 300.0 | 100.0 | 4.0 |
6.0 | 1925.58 | 2263.18 | 245.99 | 0.1 | 0.11 | 0.04 | 43.2 | 44.23 | 14.69 | 0.54 | 300.0 | 100.0 | 4.0 |
7.0 | 120.76 | 242.15 | 245.99 | 0.02 | 0.03 | 0.04 | 9.27 | 13.39 | 14.69 | 0.71 | 300.0 | 100.0 | 4.0 |
8.0 | 69.1 | 184.58 | 245.99 | 0.01 | 0.03 | 0.04 | 6.16 | 10.47 | 14.69 | 0.46 | 300.0 | 100.0 | 4.0 |
9.0 | 1415.14 | 1213.32 | 245.99 | 0.07 | 0.07 | 0.04 | 28.36 | 28.3 | 14.69 | 0.79 | 300.0 | 100.0 | 4.0 |
10.0 | 555.0 | 1154.4 | 245.99 | 0.05 | 0.06 | 0.04 | 18.94 | 26.63 | 14.69 | 0.96 | 300.0 | 100.0 | 4.0 |
11.0 | 140.44 | 160.6 | 245.99 | 0.03 | 0.02 | 0.04 | 10.59 | 8.08 | 14.69 | 0.45 | 300.0 | 100.0 | 4.0 |
2.0 | 42.83 | 114.85 | 245.99 | 0.01 | 0.02 | 0.04 | 5.1 | 8.55 | 14.69 | 0.39 | 300.0 | 100.0 | 5.0 |
3.0 | 43.97 | 120.82 | 245.99 | 0.01 | 0.02 | 0.04 | 5.4 | 8.1 | 14.69 | 0.55 | 300.0 | 100.0 | 5.0 |
4.0 | 37.5 | 91.38 | 245.99 | 0.01 | 0.02 | 0.04 | 5.42 | 6.37 | 14.69 | 0.44 | 300.0 | 100.0 | 5.0 |
5.0 | 1174.05 | 860.59 | 245.99 | 0.06 | 0.07 | 0.04 | 26.68 | 27.32 | 14.69 | 0.71 | 300.0 | 100.0 | 5.0 |
6.0 | 104.67 | 241.98 | 245.99 | 0.02 | 0.03 | 0.04 | 8.42 | 13.63 | 14.69 | 0.46 | 300.0 | 100.0 | 5.0 |
7.0 | 635.35 | 1117.42 | 245.99 | 0.04 | 0.07 | 0.04 | 18.13 | 28.63 | 14.69 | 0.41 | 300.0 | 100.0 | 5.0 |
8.0 | 1657.43 | 1377.55 | 245.99 | 0.05 | 0.06 | 0.04 | 20.09 | 24.26 | 14.69 | 0.54 | 300.0 | 100.0 | 5.0 |
9.0 | 1016.13 | 898.24 | 245.99 | 0.07 | 0.07 | 0.04 | 28.54 | 27.39 | 14.69 | 0.4 | 300.0 | 100.0 | 5.0 |
10.0 | 3091.74 | 3533.54 | 245.99 | 0.08 | 0.11 | 0.04 | 35.32 | 45.16 | 14.69 | 0.56 | 300.0 | 100.0 | 5.0 |
11.0 | 968.7 | 2353.06 | 245.99 | 0.05 | 0.09 | 0.04 | 22.55 | 38.35 | 14.69 | 0.47 | 300.0 | 100.0 | 5.0 |
2.0 | 91.52 | 256.85 | 245.99 | 0.02 | 0.03 | 0.04 | 7.96 | 14.14 | 14.69 | 0.39 | 300.0 | 100.0 | 6.0 |
3.0 | 113.34 | 602.76 | 245.99 | 0.02 | 0.04 | 0.04 | 8.53 | 16.69 | 14.69 | 0.39 | 300.0 | 100.0 | 6.0 |
4.0 | 792.7 | 1160.1 | 245.99 | 0.05 | 0.08 | 0.04 | 22.68 | 31.39 | 14.69 | 0.53 | 300.0 | 100.0 | 6. |
5.0 | 311.49 | 1036.91 | 245.99 | 0.04 | 0.06 | 0.04 | 16.63 | 24.32 | 14.69 | 0.44 | 300.0 | 100.0 | 6.0 |
6.0 | 1448.93 | 631.0 | 245.99 | 0.08 | 0.06 | 0.04 | 34.27 | 23.0 | 14.69 | 0.45 | 300.0 | 100.0 | 6.0 |
7.0 | 2067.42 | 2726.67 | 245.99 | 0.1 | 0.12 | 0.04 | 40.84 | 50.0 | 14.69 | 0.52 | 300.0 | 100.0 | 6.0 |
8.0 | 135.02 | 224.72 | 245.99 | 0.02 | 0.03 | 0.04 | 7.64 | 11.86 | 14.69 | 0.39 | 300.0 | 100.0 | 6.0 |
9.0 | 431.25 | 471.52 | 245.99 | 0.04 | 0.04 | 0.04 | 14.96 | 16.03 | 14.69 | 0.47 | 300.0 | 100.0 | 6.0 |
10.0 | 1729.53 | 951.96 | 245.99 | 0.08 | 0.06 | 0.04 | 31.43 | 26.97 | 14.69 | 0.47 | 300.0 | 100.0 | 6.0 |
11.0 | 199.01 | 379.71 | 245.99 | 0.03 | 0.04 | 0.04 | 11.16 | 15.88 | 14.69 | 0.44 | 300.0 | 100.0 | 6.0 |
2.0 | 1053.52 | 1830.94 | 245.99 | 0.07 | 0.09 | 0.04 | 28.41 | 38.14 | 14.69 | 0.39 | 300.0 | 100.0 | 7.0 |
3.0 | 810.4 | 760.82 | 245.99 | 0.05 | 0.05 | 0.04 | 20.66 | 20.48 | 14.69 | 0.38 | 300.0 | 100.0 | 7.0 |
4.0 | 2312.12 | 2413.16 | 245.99 | 0.11 | 0.11 | 0.04 | 46.18 | 47.54 | 14.69 | 0.37 | 300.0 | 100.0 | 7.0 |
5.0 | 1403.27 | 1659.74 | 245.99 | 0.08 | 0.09 | 0.04 | 33.02 | 36.86 | 14.69 | 0.35 | 300.0 | 100.0 | 7.0 |
6.0 | 2204.23 | 998.79 | 245.99 | 0.09 | 0.06 | 0.04 | 37.12 | 26.67 | 14.69 | 0.44 | 300.0 | 100.0 | 7.0 |
7.0 | 1157.11 | 973.55 | 245.99 | 0.07 | 0.07 | 0.04 | 29.61 | 29.43 | 14.69 | 0.45 | 300.0 | 100.0 | 7.0 |
8.0 | 110.39 | 1043.21 | 245.99 | 0.02 | 0.06 | 0.04 | 8.66 | 25.16 | 14.69 | 0.39 | 300.0 | 100.0 | 7.0 |
9.0 | 64.52 | 193.81 | 245.99 | 0.01 | 0.03 | 0.04 | 6.21 | 10.77 | 14.69 | 0.36 | 300.0 | 100.0 | 7.0 |
10.0 | 450.42 | 1959.15 | 245.99 | 0.04 | 0.07 | 0.04 | 15.47 | 31.09 | 14.69 | 0.41 | 300.0 | 100.0 | 7.0 |
11.0 | 8780.56 | 2300.54 | 245.99 | 0.18 | 0.07 | 0.04 | 74.67 | 29.83 | 14.69 | 0.36 | 300.0 | 100.0 | 7.0 |
2.0 | 316.58 | 996.08 | 245.99 | 0.03 | 0.06 | 0.04 | 11.92 | 24.82 | 14.69 | 0.25 | 300.0 | 100.0 | 8.0 |
3.0 | 1340.61 | 1536.67 | 245.99 | 0.08 | 0.07 | 0.04 | 32.33 | 30.85 | 14.69 | 0.25 | 300.0 | 100.0 | 8.0 |
4.0 | 1183.36 | 1006.46 | 245.99 | 0.07 | 0.06 | 0.04 | 31.23 | 27.12 | 14.69 | 0.26 | 300.0 | 100.0 | 8.0 |
5.0 | 93.79 | 222.94 | 245.99 | 0.02 | 0.03 | 0.04 | 8.21 | 12.09 | 14.69 | 0.36 | 300.0 | 100.0 | 8.0 |
6.0 | 420.98 | 980.4 | 245.99 | 0.04 | 0.06 | 0.04 | 17.47 | 26.59 | 14.69 | 0.36 | 300.0 | 100.0 | 8.0 |
7.0 | 135.68 | 263.85 | 245.99 | 0.02 | 0.03 | 0.04 | 9.45 | 14.62 | 14.69 | 0.26 | 300.0 | 100.0 | 8.0 |
8.0 | 818.56 | 1718.34 | 245.99 | 0.06 | 0.08 | 0.04 | 25.76 | 33.25 | 14.69 | 0.26 | 300.0 | 100.0 | 8.0 |
9.0 | 94.33 | 769.7 | 245.99 | 0.02 | 0.06 | 0.04 | 6.69 | 23.52 | 14.69 | 0.27 | 300.0 | 100.0 | 8.0 |
10.0 | 662.0 | 314.84 | 245.99 | 0.04 | 0.04 | 0.04 | 17.87 | 15.84 | 14.69 | 0.36 | 300.0 | 100.0 | 8.0 |
11.0 | 742.79 | 307.04 | 245.99 | 0.05 | 0.04 | 0.04 | 20.8 | 15.81 | 14.69 | 0.27 | 300.0 | 100.0 | 8.0 |
2.0 | 825.95 | 1440.6 | 245.99 | 0.05 | 0.07 | 0.04 | 21.67 | 28.7 | 14.69 | 0.23 | 300.0 | 100.0 | 9.0 |
3.0 | 48.48 | 69.68 | 245.99 | 0.01 | 0.02 | 0.04 | 6.12 | 7.02 | 14.69 | 0.21 | 300.0 | 100.0 | 9.0 |
4.0 | 3302.63 | 2400.13 | 245.99 | 0.11 | 0.1 | 0.04 | 46.46 | 41.22 | 14.69 | 0.36 | 300.0 | 100.0 | 9.0 |
5.0 | 187.92 | 430.18 | 245.99 | 0.03 | 0.04 | 0.04 | 13.07 | 17.43 | 14.69 | 0.24 | 300.0 | 100.0 | 9.0 |
6.0 | 581.96 | 2558.95 | 245.99 | 0.05 | 0.11 | 0.04 | 22.18 | 44.65 | 14.69 | 0.27 | 300.0 | 100.0 | 9.0 |
7.0 | 45.64 | 280.77 | 245.99 | 0.01 | 0.03 | 0.04 | 5.6 | 13.28 | 14.69 | 0.25 | 300.0 | 100.0 | 9.0 |
8.0 | 1844.14 | 1306.57 | 245.99 | 0.09 | 0.07 | 0.04 | 36.14 | 30.85 | 14.69 | 0.24 | 300.0 | 100.0 | 9.0 |
9.0 | 519.89 | 416.71 | 245.99 | 0.04 | 0.04 | 0.04 | 17.61 | 17.71 | 14.69 | 0.35 | 300.0 | 100.0 | 9.0 |
10.0 | 25.64 | 165.3 | 245.99 | 0.01 | 0.03 | 0.04 | 4.13 | 10.83 | 14.69 | 0.19 | 300.0 | 100.0 | 9.0 |
11.0 | 781.17 | 502.24 | 245.99 | 0.04 | 0.05 | 0.04 | 18.6 | 19.31 | 14.69 | 0.27 | 300.0 | 100.0 | 9.0 |
2.0 | 783.61 | 1054.46 | 245.99 | 0.06 | 0.07 | 0.04 | 25.45 | 30.66 | 14.69 | 0.18 | 300.0 | 100.0 | 10.0 |
3.0 | 2310.75 | 3464.63 | 245.99 | 0.1 | 0.12 | 0.04 | 42.84 | 49.07 | 14.69 | 0.23 | 300.0 | 100.0 | 10.0 |
4.0 | 4186.7 | 2536.53 | 245.99 | 0.13 | 0.1 | 0.04 | 52.27 | 43.47 | 14.69 | 0.24 | 300.0 | 100.0 | 10.0 |
5.0 | 271.87 | 950.11 | 245.99 | 0.03 | 0.06 | 0.04 | 12.32 | 24.33 | 14.69 | 0.23 | 300.0 | 100.0 | 10.0 |
6.0 | 949.33 | 2538.05 | 245.99 | 0.06 | 0.09 | 0.04 | 25.17 | 37.7 | 14.69 | 0.21 | 300.0 | 100.0 | 10.0 |
7.0 | 514.17 | 329.61 | 245.99 | 0.04 | 0.04 | 0.04 | 16.91 | 16.3 | 14.69 | 0.29 | 300.0 | 100.0 | 10.0 |
8.0 | 571.32 | 1411.69 | 245.99 | 0.04 | 0.07 | 0.04 | 15.55 | 30.21 | 14.69 | 0.23 | 300.0 | 100.0 | 10.0 |
9.0 | 1087.82 | 1120.72 | 245.99 | 0.07 | 0.07 | 0.04 | 28.18 | 27.5 | 14.69 | 0.22 | 300.0 | 100.0 | 10.0 |
10.0 | 225.42 | 1315.91 | 245.99 | 0.03 | 0.07 | 0.04 | 10.8 | 28.42 | 14.69 | 0.36 | 300.0 | 100.0 | 10.0 |
11.0 | 2130.58 | 1057.31 | 245.99 | 0.1 | 0.06 | 0.04 | 42.91 | 23.19 | 14.69 | 0.22 | 300.0 | 100.0 | 10.0 |
2.0 | 742.55 | 3332.57 | 245.99 | 0.05 | 0.12 | 0.04 | 22.59 | 48.45 | 14.69 | 0.36 | 300.0 | 100.0 | 11.0 |
3.0 | 3587.94 | 3133.55 | 245.99 | 0.12 | 0.11 | 0.04 | 49.95 | 47.48 | 14.69 | 0.19 | 300.0 | 100.0 | 11.0 |
4.0 | 1055.46 | 1217.52 | 245.99 | 0.06 | 0.07 | 0.04 | 25.36 | 29.72 | 14.69 | 0.16 | 300.0 | 100.0 | 11.0 |
5.0 | 5525.17 | 5024.16 | 245.99 | 0.11 | 0.1 | 0.04 | 45.42 | 39.73 | 14.69 | 0.4 | 300.0 | 100.0 | 11.0 |
6.0 | 370.84 | 751.9 | 245.99 | 0.04 | 0.06 | 0.04 | 17.14 | 24.48 | 14.69 | 0.36 | 300.0 | 100.0 | 11.0 |
7.0 | 691.71 | 1847.75 | 245.99 | 0.06 | 0.09 | 0.04 | 23.38 | 35.73 | 14.69 | 0.24 | 300.0 | 100.0 | 11.0 |
8.0 | 4165.01 | 2609.9 | 245.99 | 0.13 | 0.11 | 0.04 | 52.25 | 44.01 | 14.69 | 0.22 | 300.0 | 100.0 | 11.0 |
9.0 | 992.96 | 657.46 | 245.99 | 0.06 | 0.05 | 0.04 | 24.46 | 19.34 | 14.69 | 0.21 | 300.0 | 100.0 | 11.0 |
10.0 | 60.34 | 235.89 | 245.99 | 0.01 | 0.03 | 0.04 | 5.97 | 13.93 | 14.69 | 0.22 | 300.0 | 100.0 | 11.0 |
11.0 | 557.2 | 424.99 | 245.99 | 0.05 | 0.04 | 0.04 | 21.07 | 18.0 | 14.69 | 0.72 | 300.0 | 100.0 | 11.0 |
C -> B Path through the seven segments 041 009 019 056 051 023 059 | |||||||||||||
2.0 | 11.39 | 27.14 | 65.6 | 0.01 | 0.01 | 0.02 | 2.76 | 3.83 | 6.33 | 0.89 | 300.0 | 100.0 | 2.0 |
3.0 | 442.03 | 484.68 | 65.6 | 0.05 | 0.05 | 0.02 | 19.47 | 20.45 | 6.33 | 1.0 | 300.0 | 100.0 | 2.0 |
4.0 | 53.57 | 71.42 | 65.6 | 0.02 | 0.02 | 0.02 | 7.16 | 7.72 | 6.33 | 0.92 | 300.0 | 100.0 | 2.0 |
5.0 | 3.26 | 15.37 | 65.6 | 0.0 | 0.01 | 0.02 | 1.67 | 2.9 | 6.33 | 0.9 | 300.0 | 100.0 | 2.0 |
6.0 | 78.24 | 140.22 | 65.6 | 0.01 | 0.02 | 0.02 | 5.88 | 7.82 | 6.33 | 1.38 | 300.0 | 100.0 | 2.0 |
7.0 | 4.51 | 4.35 | 65.6 | 0.0 | 0.0 | 0.02 | 1.7 | 1.78 | 6.33 | 0.89 | 300.0 | 100.0 | 2.0 |
8.0 | 19.59 | 37.35 | 65.6 | 0.01 | 0.01 | 0.02 | 3.71 | 5.34 | 6.33 | 0.84 | 300.0 | 100.0 | 2.0 |
9.0 | 26.27 | 30.46 | 65.6 | 0.01 | 0.01 | 0.02 | 4.48 | 4.83 | 6.33 | 1.03 | 300.0 | 100.0 | 2.0 |
10.0 | 55.31 | 74.86 | 65.6 | 0.02 | 0.02 | 0.02 | 6.75 | 7.35 | 6.33 | 0.92 | 300.0 | 100.0 | 2.0 |
11.0 | 6.97 | 17.73 | 65.6 | 0.01 | 0.01 | 0.02 | 2.21 | 3.43 | 6.33 | 0.99 | 300.0 | 100.0 | 2.0 |
2.0 | 33.36 | 15.57 | 65.6 | 0.01 | 0.01 | 0.02 | 5.01 | 3.51 | 6.33 | 0.63 | 300.0 | 100.0 | 3.0 |
3.0 | 30.76 | 48.53 | 65.6 | 0.01 | 0.01 | 0.02 | 5.02 | 5.66 | 6.33 | 0.55 | 300.0 | 100.0 | 3.0 |
4.0 | 30.59 | 46.12 | 65.6 | 0.01 | 0.01 | 0.02 | 5.31 | 5.73 | 6.33 | 0.73 | 300.0 | 100.0 | 3.0 |
5.0 | 24.41 | 19.27 | 65.6 | 0.01 | 0.01 | 0.02 | 3.5 | 3.33 | 6.33 | 0.83 | 300.0 | 100.0 | 3.0 |
6.0 | 226.55 | 240.95 | 65.6 | 0.03 | 0.03 | 0.02 | 14.29 | 14.47 | 6.33 | 0.82 | 300.0 | 100.0 | 3.0 |
7.0 | 16.93 | 29.14 | 65.6 | 0.01 | 0.01 | 0.02 | 2.85 | 4.52 | 6.33 | 1.39 | 300.0 | 100.0 | 3.0 |
8.0 | 39.59 | 51.47 | 65.6 | 0.01 | 0.01 | 0.02 | 5.29 | 6.05 | 6.33 | 0.7 | 300.0 | 100.0 | 3.0 |
9.0 | 42.67 | 68.51 | 65.6 | 0.01 | 0.02 | 0.02 | 5.84 | 6.62 | 6.33 | 0.76 | 300.0 | 100.0 | 3.0 |
10.0 | 249.51 | 265.19 | 65.6 | 0.03 | 0.03 | 0.02 | 14.32 | 14.42 | 6.33 | 0.67 | 300.0 | 100.0 | 3.0 |
11.0 | 20.0 | 93.96 | 65.6 | 0.01 | 0.02 | 0.02 | 4.12 | 7.3 | 6.33 | 0.67 | 300.0 | 100.0 | 3.0 |
2.0 | 31.44 | 39.8 | 65.6 | 0.01 | 0.01 | 0.02 | 4.46 | 5.28 | 6.33 | 0.7 | 300.0 | 100.0 | 4.0 |
3.0 | 6.43 | 18.95 | 65.6 | 0.0 | 0.01 | 0.02 | 1.97 | 3.17 | 6.33 | 0.49 | 300.0 | 100.0 | 4.0 |
4.0 | 120.55 | 142.21 | 65.6 | 0.02 | 0.02 | 0.02 | 7.97 | 9.13 | 6.33 | 0.57 | 300.0 | 100.0 | 4.0 |
5.0 | 163.02 | 132.17 | 65.6 | 0.02 | 0.02 | 0.02 | 9.83 | 9.81 | 6.33 | 1.02 | 300.0 | 100.0 | 4.0 |
6.0 | 17.9 | 26.37 | 65.6 | 0.01 | 0.01 | 0.02 | 3.87 | 4.4 | 6.33 | 0.42 | 300.0 | 100.0 | 4.0 |
7.0 | 237.69 | 226.6 | 65.6 | 0.04 | 0.03 | 0.02 | 15.22 | 14.56 | 6.33 | 0.5 | 300.0 | 100.0 | 4.0 |
8.0 | 1126.4 | 985.48 | 65.6 | 0.06 | 0.05 | 0.02 | 23.32 | 20.89 | 6.33 | 0.56 | 300.0 | 100.0 | 4.0 |
9.0 | 241.93 | 349.14 | 65.6 | 0.03 | 0.03 | 0.02 | 12.01 | 13.12 | 6.33 | 0.52 | 300.0 | 100.0 | 4.0 |
10.0 | 5.15 | 8.5 | 65.6 | 0.0 | 0.01 | 0.02 | 2.05 | 2.52 | 6.33 | 0.56 | 300.0 | 100.0 | 4.0 |
11.0 | 145.7 | 180.36 | 65.6 | 0.03 | 0.03 | 0.02 | 11.32 | 11.9 | 6.33 | 0.54 | 300.0 | 100.0 | 4.0 |
2.0 | 12.86 | 22.55 | 65.6 | 0.01 | 0.01 | 0.02 | 2.97 | 4.11 | 6.33 | 0.42 | 300.0 | 100.0 | 5.0 |
3.0 | 366.83 | 363.22 | 65.6 | 0.04 | 0.04 | 0.02 | 18.59 | 18.6 | 6.33 | 0.43 | 300.0 | 100.0 | 5.0 |
4.0 | 74.61 | 91.36 | 65.6 | 0.02 | 0.02 | 0.02 | 6.93 | 7.52 | 6.33 | 0.52 | 300.0 | 100.0 | 5.0 |
5.0 | 27.71 | 32.75 | 65.6 | 0.01 | 0.01 | 0.02 | 4.66 | 4.81 | 6.33 | 0.56 | 300.0 | 100.0 | 5.0 |
6.0 | 90.66 | 153.54 | 65.6 | 0.02 | 0.03 | 0.02 | 8.84 | 11.67 | 6.33 | 0.54 | 300.0 | 100.0 | 5.0 |
7.0 | 24.32 | 46.04 | 65.6 | 0.01 | 0.01 | 0.02 | 3.9 | 4.98 | 6.33 | 0.65 | 300.0 | 100.0 | 5.0 |
8.0 | 178.14 | 161.21 | 65.6 | 0.02 | 0.02 | 0.02 | 7.92 | 7.6 | 6.33 | 0.47 | 300.0 | 100.0 | 5.0 |
9.0 | 53.82 | 45.77 | 65.6 | 0.01 | 0.01 | 0.02 | 6.24 | 5.97 | 6.33 | 0.55 | 300.0 | 100.0 | 5.0 |
10.0 | 24.83 | 33.74 | 65.6 | 0.01 | 0.01 | 0.02 | 4.53 | 5.28 | 6.33 | 0.34 | 300.0 | 100.0 | 5.0 |
11.0 | 118.69 | 158.51 | 65.6 | 0.02 | 0.03 | 0.02 | 8.51 | 10.93 | 6.33 | 0.41 | 300.0 | 100.0 | 5.0 |
2.0 | 44.81 | 59.0 | 65.6 | 0.01 | 0.02 | 0.02 | 5.59 | 6.71 | 6.33 | 0.54 | 300.0 | 100.0 | 6.0 |
3.0 | 63.55 | 67.58 | 65.6 | 0.02 | 0.02 | 0.02 | 6.32 | 7.55 | 6.33 | 0.7 | 300.0 | 100.0 | 6.0 |
4.0 | 73.95 | 79.6 | 65.6 | 0.02 | 0.02 | 0.02 | 7.35 | 6.65 | 6.33 | 0.37 | 300.0 | 100.0 | 6.0 |
5.0 | 97.85 | 174.68 | 65.6 | 0.02 | 0.03 | 0.02 | 9.56 | 12.59 | 6.33 | 0.36 | 300.0 | 100.0 | 6.0 |
6.0 | 85.48 | 60.98 | 65.6 | 0.02 | 0.01 | 0.02 | 7.64 | 5.93 | 6.33 | 0.42 | 300.0 | 100.0 | 6.0 |
7.0 | 628.49 | 617.02 | 65.6 | 0.05 | 0.06 | 0.02 | 21.63 | 23.01 | 6.33 | 0.36 | 300.0 | 100.0 | 6.0 |
8.0 | 823.88 | 887.21 | 65.6 | 0.05 | 0.06 | 0.02 | 22.43 | 23.71 | 6.33 | 0.35 | 300.0 | 100.0 | 6.0 |
9.0 | 38.54 | 317.4 | 65.6 | 0.01 | 0.03 | 0.02 | 5.14 | 12.72 | 6.33 | 0.35 | 300.0 | 100.0 | 6.0 |
10.0 | 1258.99 | 1276.98 | 65.6 | 0.08 | 0.08 | 0.02 | 32.07 | 32.04 | 6.33 | 0.34 | 300.0 | 100.0 | 6.0 |
11.0 | 52.03 | 91.56 | 65.6 | 0.01 | 0.02 | 0.02 | 5.97 | 7.2 | 6.33 | 0.38 | 300.0 | 100.0 | 6.0 |
2.0 | 463.89 | 463.89 | 65.6 | 0.05 | 0.05 | 0.02 | 20.17 | 20.17 | 6.33 | 0.58 | 300.0 | 100.0 | 7.0 |
3.0 | 1033.96 | 1040.88 | 65.6 | 0.05 | 0.05 | 0.02 | 19.65 | 19.83 | 6.33 | 0.34 | 300.0 | 100.0 | 7.0 |
4.0 | 603.48 | 549.11 | 65.6 | 0.05 | 0.04 | 0.02 | 19.33 | 18.43 | 6.33 | 0.34 | 300.0 | 100.0 | 7.0 |
5.0 | 34.83 | 83.8 | 65.6 | 0.01 | 0.01 | 0.02 | 4.26 | 5.07 | 6.33 | 0.36 | 300.0 | 100.0 | 7.0 |
6.0 | 27.38 | 39.32 | 65.6 | 0.01 | 0.01 | 0.02 | 4.51 | 5.36 | 6.33 | 0.7 | 300.0 | 100.0 | 7.0 |
7.0 | 43.61 | 144.06 | 65.6 | 0.01 | 0.02 | 0.02 | 6.15 | 10.3 | 6.33 | 0.43 | 300.0 | 100.0 | 7.0 |
8.0 | 12.18 | 25.95 | 65.6 | 0.01 | 0.01 | 0.02 | 2.32 | 3.28 | 6.33 | 0.33 | 300.0 | 100.0 | 7.0 |
9.0 | 86.78 | 193.72 | 65.6 | 0.02 | 0.03 | 0.02 | 7.03 | 11.68 | 6.33 | 0.37 | 300.0 | 100.0 | 7.0 |
10.0 | 51.71 | 37.42 | 65.6 | 0.01 | 0.01 | 0.02 | 4.95 | 4.94 | 6.33 | 0.31 | 300.0 | 100.0 | 7.0 |
11.0 | 101.98 | 90.9 | 65.6 | 0.02 | 0.02 | 0.02 | 7.92 | 8.22 | 6.33 | 0.42 | 300.0 | 100.0 | 7.0 |
2.0 | 191.15 | 234.8 | 65.6 | 0.03 | 0.03 | 0.02 | 12.48 | 13.67 | 6.33 | 0.61 | 300.0 | 100.0 | 8.0 |
3.0 | 107.21 | 179.89 | 65.6 | 0.02 | 0.03 | 0.02 | 9.19 | 10.66 | 6.33 | 0.44 | 300.0 | 100.0 | 8.0 |
4.0 | 517.17 | 616.22 | 65.6 | 0.04 | 0.05 | 0.02 | 16.76 | 18.98 | 6.33 | 0.51 | 300.0 | 100.0 | 8.0 |
5.0 | 1224.54 | 1297.65 | 65.6 | 0.05 | 0.05 | 0.02 | 20.39 | 22.92 | 6.33 | 0.73 | 300.0 | 100.0 | 8.0 |
6.0 | 67.01 | 81.52 | 65.6 | 0.02 | 0.02 | 0.02 | 6.62 | 7.21 | 6.33 | 0.67 | 300.0 | 100.0 | 8.0 |
7.0 | 671.84 | 1009.52 | 65.6 | 0.04 | 0.05 | 0.02 | 15.9 | 18.83 | 6.33 | 0.42 | 300.0 | 100.0 | 8.0 |
8.0 | 453.71 | 330.86 | 65.6 | 0.04 | 0.03 | 0.02 | 16.16 | 14.2 | 6.33 | 0.36 | 300.0 | 100.0 | 8.0 |
9.0 | 16.65 | 37.9 | 65.6 | 0.01 | 0.01 | 0.02 | 3.76 | 4.91 | 6.33 | 0.35 | 300.0 | 100.0 | 8.0 |
10.0 | 90.27 | 92.57 | 65.6 | 0.02 | 0.02 | 0.02 | 8.58 | 7.82 | 6.33 | 0.49 | 300.0 | 100.0 | 8.0 |
11.0 | 367.76 | 1453.37 | 65.6 | 0.04 | 0.07 | 0.02 | 14.85 | 28.81 | 6.33 | 0.56 | 300.0 | 100.0 | 8.0 |
2.0 | 60.09 | 37.05 | 65.6 | 0.02 | 0.01 | 0.02 | 6.56 | 5.14 | 6.33 | 0.42 | 300.0 | 100.0 | 9.0 |
3.0 | 257.74 | 308.43 | 65.6 | 0.03 | 0.04 | 0.02 | 13.76 | 15.75 | 6.33 | 0.47 | 300.0 | 100.0 | 9.0 |
4.0 | 294.66 | 228.29 | 65.6 | 0.03 | 0.03 | 0.02 | 14.45 | 13.29 | 6.33 | 0.47 | 300.0 | 100.0 | 9.0 |
5.0 | 12.02 | 22.33 | 65.6 | 0.01 | 0.01 | 0.02 | 2.66 | 3.42 | 6.33 | 0.46 | 300.0 | 100.0 | 9.0 |
6.0 | 71.97 | 85.6 | 65.6 | 0.02 | 0.02 | 0.02 | 7.7 | 8.45 | 6.33 | 0.43 | 300.0 | 100.0 | 9.0 |
7.0 | 80.04 | 107.61 | 65.6 | 0.02 | 0.02 | 0.02 | 8.52 | 9.12 | 6.33 | 0.43 | 300.0 | 100.0 | 9.0 |
8.0 | 533.59 | 491.39 | 65.6 | 0.04 | 0.05 | 0.02 | 18.13 | 19.53 | 6.33 | 0.44 | 300.0 | 100.0 | 9.0 |
9.0 | 201.59 | 209.51 | 65.6 | 0.02 | 0.03 | 0.02 | 10.33 | 11.5 | 6.33 | 0.49 | 300.0 | 100.0 | 9.0 |
10.0 | 1315.27 | 1294.14 | 65.6 | 0.07 | 0.07 | 0.02 | 29.39 | 29.09 | 6.33 | 0.44 | 300.0 | 100.0 | 9.0 |
11.0 | 416.54 | 739.6 | 65.6 | 0.03 | 0.04 | 0.02 | 12.87 | 15.46 | 6.33 | 0.4 | 300.0 | 100.0 | 9.0 |
2.0 | 30.98 | 49.74 | 65.6 | 0.01 | 0.01 | 0.02 | 4.55 | 5.59 | 6.33 | 0.71 | 300.0 | 100.0 | 10.0 |
3.0 | 411.51 | 260.38 | 65.6 | 0.04 | 0.03 | 0.02 | 17.91 | 14.5 | 6.33 | 0.76 | 300.0 | 100.0 | 10.0 |
4.0 | 233.84 | 172.8 | 65.6 | 0.03 | 0.03 | 0.02 | 13.34 | 10.83 | 6.33 | 0.24 | 300.0 | 100.0 | 10.0 |
5.0 | 124.07 | 140.04 | 65.6 | 0.02 | 0.02 | 0.02 | 8.45 | 10.1 | 6.33 | 0.24 | 300.0 | 100.0 | 10.0 |
6.0 | 857.03 | 686.54 | 65.6 | 0.05 | 0.05 | 0.02 | 20.83 | 22.44 | 6.33 | 0.25 | 300.0 | 100.0 | 10.0 |
7.0 | 317.79 | 163.56 | 65.6 | 0.03 | 0.02 | 0.02 | 12.11 | 10.0 | 6.33 | 0.23 | 300.0 | 100.0 | 10.0 |
8.0 | 1717.49 | 1502.99 | 65.6 | 0.09 | 0.08 | 0.02 | 36.1 | 33.65 | 6.33 | 0.25 | 300.0 | 100.0 | 10.0 |
9.0 | 882.16 | 1112.32 | 65.6 | 0.06 | 0.07 | 0.02 | 26.46 | 27.41 | 6.33 | 0.27 | 300.0 | 100.0 | 10.0 |
10.0 | 463.5 | 262.45 | 65.6 | 0.04 | 0.04 | 0.02 | 17.93 | 14.76 | 6.33 | 0.23 | 300.0 | 100.0 | 10.0 |
11.0 | 1429.01 | 3354.17 | 65.6 | 0.07 | 0.13 | 0.02 | 31.21 | 53.42 | 6.33 | 0.23 | 300.0 | 100.0 | 10.0 |
2.0 | 457.9 | 858.64 | 65.6 | 0.05 | 0.06 | 0.02 | 19.06 | 25.09 | 6.33 | 0.36 | 300.0 | 100.0 | 11.0 |
3.0 | 686.0 | 643.77 | 65.6 | 0.05 | 0.05 | 0.02 | 21.02 | 21.48 | 6.33 | 0.23 | 300.0 | 100.0 | 11.0 |
4.0 | 191.13 | 171.01 | 65.6 | 0.02 | 0.02 | 0.02 | 9.24 | 7.67 | 6.33 | 0.21 | 300.0 | 100.0 | 11.0 |
5.0 | 164.59 | 116.08 | 65.6 | 0.03 | 0.02 | 0.02 | 11.61 | 9.64 | 6.33 | 0.24 | 300.0 | 100.0 | 11.0 |
6.0 | 521.33 | 721.28 | 65.6 | 0.03 | 0.04 | 0.02 | 14.49 | 17.52 | 6.33 | 0.19 | 300.0 | 100.0 | 11.0 |
7.0 | 263.6 | 212.7 | 65.6 | 0.03 | 0.03 | 0.02 | 14.35 | 13.04 | 6.33 | 0.36 | 300.0 | 100.0 | 11.0 |
8.0 | 230.32 | 393.35 | 65.6 | 0.03 | 0.04 | 0.02 | 14.21 | 17.35 | 6.33 | 0.25 | 300.0 | 100.0 | 11.0 |
9.0 | 463.63 | 463.34 | 65.6 | 0.04 | 0.04 | 0.02 | 18.02 | 16.62 | 6.33 | 0.21 | 300.0 | 100.0 | 11.0 |
10.0 | 123.08 | 133.83 | 65.6 | 0.02 | 0.02 | 0.02 | 7.75 | 9.12 | 6.33 | 0.36 | 300.0 | 100.0 | 11.0 |
11.0 | 464.33 | 567.84 | 65.6 | 0.03 | 0.04 | 0.02 | 11.89 | 16.02 | 6.33 | 0.25 | 300.0 | 100.0 | 11.0 |
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Field | Size (Bytes) | Description |
---|---|---|
TS | 12 | Time stamp |
WS | 8 | Weight signals |
G1LDS | 2 | Group 1—right drive signals |
G2RDS | 2 | Group 2—left drive signals |
ODS | 12 | Odometry signals |
ENS | 16 | Energy signals |
NNS | 26 | Natural navigation signals |
NNCF | 20 | Natural navigation command feedback |
Neurons in the Hidden Layers | MSE | MAPE% | MAE | Training Speed Min | Batch Size | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
TW1 | TW2 | Stat | TW1 | TW2 | Stat | TW1 | TW2 | Stat | |||
Path through the 9 segments | |||||||||||
2.0 | 21.29 | 57.81 | 245.99 | 0.01 | 0.01 | 0.04 | 3.49 | 5.94 | 14.69 | 0.5 | 4 |
3.0 | 43.97 | 120.82 | 245.99 | 0.01 | 0.02 | 0.04 | 5.4 | 8.1 | 14.69 | 0.55 | 5 |
4.0 | 32.18 | 144.7 | 245.99 | 0.01 | 0.03 | 0.04 | 4.55 | 10.56 | 14.69 | 0.8 | 3 |
5.0 | 93.79 | 222.94 | 245.99 | 0.02 | 0.03 | 0.04 | 8.21 | 12.09 | 14.69 | 0.36 | 8 |
6.0 | 12.96 | 60.37 | 245.99 | 0.01 | 0.02 | 0.04 | 3.07 | 6.32 | 14.69 | 1.06 | 2 |
7.0 | 45.64 | 280.77 | 245.99 | 0.01 | 0.03 | 0.04 | 5.6 | 13.28 | 14.69 | 0.25 | 9 |
8.0 | 69.1 | 184.58 | 245.99 | 0.01 | 0.03 | 0.04 | 6.16 | 10.47 | 14.69 | 0.46 | 4 |
9.0 | 64.52 | 193.81 | 245.99 | 0.01 | 0.03 | 0.04 | 6.21 | 10.77 | 14.69 | 0.36 | 7 |
10.0 | 25.64 | 165.3 | 245.99 | 0.01 | 0.03 | 0.04 | 4.13 | 10.83 | 14.69 | 0.19 | 9 |
11.0 | 140.44 | 160.6 | 245.99 | 0.03 | 0.02 | 0.04 | 10.59 | 8.08 | 14.69 | 0.45 | 4 |
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Share and Cite
Pavliuk, O.; Cupek, R.; Steclik, T.; Medykovskyy, M.; Drewniak, M. A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells. Electronics 2023, 12, 4636. https://doi.org/10.3390/electronics12224636
Pavliuk O, Cupek R, Steclik T, Medykovskyy M, Drewniak M. A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells. Electronics. 2023; 12(22):4636. https://doi.org/10.3390/electronics12224636
Chicago/Turabian StylePavliuk, Olena, Rafal Cupek, Tomasz Steclik, Mykola Medykovskyy, and Marek Drewniak. 2023. "A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells" Electronics 12, no. 22: 4636. https://doi.org/10.3390/electronics12224636
APA StylePavliuk, O., Cupek, R., Steclik, T., Medykovskyy, M., & Drewniak, M. (2023). A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells. Electronics, 12(22), 4636. https://doi.org/10.3390/electronics12224636