Detect CANBUS Patterns using Machine Learning
I would like to use AI/TensorFlow/Machine Learning of some description in order to recognise patterns in a set of data.
Most of the samples of machine learning seem to be based on decision making, whether a value falls above or a below a line, then the decision is good or bad. But what I seem to have is a set of data, that may or may not have any relationship, may or may not have a pattern, and a single entity by itself is neither good nor bad, but the whole set of data together needs to be used to work out the type of data.
The data in question is a set of readings over a period of time from an automotive CANBUS reader - so hexadecimal values containing a Command ID, and 1 or more values, usually in the format: FFF#FF:FF:FF:FF:FF:FF:FF:FF
eg:
35C#F4:C6:4E:7C:31:2B:60:28
One canbus command, may contain one or more sensor readers - so for example F4 above might represent the steering position, C6 might indicate the pitch of the vehicle, 4E might indicate the roll.
Each sensor may take up one or more octets, 7C:31 might indicate the speed of the vehicle, and "B" of "2B" might indicate whether the engine is running or not.
I can detect the data with a human eye, and can see that the relevent item might be linear, random, static (ie a limited set of values) or it might be a bell curve.
Im new to statistical analysis and machine learning so do not know the terminology. In the first instance Im looking for the terminology and references to appropriate material that will help me achieve my goal.
My goal is given a sample of data from a CANBUS reader, to scan all the values, using each and every possible combination of numbers (eg octets 1, 1+2, 1+2+3... 2, 2+3, 2+3+4... 3, 3+4 etc) to detect patterns within the data and work out whether those patterns are linear, curve, static, or random.
I want to basically read as many CAN-BUS readings from as many cars as I can, throw it at a program to do some analysis and learning and hopefully provide me with possibilities to investigate further so I can monitor various systems on different cars.
It seems like a relatively simple premise, but extremely hard for me to define.
tensorflow machine-learning
asked Nov 15 '18 at 17:09
SimonSimon
6451819
add a comment |
I would like to use AI/TensorFlow/Machine Learning of some description in order to recognise patterns in a set of data.
Most of the samples of machine learning seem to be based on decision making, whether a value falls above or a below a line, then the decision is good or bad. But what I seem to have is a set of data, that may or may not have any relationship, may or may not have a pattern, and a single entity by itself is neither good nor bad, but the whole set of data together needs to be used to work out the type of data.
The data in question is a set of readings over a period of time from an automotive CANBUS reader - so hexadecimal values containing a Command ID, and 1 or more values, usually in the format: FFF#FF:FF:FF:FF:FF:FF:FF:FF
eg:
35C#F4:C6:4E:7C:31:2B:60:28
One canbus command, may contain one or more sensor readers - so for example F4 above might represent the steering position, C6 might indicate the pitch of the vehicle, 4E might indicate the roll.
Each sensor may take up one or more octets, 7C:31 might indicate the speed of the vehicle, and "B" of "2B" might indicate whether the engine is running or not.
I can detect the data with a human eye, and can see that the relevent item might be linear, random, static (ie a limited set of values) or it might be a bell curve.
Im new to statistical analysis and machine learning so do not know the terminology. In the first instance Im looking for the terminology and references to appropriate material that will help me achieve my goal.
My goal is given a sample of data from a CANBUS reader, to scan all the values, using each and every possible combination of numbers (eg octets 1, 1+2, 1+2+3... 2, 2+3, 2+3+4... 3, 3+4 etc) to detect patterns within the data and work out whether those patterns are linear, curve, static, or random.
I want to basically read as many CAN-BUS readings from as many cars as I can, throw it at a program to do some analysis and learning and hopefully provide me with possibilities to investigate further so I can monitor various systems on different cars.
It seems like a relatively simple premise, but extremely hard for me to define.
tensorflow machine-learning
asked Nov 15 '18 at 17:09
SimonSimon
6451819
add a comment |
I would like to use AI/TensorFlow/Machine Learning of some description in order to recognise patterns in a set of data.
Most of the samples of machine learning seem to be based on decision making, whether a value falls above or a below a line, then the decision is good or bad. But what I seem to have is a set of data, that may or may not have any relationship, may or may not have a pattern, and a single entity by itself is neither good nor bad, but the whole set of data together needs to be used to work out the type of data.
The data in question is a set of readings over a period of time from an automotive CANBUS reader - so hexadecimal values containing a Command ID, and 1 or more values, usually in the format: FFF#FF:FF:FF:FF:FF:FF:FF:FF
eg:
35C#F4:C6:4E:7C:31:2B:60:28
One canbus command, may contain one or more sensor readers - so for example F4 above might represent the steering position, C6 might indicate the pitch of the vehicle, 4E might indicate the roll.
Each sensor may take up one or more octets, 7C:31 might indicate the speed of the vehicle, and "B" of "2B" might indicate whether the engine is running or not.
I can detect the data with a human eye, and can see that the relevent item might be linear, random, static (ie a limited set of values) or it might be a bell curve.
Im new to statistical analysis and machine learning so do not know the terminology. In the first instance Im looking for the terminology and references to appropriate material that will help me achieve my goal.
My goal is given a sample of data from a CANBUS reader, to scan all the values, using each and every possible combination of numbers (eg octets 1, 1+2, 1+2+3... 2, 2+3, 2+3+4... 3, 3+4 etc) to detect patterns within the data and work out whether those patterns are linear, curve, static, or random.
I want to basically read as many CAN-BUS readings from as many cars as I can, throw it at a program to do some analysis and learning and hopefully provide me with possibilities to investigate further so I can monitor various systems on different cars.
It seems like a relatively simple premise, but extremely hard for me to define.
tensorflow machine-learning
asked Nov 15 '18 at 17:09
SimonSimon
6451819
I would like to use AI/TensorFlow/Machine Learning of some description in order to recognise patterns in a set of data.
Most of the samples of machine learning seem to be based on decision making, whether a value falls above or a below a line, then the decision is good or bad. But what I seem to have is a set of data, that may or may not have any relationship, may or may not have a pattern, and a single entity by itself is neither good nor bad, but the whole set of data together needs to be used to work out the type of data.
The data in question is a set of readings over a period of time from an automotive CANBUS reader - so hexadecimal values containing a Command ID, and 1 or more values, usually in the format: FFF#FF:FF:FF:FF:FF:FF:FF:FF
eg:
35C#F4:C6:4E:7C:31:2B:60:28
One canbus command, may contain one or more sensor readers - so for example F4 above might represent the steering position, C6 might indicate the pitch of the vehicle, 4E might indicate the roll.
Each sensor may take up one or more octets, 7C:31 might indicate the speed of the vehicle, and "B" of "2B" might indicate whether the engine is running or not.
I can detect the data with a human eye, and can see that the relevent item might be linear, random, static (ie a limited set of values) or it might be a bell curve.
Im new to statistical analysis and machine learning so do not know the terminology. In the first instance Im looking for the terminology and references to appropriate material that will help me achieve my goal.
My goal is given a sample of data from a CANBUS reader, to scan all the values, using each and every possible combination of numbers (eg octets 1, 1+2, 1+2+3... 2, 2+3, 2+3+4... 3, 3+4 etc) to detect patterns within the data and work out whether those patterns are linear, curve, static, or random.
I want to basically read as many CAN-BUS readings from as many cars as I can, throw it at a program to do some analysis and learning and hopefully provide me with possibilities to investigate further so I can monitor various systems on different cars.
It seems like a relatively simple premise, but extremely hard for me to define.
tensorflow machine-learning
tensorflow machine-learning
asked Nov 15 '18 at 17:09
SimonSimon
6451819
asked Nov 15 '18 at 17:09
SimonSimon
6451819
asked Nov 15 '18 at 17:09
SimonSimon
6451819
asked Nov 15 '18 at 17:09
SimonSimon
6451819
asked Nov 15 '18 at 17:09
SimonSimon
6451819
6451819
add a comment |
add a comment |
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