1. A tail light assembly for a motorcycle, the tail light assembly comprising:
a circuit including
a light emitting diode (LED) configured to provide at least a visual turn indication associated with the motorcycle, and
a controller having at least one controller input and at least one controller output, the at least one controller output configured to supply current to the LED; and
a housing configured to receive the circuit,
wherein the tail light assembly is configured to draw current having at least a first current magnitude when an input signal corresponding to the visual turn indication is received by the controller, wherein the first current magnitude is substantially equivalent to a magnitude of current drawn by a second tail light assembly employing an incandescent lamp to provide a visual turn indication in place of the LED.
2. The tail light assembly of claim 1 wherein the at least one controller input is electrically connected to a control module external to the tail light assembly.
3. The tail light assembly of claim 1 wherein the LED is red.
4. The tail light assembly of claim 1 wherein the tail light assembly is configured to conform to Federal Motor Vehicle Safety Standard 571.108.
5. The tail light assembly of claim 1 wherein the circuit includes an element to dissipate current when the input signal is received.
6. The tail light assembly of claim 5 wherein the element is a power resistor.
7. The tail light assembly of claim 6 wherein the housing is configured as a heat sink for the power resistor.
8. The tail light assembly of claim 1 wherein the at least one controller input includes a tail input, a stop input, and a turn input.
9. The tail light assembly of claim 8 wherein the tail light assembly is configured to draw current having a second current magnitude when a second input signal corresponding to a visual tail indication is received by the controller.
10. The tail light assembly of claim 9 wherein the tail light assembly is configured to draw current having a third current magnitude when a third input signal corresponding to a visual stop indication is received by the controller.
11. The tail light assembly of claim 10 wherein the first current magnitude is substantially greater than the second current magnitude and the third current magnitude.
12. The tail light assembly of claim 10 further comprising a second LED configured to provide a visual turn indication when the input signal is received, wherein the LED is configured to provide a visual tail indication when the second input signal is received and the LED is configured to provide a visual stop indication when the third input signal is received.
13. The tail light assembly of claim 12 wherein the second LED is amber.
14. The tail light assembly of claim 10 wherein the controller is configured to drive the LED to display a visual tail indication when the controller receives a signal on the tail input only and to display a visual stop indication when the controller receives a signal on the stop input but not on the turn input, the visual tail indication having a first brightness and the visual stop indication having a second brightness.
15. The tail light assembly of claim 14 wherein the second brightness is substantially brighter than the first brightness.
16. The tail light assembly of claim 15 wherein the controller is configured to drive the LED such that the LED oscillates between the first brightness and the second brightness when the controller receives a signal on the turn input.
17. A method of operating a motorcycle tail light assembly including a light emitting diode (LED), a controller having at least one controller input and at least one controller output, and a housing, the method comprising:
receiving an input signal corresponding to a visual turn indication on the at least one controller input;
supplying a current to the LED from the at least one controller output;
providing the visual turn indication; and
drawing, by the tail light assembly, a current having a first current magnitude, the first current magnitude substantially equivalent to a magnitude of current drawn by a second tail light assembly employing an incandescent lamp to provide the visual turn indication in place of the LED.
18. The method of claim 17 and further comprising dissipating current in an element when the input signal is received.
19. A motorcycle comprising:
an engine;
a plurality of wheels; and
a lighting and indication system including
at least one headlight,
a plurality of forward turn signals, and
a plurality of tail light assemblies, each of the tail light assemblies including
a circuit including
a light emitting diode (LED) configured to provide at least a visual turn indication associated with the motorcycle, and
a controller having at least one controller input and at least one controller output, the at least one controller output configured to supply current to the LED; and
a housing configured to receive the circuit,
wherein each tail light assembly is configured to draw current having at least a first current magnitude when an input signal corresponding to the visual turn indication is received by the controller, wherein the first current magnitude is substantially equivalent to a magnitude of current drawn by a second tail light assembly employing an incandescent lamp to provide a visual turn indication in place of the LED.
20. The motorcycle of claim 19 and further comprising a control module electrically connected to the at least one controller input, the control module configured to output the at least one input signal.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method, performed by a computer, of providing service analysis in service level management, comprising:
providing a service over a network having a plurality of network components that support the service, wherein performance of the service depends upon performances of the plurality of network components that support the service, and wherein the service has a state that represents the performance of the service;
measuring a plurality of component parameters for the plurality of network components that support the service across a plurality of domains of the network, wherein the plurality of component parameters measure performances of the plurality of network components that support the service;
determining the state of the service from the plurality of component parameters measured across the plurality of domains of the network, wherein determining the state of the service includes mapping the plurality of component parameters measured across the plurality of domains of the network to a service parameter that represents the state of the service, and wherein the state of the service indicates whether the service conforms to an agreed upon service level identified in a service level agreement;
executing one or more data mining algorithms to discover a respective influence that a first one of the measured component parameters in a first domain of the plurality of domains of the network has on the service parameter;
executing the one or more data mining algorithms to discover a respective influence that a second one of the measured component parameters in a second domain of the plurality of domains of the network has on the service parameter;
identifying one of the first component parameter or the second component parameter that has a greatest influence on the service parameter based on the respective influences that the first component parameter and the second component parameter have on the service parameter; and
designating the identified component parameter to be the service parameter.
2. The method of claim 1, wherein the service parameter represents one or more of a response time for one or more of the plurality of network components, traffic congestion in a selected portion of the network, availability for one or more of the plurality of network components, reliability for one or more of the plurality of network components, security for one or more of the plurality of network components, performance for one or more of the plurality of network components, or a configuration for one or more of the plurality of network components.
3. The method of claim 1, wherein a plurality of monitoring agents are configured to measure the plurality of component parameters for the plurality of network components that support the service.
4. The method of claim 3, wherein each of the plurality of monitoring agents are configured to measure a subset of the plurality of component parameters in a respective domain of the plurality of domains of the network.
5. The method of claim 4, wherein the one or more data mining algorithms include an inductive logic algorithm that comprises:
incorporating knowledge relating to the plurality of domains of the network and knowledge relating to the plurality of measured component parameters within a rule base;
inferring the respective influences that each of the plurality of measured component parameters have on the service parameter using the knowledge incorporated within the rule base; and
producing one or more of propositional statements or quantified statements that express the respective influences that one or more of the plurality of component parameters have on the service parameter.
6. The method of claim 1, further comprising integrating the plurality of domains into a service level management domain that comprises a hierarchical structure for the plurality of domains.
7. The method of claim 1, wherein designating the identified component parameter to be the service parameter includes
designating the first component parameter to be the service parameter
in response to determining that the respective influence that the first component parameter has on the service parameter is greater than
the respective influence that the second component parameter has on the service parameter.
8. The method of claim 1, wherein designating the identified component parameter to be the service parameter includes designating the second component parameter to be the service parameter in response to determining that the respective influence that the second component parameter has on the service parameter is greater than the respective influence that the first component parameter has on the service parameter.
9. The method of claim 1, wherein the one or more data mining algorithms are executed while the plurality of network components are operating to support the service.
10. The method of claim 1, wherein the one or more data mining algorithms include a neural network algorithm that comprises:
identifying a case library that includes a plurality of cases representing episodes of problem solving;
applying a plurality of relevance rules to identify one or more of the cases in the case library that are relevant to discovering the respective influences for the plurality of measured component parameters; and
adapting one or more solutions variables associated with the identified cases using parameterized adaption logic to discover the respective influences that each of the plurality of measured component parameters have on the service parameter.
11. The method of claim 1, wherein the one or more data mining algorithms include a decision tree algorithm that comprises producing a decision tree that represents the respective influences that each of the plurality of measured component parameters have on the service parameter.
12. The method of claim 11, wherein the decision tree includes one or more of numeric values or binary values that represent the respective influences that each of the plurality of measured component parameters have on the service parameter.
13. The method of claim 11, wherein the decision tree includes a root node that represents the service parameter, a plurality of leaf nodes that respectively represent the plurality of component parameters, and a plurality of dependencies between the root node and the plurality of leaf nodes that represent the respective influences that each of the plurality of component parameters have on the service parameter.
14. The method of claim 1, wherein the one or more data mining algorithms include a top N algorithm that comprises:
identifying a predetermined number of the plurality of measured component parameters that have a greatest influence on the service parameter; and
producing a list that includes the identified component parameters having the greatest influence on the service parameter, wherein the identified component parameters are listed in a decreasing order of the respective influences that the identified component parameters have on the service parameter.
15. The method of claim 1, wherein the one or more data mining algorithms include a rule induction algorithm that comprises producing one or more rules that represent the respective influences that one or more of the plurality of measured component parameters have on the service parameter.
16. The method of claim 15, wherein the one or more rules include one or more of propositional statements or quantified statements that represent the respective influences that the one or more component parameters have on the service parameter.
17. The method of claim 1, wherein the one or more data mining algorithms include a fuzzy logic algorithm that comprises:
translating the plurality of measured component parameters into a plurality of respective fuzzy concepts;
determining grades of membership that the measured component parameters have in the respective fuzzy concepts, wherein the grades of membership quantify transitions between a plurality of states in a state transition graph; and
inferring the respective influences that each of the plurality of measured component parameters have on the service parameter from the grades of membership that the measured component parameters have in the respective fuzzy concepts.
18. A device readable medium storing device executable instructions for executing a method of providing service analysis in service level management, the method comprising:
providing a service over a network having a plurality of network components that support the service, wherein performance of the service depends upon performances of the plurality of network components that support the service, and wherein the service has a state that represents the performance of the service;
measuring a plurality of component parameters for the plurality of network components that support the service across a plurality of domains of the network, wherein the plurality of component parameters measure performances of the plurality of network components that support the service;
determining the state of the service from the plurality of component parameters measured across the plurality of domains of the network, wherein determining the state of the service includes mapping the plurality of component parameters measured across the plurality of domains of the network to a service parameter that represents the state of the service, and wherein the state of the service indicates whether the service conforms to an agreed upon service level identified in a service level agreement;
executing one or more data mining algorithms to discover a respective influence that a first one of the measured component parameters in a first domain of the plurality of domains of the network has on the service parameter;
executing the one or more data mining algorithms to discover a respective influence that a second one of the measured component parameters in a second domain of the plurality of domains of the network has on the service parameter;
identifying one of the first component parameter or the second component parameter that has a greatest influence on the service parameter based on the respective influences that the first component parameter and the second component parameter have on the service parameter; and
designating the identified component parameter to be the service parameter.
19. The medium of claim 18, wherein the service parameter represents one or more of a response time for one or more of the plurality of network components, traffic congestion in a selected portion of the network, availability for one or more of the plurality of network components, reliability for one or more of the plurality of network components, security for one or more of the plurality of network components, performance for one or more of the plurality of network components, or a configuration for one or more of the plurality of network components.
20. The medium of claim 18, wherein a plurality of monitoring agents are configured to measure the plurality of component parameters for the plurality of network components that support the service.
21. The medium of claim 20, wherein the method further comprises the plurality of monitoring agents are configured to measure a subset of the plurality of component parameters in a respective domain of the plurality of domains of the network.
22. The medium of claim 21, wherein the one or more data mining algorithms include an inductive logic algorithm that comprises:
incorporating knowledge relating to the plurality of domains of the network and knowledge relating to the plurality of measured component parameters within a rule base;
inferring the respective influences that each of the plurality of measured component parameters have on the service parameter using the knowledge incorporated within the rule base; and
producing one or more of propositional statements or quantified statements that express the respective influences that one or more of the plurality of component parameters have on the service parameter.
23. The medium of claim 18, wherein the method further comprises integrating the plurality of domains into a service level management domain that comprises a hierarchical structure for the plurality of domains.
24. The medium of claim 18, wherein designating the identified component parameter to be the service parameter includes
designating the first component parameter to be the service parameter
in response to determining that the respective influence that the first component parameter has on the service parameter is greater than
the respective influence that the second component parameter has on the service parameter.
25. The medium of claim 18, wherein designating the identified component parameter to be the service parameter includes designating the second component parameter to be the service parameter in response to determining that the respective influence that the second component parameter has on the service parameter is greater than the respective influence that the first component parameter has on the service parameter.
26. The medium of claim 18, wherein the one or more data mining algorithms include a neural network algorithm that comprises:
identifying a case library that includes a plurality of cases representing episodes of problem solving;
applying a plurality of relevance rules to identify one or more of the cases in the case library that are relevant to discovering the respective influences for the plurality of measured component parameters; and
adapting one or more solutions variables associated with the identified, cases using parameterized adaption logic to discover the respective influences that each of the plurality of measured component parameters have on the service parameter.
27. The medium of claim 18, wherein the one or more data mining algorithms include a decision tree algorithm that comprises producing a decision tree that represents the respective influences that each of the plurality of measured component parameters have on the service parameter.
28. The medium of claim 27, wherein the decision tree includes one or more of numeric values or binary values that represent the respective influences that each of the plurality of measured component parameters have on the service parameter.
29. The medium of claim 27, wherein the decision tree includes a root node that represents the service parameter, a plurality of leaf nodes that respectively represent the plurality of component parameters, and a plurality of dependencies between the root node and the plurality of leaf nodes that represent the respective influences that each of the plurality of component parameters have on the service parameter.
30. The medium of claim 18, wherein the one or more data mining algorithms include a top N algorithm that comprises:
identifying a predetermined number of the plurality of measured component parameters that have a greatest influence on the service parameter; and
producing a list that includes the identified component parameters having the greatest influence on the service parameter, wherein the identified component parameters are listed in a decreasing order of the respective influences that the identified component parameters have on the service parameter.
31. The medium of claim 18, wherein the one or more data mining algorithms include a rule induction algorithm that comprises producing one or more rules that represent the respective influences that one or more of the plurality of measured component parameters have on the service parameter.
32. The medium of claim 31, wherein the one or more rules include one or more of propositional statements or quantified statements that represent the respective influences that the one or more component parameters have on the service parameter.
33. The medium of claim 18, wherein the one or more data mining algorithms include a fuzzy logic algorithm that comprises:
translating the plurality of measured component parameters into a plurality of respective fuzzy concepts;
determining grades of membership that the measured component parameters have in the respective fuzzy concepts, wherein the grades of membership quantify transitions between a plurality of states in a state transition graph; and
inferring the respective influences that each of the plurality of measured component parameters have on the service parameter from the grades of membership that the measured component parameters have in the respective fuzzy concepts.