Abstract: Systems and methods are provided for verifying the placement of tows by a robot. One embodiment includes a robot that includes an end effector that lays up tows, actuators that reposition the end effector, a memory storing a Numerical Control (NC) program, and a robot controller that directs the actuators to reposition the end effector based on the NC program, and instructs the end effector to lay up tows based on the NC program. The system also includes a sensor system comprising an imaging device that acquires images of the tows as the tows are laid-up, a measuring device that generates input as tows are laid-up by the end effector, and a sensor controller that receives images from the imaging device and the input from the measuring device, and updates stored data to correlate the images with instructions in the NC program, based on the input.

Abstract: Systems and methods are provided for verifying the placement of tows by a robot. One embodiment includes a robot that includes an end effector that lays up tows, actuators that reposition the end effector, a memory storing a Numerical Control (NC) program, and a robot controller that directs the actuators to reposition the end effector based on the NC program, and instructs the end effector to lay up tows based on the NC program. The system also includes a sensor system comprising an imaging device that acquires images of the tows as the tows are laid-up, a measuring device that generates input as tows are laid-up by the end effector, and a sensor controller that receives images from the imaging device and the input from the measuring device, and updates stored data to correlate the images with instructions in the NC program, based on the input.

Abstract: Systems and methods are provided for verifying the placement of tows by a robot. One embodiment includes a robot that includes an end effector that lays up tows, actuators that reposition the end effector, a memory storing a Numerical Control (NC) program, and a robot controller that directs the actuators to reposition the end effector based on the NC program, and instructs the end effector to lay up tows based on the NC program. The system also includes a sensor system comprising an imaging device that acquires images of the tows as the tows are laid-up, a measuring device that generates input as tows are laid-up by the end effector, and a sensor controller that receives images from the imaging device and the input from the measuring device, and updates stored data to correlate the images with instructions in the NC program, based on the input.

Abstract: Systems and methods are provided for verifying the placement of tows by a robot. One embodiment includes a robot that includes an end effector that lays up tows, actuators that reposition the end effector, a memory storing a Numerical Control (NC) program, and a robot controller that directs the actuators to reposition the end effector based on the NC program, and instructs the end effector to lay up tows based on the NC program. The system also includes a sensor system comprising an imaging device that acquires images of the tows as the tows are laid-up, a measuring device that generates input as tows are laid-up by the end effector, and a sensor controller that receives images from the imaging device and the input from the measuring device, and updates stored data to correlate the images with instructions in the NC program, based on the input.

Abstract: Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. An implementation of the DCA may detect anomalous behavior in various processes of a device or devices. Unlike previous approaches, the DCA implementation may use an inflammation signal to communicate information among the processes of device or a network, where the inflammatory signal indicates a likelihood that a process has been attacked by malicious software.

Abstract: Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. A DCA module may receive an output or signal from multiple indicators concerning the state of at least a portion of the system. The DCA module is configured to combine the plurality of signals into a single signal vector. The DCA module may be configured to sort the received signals based on signal type and magnitude of each signal. The DCA module may then use a decay factor to weight the received signals so that a large number of “nominal” signals do not drown out a small number of “strong” signals indicating a malware attack. The decay factor may be exponentially increased each time it is applied so that all received signals are considered by the DCA module, but so that the “nominal” signals may have a minimal effect.

Abstract: Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. The DCA implementation may use an inflammation signal to communicate information among the processes of device or a network or among nodes of a network, where the inflammatory signal indicates a likelihood that a process or a node has been attacked by malicious software. The DCA implementation may dynamically change the malware sensitivity and responsiveness based on the inflammation signals without requiring user intervention. The inflammatory signal includes one or more inflammatory tuples, which may include multiple components such as a strength, a PrimeIndicator, and an optional third element, p. The strength component may be an indication of the magnitude of an attack and provide a degree of certainty of the attack. The PrimeIndicator may be an identifier of the indicator type that is the source of the inflammation tuple.

Abstract: Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. A DCA module may receive an output or signal from multiple indicators concerning the state of at least a portion of the system. The DCA module is configured to combine the plurality of signals into a single signal vector. The DCA module may be configured to sort the received signals based on signal type and magnitude of each signal. The DCA module may then use a decay factor to weight the received signals so that a large number of “nominal” signals do not drown out a small number of “strong” signals indicating a malware attack. The decay factor may be exponentially increased each time it is applied so that all received signals are considered by the DCA module, but so that the “nominal” signals may have a minimal effect.

Abstract: Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. The DCA implementation may use an inflammation signal to communicate information among the processes of device or a network or among nodes of a network, where the inflammatory signal indicates a likelihood that a process or a node has been attacked by malicious software. The DCA implementation may dynamically change the malware sensitivity and responsiveness based on the inflammation signals without requiring user intervention. The inflammatory signal includes one or more inflammatory tuples, which may include multiple components such as a strength, a PrimeIndicator, and an optional third element, p. The strength component may be an indication of the magnitude of an attack and provide a degree of certainty of the attack. The PrimeIndicator may be an identifier of the indicator type that is the source of the inflammation tuple.

Abstract: Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. An implementation of the DCA may detect anomalous behavior in various processes of a device or devices. Unlike previous approaches, the DCA implementation may use an inflammation signal to communicate information among the processes of device or a network, where the inflammatory signal indicates a likelihood that a process has been attacked by malicious software.