Abstract: A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

Abstract: A training set that includes at least two data types corresponding to operations and control of a manufacturing process is obtained. A deep learning processor is trained to predict expected characteristics of output control signals that correspond with one or more corresponding input operating instructions. A first input operating instruction is received from a first signal splitter. A first output control signal is received from a second signal splitter. The deep learning processor correlates the first input operating instruction and the first output control signal. Based on the correlating, the deep learning processor determines that the first output control signal is not within a range of expected values based on the first input operating instruction. Responsive to the determining, an indication of an anomalous activity is provided as a result of detection of the anomalous activity in the manufacturing process.

Abstract: A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.

Abstract: A PEG tube support and comfort system for providing support for a patient's PEG feeding tube and comfort for the patient. The PEG tube support and comfort system including a unique pillow-like, padded support device that is worn by the patient at or near the incision site. The support device including a flat portion that rests against the waist of the patient and a crescent-shaped, padded, pillow-like portion that extends outwardly from the flat portion and which provides a support or resting place for the PEG tube. The flat portion including a pouch or pocket which provides protected space for the patient to place and store the extra PEG tubing. The PEG tube and comfort system also including a strap that is wrapped around the waist area of the patient and which holds the support device in place against the waist of the patient.