Abstract: Systems and methods for utility pole localization employing a DFOS/DAS interrogator located at one end of an optical sensor fiber remotely capture dynamic strains on the optical sensor fiber induced by acoustic events. A captured two-dimensional spatiotemporal map in an ambient noisy environment is analyzed by a trained machine learning model which then automatically detects an area in which a pole is located without requiring domain knowledge. Original DFOS/DAS signals are separated into pole regions and non-pole region time series for machine learning model training. A contrastive loss function measures similarities between low-frequency and high-frequency features. A Gaussian distribution is applied to the original signals to generate weighted labels to eliminate effects of label noise. The machine learning model fuses low-frequency and high-frequency features in the frequency domain for pole region classification.

Abstract: In sharp contrast to the prior art, a fallen tree detection and localization method based on distributed fiber optical sensing (DFOS) technique and physics informed machine learning is described in which DFOS leverages existing fiber cables that are conventionally installed on the bottom layer of distribution lines and used to provide high-speed communications. The DFOS collects and transmits fallen tree induced vibration data along the length of the entire overhead lines, including distribution lines and transmission lines, where there is a fiber cable deployed. The developed physics-informed neural network model processes the data and localizes the fallen tree location along the lines. The location is interpreted in at least two aspects: the fallen tree location in terms of the fiber cable length; and the exact cable location (power cable or fiber cable) that the fallen tree mechanically impacts.

Abstract: Computer vision based, wide-area snow/water level estimation methods using disparity maps. In one embodiment our method provides rich depth-information using a stereo camera and image processing. Scene images at normal and snow/rain weather conditions are obtained by a double-lens stereo camera and a disparity map is generated from the scene images at left and right lenses using a self-supervised deep convolutional network. In another embodiment, our method uses a single point snow/water level sensor, a stationary monocular camera to measure snow/water levels covering a wide area.

Abstract: A radio-controlled, two-way acoustic modem for operating with a distributed fiber optic sensing (DFOS) system including circuitry that receives radio signals including configuration information, configures the modem to operate according to the configuration information, and generate acoustic signals that are detected by the DFOS system. The acoustic modem includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The received configuration information may change the operating times, sensors or other operating aspects of the modem as desired and such information may be transmitted from a fixed location or a mobile vehicle.

Abstract: A vehicle capable of taking off and landing vertically and operating in water, land, air and submarine environments includes a fuselage, two main wings, ailerons, a vertical tail, a rudder, a horizontal tail, elevators, a propeller, rotor wings, rotor wing supports, etc. The vehicle has the advantages of adaptability to various environments, good concealment and strong survivability. Compared with a traditional unmanned rotorcraft, the vehicle has longer endurance time and larger load. Compared with a fixed wing UAV, the vertical take-off and landing function makes the work more convenient. Compared with unmanned diving equipment, the vehicle is applicable to richer environments, and can complete designated missions in air, land, water and submarine environments. Compared with a tilt rotor UAV in water, land, air and submarine environments, the vehicle is rapider in switching of various modes and is higher in stability.

Abstract: A vehicle capable of taking off and landing vertically and operating in water, land, air and submarine environments includes a fuselage, two main wings, ailerons, a vertical tail, a rudder, a horizontal tail, elevators, a propeller, rotor wings, rotor wing supports, etc. The vehicle has the advantages of adaptability to various environments, good concealment and strong survivability. Compared with a traditional unmanned rotorcraft, the vehicle has longer endurance time and larger load. Compared with a fixed wing UAV, the vertical take-off and landing function makes the work more convenient. Compared with unmanned diving equipment, the vehicle is applicable to richer environments, and can complete designated missions in air, land, water and submarine environments. Compared with a tilt rotor UAV in water, land, air and submarine environments, the vehicle is rapider in switching of various modes and is higher in stability.