Abstract-Rivers with heavy vegetation are hard to map from the air. Here we consider the task of mapping their course and the vegetation along the shores with the specific intent of determining river width and canopy height. A complication in such riverine environments is that only intermittent GPS may be available depending on the thickness of the surrounding canopy. We present a multimodal perception system to be used for the active exploration and mapping of a river from a small rotorcraft flying a few meters above the water. We describe three key components that use computer vision, laser scanning, and inertial sensing to follow the river without the use of a prior map, estimate motion of the rotorcraft, ensure collisionfree operation, and create a three dimensional representation of the riverine environment. While the ability to fly simplifies the navigation problem, it also introduces an additional set of constraints in terms of size, weight and power. Hence, our solutions are cognizant of the need to perform multi-kilometer missions with a small payload. We present experimental results along a 2km loop of river using a surrogate system.
US military forces now face asymmetric military operations. Management of relationships with civilians is often crucial to success. Local population groups can provide critical intelligence or be sources of increasingly violent insurgent activity. A variety of organizations that are neither citizens nor military forces complicate the scenario. Mission readiness and rehearsal training are evolving to respond to this new operating environment. In particular, the Joint Land Component Constructive Training Capability (JLCCTC) adds the Joint Non-kinetic Effects Model (JNEM) and the Independent Stimulation Module (ISM) to any of several combat models. JNEM models the nonkinetic effects of joint military operations on the attitudes and reactions of civilian population groups. ISM manages the flow and delivery of information. All components of JLCCTC communicate in real time during training. Commanders learn that appropriate actions improve the situation (e.g., better cooperation) and inappropriate actions make things worse (e.g., increased numbers of insurgents).
A method has been developed that locates and determines well-to-well hydraulic fracture interference (frac-hit) in shale plays using hard data. This method uses Artificial Neural Networks (ANN) with designated parameters and target outputs in conjunction with graphs of gas flowrate, tubing pressure, and cumulative gas prediction. The method was created to address the significant increase in frac-hit occurrences due to the infill wells being completed in shale plays. The production data of the well is first cleaned to eliminate outliers in the initial timeframe of the well and periods of no production so that the ANN model can be accurately trained. The model then predicts daily gas flowrate and is graphed against the wells cumulative gas and tubing pressures. The location of the section of variance from real data versus the predicted results will indicate a phenomenon at a given instant. This can indicate frac-hits through graphing a plot of a parent wells tubing pressure, gas flowrate, and cumulative gas production against a new child well at the location of variance that was observed in the model prediction. The results of ANN training and test results accurately predicted cases where frac-hits are observed in the given field. This model also was able to predict the onset of the frac-hit which correlated to the same time that a new well was being completed in the area. This method allowed further research into the results since it was able to provide predicted flowrates at the time periods of frac-hits rather than only the time of the hit. Therefore, the ANN model was determined to be an adequate choice in analyzing frac-hits due to the sheer volume of information that can be taken away from the results.
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