A Short Study on Bias Present in Classical Random Processes

Sometimes, outcomes of random processes don’t seem to follow the theoretical probabilities due to the presence of bias and even when the probabilities are followed in a large number of trials, dynamic bias is still evident in many of these processes. This paper provides a short study on the bias using examples and defines what kind of processes could be biased. It also demonstrates the two types of bias which are dynamic and fixed. This study could be used to analyze the bias in various random processes and get a better understanding of the outcomes. Dynamic bias has further been explained with the help of 52 cards. The study helps in providing a better understanding of randomness and further helps in designing experiments.

THE MODELS OF AUTO TARGETING STRIKE UAV AT THE MOVING TARGET

A special kind of use of UAVs as transport is their use to deliver the destruction means to the target on the battlefield, i.e. as strike unmanned aerial vehicles (UAVs), capable to inflict missile and bomb strikes on the enemy. Of particular interest are such drones, which do not require constant aiming at the target by the flight operator (ground pilot) with the help of a video transmitting system, but are targeted independently. Such UAVs have onboard a target sighting sensor (coordinator), with which the onboard control system determines the angular position of the target relative to the UAV. It is necessary to determine the most optimal targeting algorithms to be used in onboard control systems. UAVs due to the successful combination of cheap goods, technological availability and availability of other technical indicators (load capacity, efficiency and duration of flight, maneuverability) can be used as vehicles for cargo delivery. For example, UAVs are used to deliver ammunition and medicine to victims. The mechanism of the primary notification to the on-board control system of the UAV of the coordinates of the target to be hit, ie the task of "primary targeting", remains not completely solved by the technical and organizational task when using strike UAVs. Targeting methods using the on-board video broadcast system and direct commands of the ground pilot require, firstly, the installation of appropriate payload on board with an understanding of the loss of this equipment in combat use of UAVs, and secondly, effective avoidance of radio suppression by the enemy. The article presents the results of modeling with the help of Simulink models of the processes of guidance of a strike UAV with targeting on a moving aim by two methods: direct guidance and dynamic bias. The trajectories of UAV flights during diving to the target were obtained. The changes in the main working parameters during the guidance process were obtained as well. The conclusion about the advantage of the method of dynamic bias is made.

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor

This paper describes the possibility of using an Electrically Programmable Analog Device (EPAD) as a gamma radiation sensor. Zero-biased EPAD has the lowest fading and the highest sensitivity in the 300 Gy dose range. Dynamic bias of the control gate during irradiation was presented for the first time; this method achieved higher sensitivity compared to static-biased EPADs and better linear dependence. Due to the degradation of the transfer characteristics of EPAD during irradiation, a function of the safe operation area has been found that determines the maximum voltage at the control gate for the desired dose, which will not lead to degradation of the transistor. Using an energy band diagram, it was explained why the zero-biased EPAD has higher sensitivity than the static-biased EPAD.