Using ROC and Unlabeled Data for Increasing Low-Shot Transfer Learning Classification Accuracy

One of the most important characteristics of human visual intelligence is the ability to identify unknown objects. The capability to distinguish between a substance which a human mind has no previous experience of and a familiar object, is innate to every human. In everyday life, within seconds of seeing an "unknown" object, we are able to categorize it as such without any substantial effort. Convolutional Neural Networks, regardless of how they are trained (i.e. in a conventional manner or through transfer learning) can recognize only the classes that they are trained for. When using them for classification, any candidate image will be placed in one of the available classes. We propose a low-shot classifier which can serve as the top layer to any existing CNN that the feature extractor was already trained. Using a limited amount of labeled data for the type of images which need to be specifically classified along with unlabeled data for all other images, a unique target matrix and a Receiver Operator Curve (ROC) criterion, we are able to increase identification accuracy by up to 30% for the images that do not belong to any specific classes, while retaining the ability to identify images that belong to the specific classes of interest

Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images

In search exploration and reconnaissance tasks performed with autonomous ground vehicles an image classification capability is needed for specifically identifying targeted objects relevant classes and at the same time recognize when a candidate image does not belong to anyone of the relevant classes irrelevant images In this paper we present an open-set low-shot classifier that uses during its training a modest number less than 40 of labeled images for each relevant class and unlabeled irrelevant images that are randomly selected at each epoch of the training process The new classifier is capable of identifying images from the relevant classes determining when a candidate image is irrelevant and it can further recognize categories of irrelevant images that were not included in the training unseen The proposed low-shot classifier can be attached as a top layer to any pre-trained feature extractor when constructing a Convolutional Neural Networ

Remotely Operated Aerial Vehicles and Their Applications

This project examines relevant designs and applications of unmanned aerial vehicles (UAVs). We propose UAV design solutions, which can be refined and incorporated into emergency medical services. Mathematical and engineering concepts are used to select the design solutions. We believe that the proposed design solutions will enhance the quality of care in emergency medical services

Estimation of a Plume with an Unmanned Terrestrial Vehicle

This work involves the design and implementation of a gas-sensing mobile robot as an experimental tool to localize a carbon dioxide source. The autonomous robot achieves navigation through an embedded microcontroller using a strap-down accelerometer and a fusion of four CO2 sensors. A mass flow controller and diffuser are used to dependably generate a plume that simulates a point source. A base station receives sensor data and calculates the robots position using the accelerometer data filtered using a low pass filter followed an Extended Kalman Filter. This method has applicability for unmanned vehicles tracking emissions of contaminants and their effects in the environment

Review of solar energetic particle models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.</p

Processing Image Data from Unstructured Environments

Advanced mobility research centers capture large amounts of data from ground vehicle systems during development and experimentation in both manned and autonomous operations. This exponential growth of digital image data has given rise to the need of understanding the content of image datasets by clustering and classifying them without the use of manual labor. Currently, there is a lack of tools which -through processing raw data- can provide a semantic understanding of an environment or dataset and can be used in place of a human to provide context to situations that threaten the uninterrupted operation of an autonomous vehicle. In search, exploration, and reconnaissance tasks performed with autonomous ground vehicles, an image classification capability is needed for specifically identifying targeted objects (relevant classes) and at the same time recognize when a candidate image does not belong to anyone of the relevant classes (irrelevant images). An open-set low-shot (OSLS) classifier was developed for addressing this need. During its training, it uses a modest number (less than 40) of labeled images for each relevant class, and unlabeled irrelevant images that are randomly selected at each epoch of the training process. The new OSLS classifier is capable of identifying images from the relevant classes, determining when a candidate image is irrelevant, and it can further recognize categories of irrelevant images that were not included in the training (unseen). The OSLS was integrated with an unsupervised learning feature extraction framework based on the instance discrimination method for creating an instance discrimination low shot (IDLS) module. The IDLS can identify targeted objects while at the same time recognize when candidate images do not belong to any one of the target classes, both in a very data-inexpensive way. The IDLS is dynamic, adapts to new environments during operation and is resilient to adversaries. The OSLS and IDLS algorithms were compared to a variety of alternative supervised methods showing comparable and often times better results in performing classification tasks, while requiring very few labeled images for training (i.e. less that 0.3% of labeled data compared to a supervised CNN for comparable levels of accuracy). This work also developed a soft-labeling capability for grouping collected images into categories using a new formulation that is based on an extended variance ratio criterion (E-VRC). The E-VRC comprises an unsupervised clustering capability since it does not require any initializations or prior knowledge about how many clusters will be encountered. As it is done with the previous two modules (OSLS and IDLS), the E-VRC too is being tested on several different datasets, demonstrating that it is useful not only for autonomous exploration and reconnaissance operations but also for the efficient content management and retrieval tasks. Additionally, the E-VRC algorithm developed by this research was compared to other available unsupervised clustering methods yielding superior results.PhDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/176558/1/skasapis_1.pd

Review of Solar Energetic Particle Models

Solar Energetic Particles (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to imific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data