Poster Presentation at Intern Review; 29 Jul. 2013; Houston, TX; United States
Documentation and Information Science
The need to preserve works and NASA documented articles is done via the collection of various Space Environments and Effects (SEE) related articles. (SEE) contains and lists the various projects that are ongoing, or have been conducted with the help of NASA. The goal of the (SEE) program is to make publicly available the environment technologies that are required to design, manufacture and operate reliable, cost-effective spacecraft for the government and commercial sectors. Of the many projects contained within the (SEE) program the Lunar-E Library and Spacecraft Materials Selector (SMS) have been selected for a more user friendly means to make the tools easily available to the public. This information which is still available required a person or entity to request access from a point of contact at NASA and wait for the requested bundled software DVD via postal service. Redesigning the material presentation and availability has been mapped to a single step process with faster turnaround time via Materials and Processes Technical Information System (MAPTIS) database. This process requires users to register and be verified in order to gain access to the information contained within. Aiding in the progression of making the software tools/documents available required a combination of specialized in-house data gathering software tools and software archeology.
An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item to present to the expert for labeling. Experiments on several data sets have demonstrated that the Relevance Bias approach significantly decreases the number of irrelevant items queried and also accelerates learning speed.
JPL, A Decade of Neural Networks: Practical Applications and Prospects; p 23-28
Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.
Gader, Paul, Chiang, Jung-Hsien, and Mohamed, Magdi
NASA. Johnson Space Center, North American Fuzzy Logic Processing Society (NAFIPS 1992), Volume 1; p 257-265
Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level.
A conventional Volkswagen transporter, a Renault 5, a Pacer, and a U. S. Postal Service general DJ-5 delivery van were treated to an electric vehicle test procedure in order to allow direct comparison of conventional and electric vehicles. Performance test results for the four vehicles are presented.
Fuel consumption, range, and emissions data were obtained while operating a hydrogen-fueled postal delivery vehicle over a defined Postal Service Driving Cycle and the 1975 Urban Driving Cycle. The vehicle's fuel consumption was 0.366 pounds of hydrogen per mile over the postal driving cycle and 0.22 pounds of hydrogen per mile over the urban driving cycle. These data correspond to 6.2 and 10.6 mpg equivalent gasoline mileage for the two driving cycles, respectively. The vehicle's range was 24.2 miles while being operated on the postal driving cycle. Vehicle emissions were measured over the urban driving cycle. HC and CO emissions were quite low, as would be expected. The oxides of nitrogen were found to be 4.86 gm/mi, a value which is well above the current Federal and California standards. Vehicle limitations discussed include excessive engine flashbacks, inadequate acceleration capability the engine air/fuel ratio, the water injection systems, and the cab temperature. Other concerns are safety considerations, iron-titanium hydride observed in the fuel system, evidence of water in the engine rocker cover, and the vehicle maintenance required during the evaluation.