The Oak Ridge National Laboratory and the New Technology Demonstration Program

The Oak Ridge National Laboratory is one of four National Labs implementing the Federal Energy Management Program`s New Technology Demonstration Program for the Department of Energy. The Oak Ridge National Laboratory has an extensive history of working on energy-related projects in both the public and private domain. Work on this program is intended to bring the strength of the Oak Ridge National Laboratory technology development abilities and unique facilities to bear on the technical challenges associated with evaluating energy efficient technologies. This paper describes some energy-related experiences at the Oak Ridge National Laboratory and the New Technology Demonstration Program at the Lab. The five technologies that the Lab is supporting in this Program are introduced. One of the technologies being evaluated, a retrofit system for rooftop units, is described in detail

Non-Linear Interference Mitigation Techniques for Broadband Multimedia Satellite Systems

This contribution explores the use of interference mitigation techniques applied to broadband satellite systems with co-channel interference. In particular, our focus is on non-linear precoding techniques, borrowing ideas from the theory of broadcast MIMO channels. A number of schemes are compared, including several implementations of Tomlinson-Harashima precoding and their linear precoding counterparts. Simulations on realistic scenarios show potential improvements of non-linear precoding with respect to linear interference mitigation and classical countermeasures based on frequency division among beams. Also, we identify several practical issues related to the implementation of Tomlinson-Harashima Precoding in satellite communication systems

The technical viability of alternative blowing agents in polyisocyanurate roof insulation

This paper presents a progress report on field thermal performance measurements on a set of private industry-produced, experimental polyisocyanurate laminate board stock foams blown with CFC-11, HCFC-123, HCFC-141b, 50/50, and 65/35 blends of HCFC-123/HCFC-141b. These boards have been observed for almost 300 days of roof field exposure in East Tennessee. The field data are used to derive an empirical model which can be used to predict effective diffusion coefficients for the air components into the foam cells. These diffusion coefficients are compared with those developed from steady state laboratory measurements of thin sliced samples from the same batch of experimental boards. The relative performance of test specimens of HCFC-141b under a black and under a white membrane are reported. The aging of the HCFC-141b blown foam under the white membrane occurred more slowly during cold weather, but accelerated after the winter season, resulting in no significant resistivity difference after 280 days of exposure from September 1989 until May 1990. The field data analysis suggests that the percent increase in k over that of the foam blown with CFC-11 is, after one year of aging, 5.5% for HCFC-123 and 11.7% for HCFC-141b. This leads to the same ordering as derived from the laboratory thin-slicing analysis report in Part 3 of this session. Additional plans are described for further thermal and mechanical property measurements to be conducted on two ORNL roof field testers. After the first year of this three-year study, there has been no indication that thermal performance differences are serious enough to suggest that any or all of the HCFC alternate blowing agents would not be technically viable in polyisocyanurate roof insulations. 5 refs., 19 figs

Deep and Modular Neural Networks

In this chapter, we focus on two important areas in neural computation, i.e., deep and modular neural networks, given the fact that both deep and modular neural networks have been among the most powerful machine learning and pattern recognition techniques for complex AI problem solving. We begin by providing a general overview of deep and modular neural networks to describe the general motivation behind such neural architectures and fundamental requirements imposed by complex AI problems. Next, we describe background and motivation, methodologies, major building blocks, and the state-of-the-art hybrid learning strategy in context of deep neural architectures. Then, we describe background and motivation, taxonomy and learning algorithms pertaining to various yet typical modular neural networks in a wide context. Furthermore, we also examine relevant issues and discuss open problems in deep and modular neural network research areas.