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At least 109 records · Page 6

Investigating the Impact of Connected Vehicle Market Share on the Performance of Reinforcement-Learning Based Traffic Signal Control

We aim to understand and explore the performance of reinforcement learning based signal control algorithms in a mixed environment with less than 100% market share of connected and automated vehicles (CAVs). Within a simulation environment, we have considered partial connectivity—less than 100% market share of CAVs—in the network and investigated the impact on the performance of the signal control algorithm. Two test networks including a four-intersection arterial in Lankershim Boulevard, CA, and a portion of downtown Springfield, IL, with 20 intersections. The first network is calibrated in the micro-simulator PTV Vissim with the US DOT provided NGSIM datasets. The results provide insights regarding the impact of the connectivity and sensing technologies on the practical implementation of traffic signal control algorithms that leverage the data sharing capability of a connected environment. For scenarios with 40% or more market share of CAVs, we observed improvement in the performance metrics—travel time, queue time, and energy consumption—with the increase in market share. Results from our experiments do not indicate any clear trend when the networks have low (less than 40%) market share of CAVs. The higher standard deviations as obtained from the statistical analyses of the performance metrics at low market share may indicate the instability of the RL controller arising from the partial (if not zero) observability of the traffic states. Further, we have conducted simplified scenario analyses to explore the impact of the market share of battery electric vehicles (BEVs) on energy consumption due to the regenerative braking feature. Our results and findings will be the foundation for the future reinforcement learning based control algorithm development that accounts for partial connectivity—less than 100% CAV market share, and the presence of BEVs in a network of connected and automated signalized intersections.

33 ADVANCED PROPULSION SYSTEMS↗

Effects of Pore Connectivity on the Sorption of Fluids in Nanoporous Material: Ethane and CO2 Sorption in Silicalite

Adsorption of fluids in nanoporous materials is important for several applications including gas storage and catalysis. The pore network in natural, as well as engineered, materials can exhibit different degrees of connectivity between pores. While this might have important implications for the sorption of fluids, the effects of pore connectivity are seldom addressed in the studies of fluid sorption. We have carried out Monte Carlo simulations of the sorption of ethane and CO2 in silicalite, a nanoporous material characterized by sub-nanometer pores of different geometries (straight and zigzag channel like pores), with varied degrees of pore connectivity. The variation in pore connectivity is achieved by selectively blocking some pores by loading them with methane molecules that are treated as a part of the rigid nanoporous matrix in the simulations. Normalized to the pore space available for adsorption, the magnitude of sorption increases with a decrease in pore connectivity. The increased adsorption in the systems where pore connections are removed by blocking them is because of additional, albeit weaker, adsorption sites provided by the blocker molecules. By selectively blocking all straight or zigzag channels, we find differences in the absorption behavior of guest molecules in these channels.

Gautam, Siddharth (ORCID:0000000314435382)↗

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

Introduction to a system for implementing neural net connections on SIMD architectures

Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized communication. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.

Tomboulian, Sherryl↗

Characterization of damped structural connections for multi-component systems

The inability to model connections adequately has historically limited the ability to predict overall system dynamic response. Connections between structural components are often mechanically complex and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic predictions. This study explores combining Component Mode Synthesis methods for coupling structural components with Parameter Identification procedures for improving the analytical modeling of the connections. Improvements in the connection stiffness and damping properties are computed in terms of physical parameters so the physical characteristics of the connections can be better understood, in addition to providing improved input for the system model.

Lawrence, Charles↗

Introduction to a system for implementing neural net connections on SIMD architectures

Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized elements. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.

Tomboulian, Sherryl↗

Ground-state coding in partially connected neural networks

Patterns over (-1,0,1) define, by their outer products, partially connected neural networks, consisting of internally strongly connected, externally weakly connected subnetworks. The connectivity patterns may have highly organized structures, such as lattices and fractal trees or nests. Subpatterns over (-1,1) define the subcodes stored in the subnetwork, that agree in their common bits. It is first shown that the code words are locally stable stares of the network, provided that each of the subcodes consists of mutually orthogonal words or of, at most, two words. Then it is shown that if each of the subcodes consists of two orthogonal words, the code words are the unique ground states (absolute minima) of the Hamiltonian associated with the network. The regions of attraction associated with the code words are shown to grow with the number of subnetworks sharing each of the neurons. Depending on the particular network architecture, the code sizes of partially connected networks can be vastly greater than those of fully connected ones and their error correction capabilities can be significantly greater than those of the disconnected subnetworks. The codes associated with lattice-structured and hierarchical networks are discussed in some detail.

Baram, Yoram↗

Characterization of damped structural connections for multi-component systems

The inability to model connections adequately has historically limited the ability to predict overall system dynamic response. Connections between structural components are often mechanically complex and difficult to accurataely model analytically. Improved analytical models for connections are needed to improve system dynamic predictions. This study explores combining Component Mode Synthesis methods for coupling structural components with Parameter Identification procedures for improving the analytical modeling of the connections. Improvements in the connection stiffness and damping properties are computed in terms of physical parameters so the physical characteristics of the connections can be better understood, in addition to providing improved input for the system model.

Lawrence, Charles↗

Connectivity strategies for higher-order neural networks applied to pattern recognition

Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

Spirkovska, Lilly↗

Identification of the connections in biologically inspired neural networks

We developed an identification method to find the strength of the connections between neurons from their behavior in small biologically-inspired artificial neural networks. That is, given the network external inputs and the temporal firing pattern of the neurons, we can calculate a solution for the strengths of the connections between neurons and the initial neuron activations if a solution exists. The method determines directly if there is a solution to a particular neural network problem. No training of the network is required. It should be noted that this is a first pass at the solution of a difficult problem. The neuron and network models chosen are related to biology but do not contain all of its complexities, some of which we hope to add to the model in future work. A variety of new results have been obtained. First, the method has been tailored to produce connection weight matrix solutions for networks with important features of biological neural (bioneural) networks. Second, a computationally efficient method of finding a robust central solution has been developed. This later method also enables us to find the most consistent solution in the presence of noisy data. Prospects of applying our method to identify bioneural network connections are exciting because such connections are almost impossible to measure in the laboratory. Knowledge of such connections would facilitate an understanding of bioneural networks and would allow the construction of the electronic counterparts of bioneural networks on very large scale integrated (VLSI) circuits.

Demuth, H.↗

Quick connect fastener

A quick connect fastener and method of use is presented wherein the quick connect fastener is suitable for replacing available bolts and screws, the quick connect fastener being capable of installation by simply pushing a threaded portion of the connector into a member receptacle hole, the inventive apparatus being comprised of an externally threaded fastener having a threaded portion slidably mounted upon a stud or bolt shaft, wherein the externally threaded fastener portion is expandable by a preloaded spring member. The fastener, upon contact with the member receptacle hole, has the capacity of presenting cylindrical threads of a reduced diameter for insertion purposes and once inserted into the receiving threads of the receptacle member hole, are expandable for engagement of the receptacle hole threads forming a quick connect of the fastener and the member to be fastened, the quick connect fastener can be further secured by rotation after insertion, even to the point of locking engagement, the quick connect fastener being disengagable only by reverse rotation of the mated thread engagement.

Weddendorf, Bruce↗

A Comparison of Structurally Connected and Multiple Spacecraft Interferometers

Structurally connected and multiple spacecraft interferometers are compared in an attempt to establish the maximum baseline (referred to as the "cross-over baseline") for which it is preferable to operate a single-structure interferometer in space rather than an interferometer composed of numerous, smaller spacecraft. This comparison is made using the total launched mass of each configuration as the comparison metric. A framework of study within which structurally connected and multiple spacecraft interferometers can be compared is presented in block diagram form. This methodology is then applied to twenty-two different combinations of trade space parameters to investigate the effects of different orbits, orientations, truss materials, propellants, attitude control actuators, onboard disturbance sources, and performance requirements on the cross-over baseline. Rotating interferometers and the potential advantages of adding active structural control to the connected truss of the structurally connected interferometer are also examined. The minimum mass design of the structurally connected interferometer that meets all performance-requirements and satisfies all imposed constraints is determined as a function of baseline. This minimum mass design is then compared to the design of the multiple spacecraft interferometer. It is discovered that the design of the minimum mass structurally connected interferometer that meets all performance requirements and constraints in solar orbit is limited by the minimum allowable aspect ratio, areal density, and gage of the struts. In the formulation of the problem used in this study, there is no advantage to adding active structural control to the truss for interferometers in solar orbit. The cross-over baseline for missions of practical duration (ranging from one week to thirty years) in solar orbit is approximately 400 m for non-rotating interferometers and 650 m for rotating interferometers.

Surka, Derek M.↗

An Evaluation of Collagen Metabolism in Non Human Primates Associated with the Bion 11 Space Program-Markers of Urinary Collagen Turnover and Muscle Connective Tissue

Patients exhibiting changes in connective tissue and bone metabolism also show changes in urinary by-products of tissue metabolism. Furthermore, the changes in urinary connective tissue and bone metabolites precede alterations at the tissue macromolecular level. Astronauts and Cosmonauts have also shown suggestive increases in urinary by-products of mineralized and non-mineralized tissue degradation. Thus, the idea of assessing connective tissue and bone response in spaceflight monkeys by measurement of biomarkers in urine has merit. Other investigations of bone and connective histology, cytology and chemistry in the Bion 11 monkeys will allow for further validation of the relationship of urinary biomarkers and tissue response. In future flights the non-invasive procedure of urinary analysis may be useful in early detection of changes in these tissues. Purpose: The purpose of this grant investigation was to evaluate mineralized and non-mineralized connective tissue responses of non-human primates to microgravity by the non-invasive analysis of urinary biomarkers. Secondly, we also wanted to assess muscle connective tissue adaptive changes in three weight-bearing skeletal muscles: the soleus, medial gastrocnemius and tibialis anterior by obtaining pre-flight and post-flight small biopsy specimens in collaboration with Dr. V. Reggie Edgerton's laboratory at the University of California at Los Angeles.

Vailas, Arthur C.↗

An Evaluation of Collagen Metabolism in Non Human Primates Associated with the Bion 11 Space Program-Markers of Urinary Collagen Turnover and Muscle Connective Tissue

Patients exhibiting changes in connective tissue and bone metabolism also show changes in urinary by-products of tissue metabolism. Furthermore, the changes in urinary connective tissue and bone metabolites precede alterations at the tissue macromolecular level. Astronauts and Cosmonauts have also shown suggestive increases in urinary by-products of mineralized and non-mineralized tissue degradation. Thus, the idea of assessing connective tissue and bone response in spaceflight monkeys by measurement of biomarkers in urine has merit. Other investigations of bone and connective histology, cytology and chemistry in the Bion 11 monkeys will allow for further validation of the relationship of urinary biomarkers and tissue response. In future flights the non-invasive procedure of urinary analysis may be useful in early detection of changes in these tissues. The purpose of this grant investigation was to evaluate mineralized and non-mineralized connective tissue responses of non-human primates to microgravity by the non-invasive analysis of urinary biomarkers. Secondly, we also wanted to assess muscle connective tissue adaptive changes in three weight-bearing skeletal muscles: the soleus, media] gastrocnemius and tibialis anterior by obtaining pre-flight and post-flight small biopsy specimens in collaboration with Dr. V. Reggie Edgerton's laboratory at the University of California at Los Angeles.

Vailas, Arthur C.↗

Connect Global Positioning System RF Module

The CoNNeCT Global Positioning System RF Module (GPSM) slice is part of the JPL CoNNeCT Software Defined Radio (SDR). CoNNeCT is the Communications, Navigation, and Net working reconfigurable Testbed project that is part of NASA's Space Communication and Nav igation (SCaN) Program. The CoNNeCT project is an experimental dem onstration that will lead to the advancement of SDRs and provide a path for new space communication and navigation systems for future NASA exploration missions. The JPL CoNNeCT SDR will be flying on the International Space Station (ISS) in 2012 in support of the SCaN CoNNeCT program. The GPSM is a radio-frequency sampler module (see Figure 1) that directly sub-harmonically samples the filtered GPS L-band signals at L1 (1575.42 MHz), L2 (1227.6 MHz), and L5 (1176.45 MHz). The JPL SDR receives GPS signals through a Dorne & Margolin antenna mounted onto a choke ring. The GPS signal is filtered against interference, amplified, split, and fed into three channels: L1, L2, and L5. In each of the L-band channels, there is a chain of bandpass filters and amplifiers, and the signal is fed through each of these channels to where the GPSM performs a one-bit analog-to-digital conversion (see Figure 2). The GPSM uses a sub-harmonic, single-bit L1, L2, and L5 sampler that samples at a clock rate of 38.656 MHz. The new capability is the down-conversion and sampling of the L5 signal when previous hardware did not provide this capability. The first GPS IIF Satellite was launched in 2010, providing the new L5 signal. With the JPL SDR flying on the ISS, it will be possible to demonstrate navigation solutions with 10-meter 3-D accuracy at 10-second intervals using a field-program mable gate array (FPGA)-based feedback loop running at 50 Hz. The GPS data bits will be decoded and used in the SDR. The GPSM will also allow other waveforms that are installed in the SDR to demonstrate various GNSS tracking techniques.

Franklin, Garth W.↗

The Effects of Long Duration Bed Rest on Brain Functional Connectivity and Sensorimotor Functioning

Long duration spaceflight has been associated with detrimental alterations in human sensorimotor functioning. Prolonged exposure to a head-down tilt (HDT) position during long duration bed rest can resemble several effects of the microgravity environment such as reduced sensory inputs, body unloading and increased cephalic fluid distribution. The question of whether microgravity affects other central nervous system functions such as brain functional connectivity and its relationship with behavior is largely unknown, but of importance to the health and performance of astronauts both during and post-flight. In the present study, we investigate the effects of prolonged exposure to HDT bed rest on resting state brain functional connectivity and its association with behavioral changes in 17 male participants. To validate that our findings were not due to confounding factors such as time or task practice, we also acquired resting state functional magnetic resonance imaging (rs-fMRI) and behavioral measurements from 14 normative control participants at four time points. Bed rest participants remained in bed with their heads tilted down six degrees below their feet for 70 consecutive days. Rs-fMRI and behavioral data were obtained at seven time points averaging around: 12 and 8 days prior to bed rest; 7, 50, and 70 days during bed rest; and 8 and 12 days after bed rest. 70 days of HDT bed rest resulted in significant increases in functional connectivity during bed rest followed by a reversal of changes in the post bed rest recovery period between motor cortical and somatosensory areas of the brain. In contrast, decreases in connectivity were observed between temporoparietal regions. Furthermore, post-hoc correlation analyses revealed a significant relationship between motor-somatosensory network connectivity and standing balance performance changes; participants that exhibited the greatest increases in connectivity strength showed the least deterioration in postural equilibrium with HDT bed rest. This suggests that neuroplastic processes may facilitate adaptation to the HDT bed rest environment. The findings from this study provide novel insights into the neurobiology and future risk assessments of long-duration spaceflight.

Cassady, K.↗

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DC-connected Solar Plus Storage Modeling and Analysis for Behind-The-Meter Systems in the System Advisor Model: Preprint

A detailed model for PV plus DC-connected batteries was developed. This model was compared to an existing AC connected battery model in the System Advisor Model (SAM) tool using a hypothetical Honolulu residence with a PV plus storage system. The stand-alone PV system was shown to provide the most value; however due to the increased difficulty of PV systems being approved for grid interconnection in Hawaii, it is valuable to install an AC or DC connected battery to enable full PV self-consumption under a less restrictive interconnection option. In that case both battery systems enabled full PV self consumption at the residence, requiring no power to be exported to the grid. The DC-connected battery system captured energy from the PV array which would have also been clipped due to inverter limitations. For a PV system with DC/AC ratio of 1.23, the battery configurations produced net-present values within 1% of each other, but when the DC/AC ratio increased to 1.85, the DC-connected system increased the net-present value by 13%.

14 SOLAR ENERGY↗