GENERATIVE SOFTWARE ARCHITECTURE ORACLE

A system may assist the development-to-deployment workflow by presenting information about the architecture of a software system in real-time to facilitate understanding of the architecture. The system may analyze metadata collected from the software system to extract information about the code dependency and performance of at least a portion of the software system from end-to-end. In some examples, the system may predict the results of software testing based on the extracted information, which may help with identifying redundant testing that can be omitted from the development-to-deployment workflow

Force tracking control for motion synchronization in human-robot collaboration

In this paper, motion synchronization is investigated for human-robot collaboration such that the robot is able to “actively” follow its human partner. Force tracking is achieved with the proposed method under the impedance control framework, subject to uncertain human limb dynamics. Adaptive control is developed to deal with point-to-point movement, and learning control and neural networks (NN) control are developed to generate periodic and arbitrary continuous trajectories, respectively. Stability and tracking performance of the closed-loop system are discussed through rigorous analysis. The validity of the proposed method is verified through simulation and experiment studies

Value of local offshore renewable resource diversity for network hosting capacity

It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components

Kansas Speaks 2011 Statewide Public Opinion Survey

The Docking Institute of Public Affairs at Fort Hays State University conducted the 2011 Kansas Speaks survey from June 21 to September 2, 2011. A random sample of adult residents of Kansas age 18 and older was surveyed by telephone or mail questionnaire to assess their attitudes and opinions regarding various issues of interest to Kansas citizens

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation to be off-policy regardless of the fact that these off-policy extensions could either change the original objective or involve complicated optimization. We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our formulation capitalizes on two key insights: (1) the similarity between the Bellman equation and the stationary state-action distribution equation allows us to derive a novel temporal difference (TD) learning approach; and (2) the use of a deterministic policy simplifies the TD learning. Combined, these insights yield a practical algorithm, Deterministic and Discriminative Imitation (D2-Imitation), which operates by first partitioning samples into two replay buffers and then learning a deterministic policy via off-policy reinforcement learning. Our empirical results show that D2-Imitation is effective in achieving good sample efficiency, outperforming several off-policy extension approaches of adversarial imitation on many control tasks.Comment: AAAI 202

Chatter, process damping, and chip segmentation in turning: A signal processing approach

An increasing number of aerospace components are manufactured from titanium and nickel alloys that are difficult to machine due to their thermal and mechanical properties. This limits the metal removal rates that can be achieved from the production process. However, under these machining conditions the phenomenon of process damping can be exploited to help avoid self-excited vibrations known as regenerative chatter. This means that greater widths of cut can be taken so as to increase the metal removal rate, and hence offset the cutting speed restrictions that are imposed by the thermo-mechanical properties of the material. However, there is little or no consensus as to the underlying mechanisms that cause process damping. The present study investigates two process damping mechanisms that have previously been proposed in the machining literature: the tool flank/workpiece interference effect, and the short regenerative effect. A signal processing procedure is employed to identify flank/workpiece interference from experimental data. Meanwhile, the short regenerative model is solved using a new frequency domain approach that yields additional insight into its stabilising effect. However, analysis and signal processing of the experimentally obtained data reveals that neither of these models can fully explain the increases in stability that are observed in practice. Meanwhile, chip segmentation effects were observed in a number of measurements, and it is suggested that segmentation could play an important role in the process-damped chatter stability of these materials

Design, Synthesis, Characterizations, and Processing of a Novel c-Donor-nc-Bridge-cf-Acceptor Type Block Copolymer for Optoelecronic Applications

A novel c-D-nc-B-cf-A (or DBfA) type of block copolymer has been designed, synthesized, characterized, and preliminarily studied for optoectronic applications, where c-D is a conjugated donor type polyphenylenevinylene (PPV) block, nc-B is a non-conjugated bridge unit, and cf-A is a conjugated and fluorinated acceptor type PPV block. The frontier HOMO/LUMO orbital levels of D and fA conjugated blocks are -5.22/-3.06 and -6.10/-3.43 as determined from electrochemical and optical measurements. Photoluminescence emissions of D and fA are quenched in DBfA indicating a potential photo induced charge separation pathway between the donor and the acceptor blocks. Solid state thin film studies revealed more uniform and nano-scale phase separated morphologies in DBfA as compared to D/fA blend. A two orders of magnitude enhancement of photoelectric energy conversion efficiency was observed in a best solar cell fabricated from the DBfA block copolymer as compared to a best cell fabricated from the corresponding D/fA blend. Such significant photoelectric conversion enhancement could be attributed to the improvements of phase separated and bicontinously ordered nanostructure (BONS) morphology in DBfA as compared to D/fA