Real-time Information, Uncertainty and Quantum Feedback Control

Feedback is the core concept in cybernetics and its effective use has made great success in but not limited to the fields of engineering, biology, and computer science. When feedback is used to quantum systems, two major types of feedback control protocols including coherent feedback control (CFC) and measurement-based feedback control (MFC) have been developed. In this paper, we compare the two types of quantum feedback control protocols by focusing on the real-time information used in the feedback loop and the capability in dealing with parameter uncertainty. An equivalent relationship is established between quantum CFC and non-selective quantum MFC in the form of operator-sum representation. Using several examples of quantum feedback control, we show that quantum MFC can theoretically achieve better performance than quantum CFC in stabilizing a quantum state and dealing with Hamiltonian parameter uncertainty. The results enrich understanding of the relative advantages between quantum MFC and quantum CFC, and can provide useful information in choosing suitable feedback protocols for quantum systems.Comment: 24 page

Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation

Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human–robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.IMOCOe4.0 [EU H2020RIA-101007311]Spanish national funding [PCI2021-121925INTSENSO [MICINN-FEDER-PID2019- 109991GB-I00]INTARE (TED2021-131466B-I00) projects funded by MCIN/AEI/10.13039/501100011033EU NextGenerationEU/PRTR to ERThe SPIKEAGE [MICINN629PID2020-113422GAI00]DLROB [TED2021 131294B-I00]Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRT

Developing Condition-Based Triggers for Bridge Deck Maintenance and Rehabilitation Treatments

The bridges in the U.S. highway system suffer from deficiencies in both their structural condition and functionality. In an effort to improve the condition of bridges, highway agencies continually seek effective and efficient approaches to maintenance and rehabilitation (M&R) treatments for their bridges. However, one drawback to new approaches is that highway agencies have long relied on the subjective judgment of their engineers to determine the time or condition at which to implement the treatments as well as the types of treatments to be applied. The literature shows that previous researchers mainly focused on time-based M&R strategies, but there have been some efforts toward developing condition-based strategies, such as the Indiana Bridge Management System (IBMS). While IBMS and similar systems were laudable efforts, they also were developed on the basis of the judgment and experience of bridge management personnel and were not data-driven

An Analysis of the Determinants of Total Factor Productivity in China

In this study, I analyse total factor productivity (TFP) and its determinants in Chinese industrial firms. The results from the system-GMM estimation indicate the existence of increasing returns to scale and a positive impact on firms’ TFP arising from technological change. Moreover, the following factors were found to be determinants of higher TFP levels in most industries: lack of political affiliation, paid-in capital share owned by investors other than the State, Marshallian and Jacobian spillovers, age, marketing capabilities, internal liquidity and industrial competition. The results from the TFP growth decomposition indicate an annual average TFP growth of 9.68% across Chinese industrial firms during the period of 1998-2007. This was largely determined by the reallocation of resources across existing firms. From a policymaking perspective, measures targeting the previously mentioned determinants are likely to spur firms’ TFP and consequently drive national long-run economic growth

Astrophysical Accretion and Feedback: The Bayesian Linchpin of Theory and Observation

Despite being a major pillar of galaxy evolution, galactic feedback from stars and supermassive black holes (SMBHs) is subject to very little observational constraint. This is particularly true of the hot component, as viewed in X-rays. Yet, the hot component is directly linked to much of the energetic feedback released from these compact objects. X-ray observations suffer from several challenges that make placing this constraint a difficult task. In the face of considerable model uncertainty, these challenges underscore the need for novel X-ray data analysis techniques. In this dissertation, I seek to lend a unique perspective to X-ray data analysis and initiate the steps towards unravelling the hot component of galactic feedback. This is done through spatio-spectral fitting with Markov Chain Monte Carlo (MCMC). First, I fit 2D simulations of SMBH accretion to three separate bands of Chandra imaging data of Sgr A*, the SMBH at our galactic center. In this study I place the first observational constraint on the angular momentum of accreting gas and self-consistently deconvolve residual point-like emission from the spatially extended accretion flow. I extend this analysis in Appendix 2 by re-examining the spectral energy distribution of Sgr A* from radio to X-ray. I find that a 1D accretion flow model cannot be reconciled with the more detailed X-ray modelling results. I further speculate on the origin of very steep synchrotron emission, suggesting that the residual point-like emission is accelerated by magnetic turbulence. Second, I describe the methodology for extracting spatial information from the RGS grating spectrometer onboard the XMM-Newton satellite. I demonstrate this method using 32 observations of M31 by fitting the OVIII Ly-alpha and OVII K-alpha transitions. I show that the observed spectral peculiarities are much more likely the result of resonance scattering, rather than SMBH feedback effects seen through plasma overionization. A semiparametric extention of that work is also provided in an appendix. Finally, I conclude with a discussion of the usefulness of spatio-spectral analysis and highlight the promising research toward understanding galactic feedback that can be done as an extention to the work herein

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering