A Unified Filter for Simultaneous Input and State Estimation of Linear Discrete-time Stochastic Systems

In this paper, we present a unified optimal and exponentially stable filter for linear discrete-time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense, without making any assumptions on the direct feedthrough matrix. We also derive input and state observability/detectability conditions, and analyze their connection to the convergence and stability of the estimator. We discuss two variations of the filter and their optimality and stability properties, and show that filters in the literature, including the Kalman filter, are special cases of the filter derived in this paper. Finally, illustrative examples are given to demonstrate the performance of the unified unbiased minimum-variance filter.Comment: Preprint for Automatic

UNDERSTANDING THE ROLE OF COMMITMENTS IN EXPLAINING CROWDFUNDING INVESTING WILLINGNESS: ANTECEDENTS AND CONSEQUENCES

Crowdfunding is a new financing channel for small- and medium-sized enterprises and start-ups to raise funds for innovation projects online. Despite its rapid development, few empirical research has been performed to identify individuals’ motivations to continuously invest in crowdfunding. The high practical significance and lack of research indicate the importance of the present study. This study aims to apply Meyer & Allen’s three-component model of commitment to construct a research model, incorporating context-specific antecedents. The results of our survey of 186 actual funders of the crowdfunding platforms in China indicated that affective and calculative commitment are the main drivers of funders’ continuous investments in crowdfunding. Calculative commitment was proved to have a positive influence on affective commitment. Further, perceived self-worth and trust performed well as antecedents of both affective and calculative commitment, though trust played a negative role in the latter, which differed from the three other paths. And also, perceived critical mass was significantly associated with calculative commitment. The results of this research provided theoretical implications for future research and practical implications for the success of crowdfunding platforms

Non-uniform Multi-rate Estimator based Periodic Event-Triggered Control for resource saving

[EN] This paper proposes a systematic non-uniform multi-rate estimation and control framework for a periodic event-triggered system which is subject to external disturbance and sensor noise. When the disturbance dynamic model is available, and in order to efficiently estimate the state variable and disturbance from non-uniform slow-rate measurements, a time-varying Kalman filter is designed. When the disturbance dynamic model is not available, a disturbance observer is proposed as an alternative approach. Both the Kalman filter and the disturbance observer are proposed in a non-uniform multi-rate format. Such disturbance estimation enables faster controller updating in spite of slower measurement. Interlacing techniques are used in the control system to uniformly distribute the computational load at each fast sampling instance. Compared to the conventional time-triggered sampling paradigm, the control solution is able to reduce the resource utilization, while maintaining a satisfactory control performance. The proposed control solution will reduce the number of transmissions among devices, which enhances the energy and computational efficiency. Simulation results are provided to validate the effectiveness and benefits of the proposed control algorithms. (C) 2018 Elsevier Inc. All rights reserved.This research work has been developed as a result of a mobility stay funded by the Fulbright Visiting Scholar Program of the Fulbright Commission and the Spanish Ministry of Education under Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013 2016 . In addition, the work is funded by European Commission as part of Project H2020-SEC-2016-2017 - Topic: SEC-20-BES-2016 - Id: 740736 - C2 Advanced Multi-domain Environment and Live Observation Technologies (CAMELOT). Part WP5 supported by Tekever ASDS, Thales Research and Technology, Viasat Antenna Systems, Universitat Politècnica de València, Fundação da Faculdade de Ciências da Universidade de Lisboa, Ministério da Defensa Nacional - Marinha Portuguesa, Ministério da Administração Interna Guarda Nacional Republicana.Cuenca, Á.; Zheng, M.; Tomizuka, M.; Sanchez, S. (2018). Non-uniform Multi-rate Estimator based Periodic Event-Triggered Control for resource saving. Information Sciences. 459:86-102. https://doi.org/10.1016/J.INS.2018.05.038S8610245

Anytime computation algorithms for stochastically parametric approach-evasion differential games

We consider an approach-evasion differential game where the inputs of one of the players are upper bounded by a random variable. The game enjoys the order preserving property where a larger relaxation of the random variable induces a smaller value function. Two numerical computation algorithms are proposed to asymptotically recover the expected value function. The performance of the proposed algorithms is compared via a stochastically parametric homicidal chauffeur game. The algorithms are also applied to the scenario of merging lanes in urban transportation.National Science Foundation (U.S.) (Grant 1239182)United States. Air Force Office of Scientific Research (Grant FA8650-07-2-3744

One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls

It is well known that many open-released foundational diffusion models have difficulty in generating images that substantially depart from average brightness, despite such images being present in the training data. This is due to an inconsistency: while denoising starts from pure Gaussian noise during inference, the training noise schedule retains residual data even in the final timestep distribution, due to difficulties in numerical conditioning in mainstream formulation, leading to unintended bias during inference. To mitigate this issue, certain $\epsilon$-prediction models are combined with an ad-hoc offset-noise methodology. In parallel, some contemporary models have adopted zero-terminal SNR noise schedules together with $\mathbf{v}$-prediction, which necessitate major alterations to pre-trained models. However, such changes risk destabilizing a large multitude of community-driven applications anchored on these pre-trained models. In light of this, our investigation revisits the fundamental causes, leading to our proposal of an innovative and principled remedy, called One More Step (OMS). By integrating a compact network and incorporating an additional simple yet effective step during inference, OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters. Once trained, various pre-trained diffusion models with the same latent domain can share the same OMS module.Comment: Project Page: https://jabir-zheng.github.io/OneMoreStep/, Demo Page: https://huggingface.co/spaces/h1t/oms_sdxl_lc