Multivariable Repetitive-predictive Controllers using Frequency Decomposition

Repetitive control is a methodology for the tracking of a periodic reference signal. This paper develops a new approach to repetitive control systems design using receding horizon control with frequency decomposition of the reference signal. Moreover, design and implementation issues for this form of repetitive predictive control are investigated from the perspectives of controller complexity and the effects of measurement noise. The analysis is supported by a simulation study on a multi-input multi-output robot arm where the model has been constructed from measured frequency response data, and experimental results from application to an industrial AC motor

Modeling gap seeking behaviors for agent-based crowd simulation

Research on agent-based crowd simulation has gained tremendous momentum in recent years due to the increase of computing power. One key issue in this research area is to develop various behavioral models to capture the microscopic behaviors of individuals (i.e., agents) in a crowd. In this paper, we propose a novel behavior model for modeling the gap seeking behavior which can be frequently observed in real world scenarios where an individual in a crowd proactively seek for gaps in the crowd flow so as to minimize potential collision with other people. We propose a two-level modeling framework and introduce a gap seeking behavior model as a proactive conflict minimization maneuver at global navigation level. The model is integrated with the reactive collision avoidance model at local steering level. We evaluate our model by simulating a real world scenario. The results show that our model can generate more realistic crowd behaviors compared to the classical social-force model in the given scenario

How Catastrophic Innovation Failure Affects Organizational and Industry Legitimacy: The 2014 Virgin Galactic Test Flight Crash

We examine how catastrophic innovation failure affects organizational and industry legitimacy in nascent sectors by analyzing the interactions between Virgin Galactic and stakeholders in the space community in the aftermath of the firm’s 2014 test flight crash. Following catastrophic innovation failure, we find that industry participants use their interpretations of the failure to either uphold or challenge the legitimacy of the firm while maintaining the legitimacy of the industry. These dynamics yield two interesting effects. First, we show that, in upholding the legitimacy of the industry, different industry participants rhetorically redraw the boundaries of the industry to selectively include players they consider legitimate and exclude those they view as illegitimate: detracting stakeholders constrain the boundaries of the industry by excluding the firm or excluding the firm and its segment, whereas the firm and supporting stakeholders amplify the boundaries of the industry by including firms in adjacent high-legitimacy sectors. Second, we show that, in assessing organizational legitimacy, the firm and its stakeholders differ in the way they approach distinctiveness between the identities of the industry and the firm. Detracting stakeholders differentiate the firm from the rest of the industry and isolate it, whereas the firm and supporting stakeholders reidentify the firm with the industry, embedding the firm within it. Overall, our findings illuminate the effects that catastrophic innovation failure has over high-order dynamics that affect the evolution of nascent industries

How Firms Frame Catastrophic Innovation Failure

We examine how catastrophic innovation failure affects organizational and industry legitimacy in nascent sectors by analyzing the interactions between Virgin Galactic and stakeholders in the space community in the aftermath of the firm’s 2014 test flight crash. Following catastrophic innovation failure, we find that industry participants use their interpretations of the failure to either uphold or challenge the legitimacy of the firm, while maintaining the legitimacy of the industry. These dynamics yield two interesting effects. First, we show that, in upholding the legitimacy of the industry, different industry participants rhetorically re-draw the boundaries of the industry to selectively include players they consider ‘legitimate’ and exclude those they view as ‘illegitimate:’ detracting stakeholders constrain the boundaries of the industry by excluding the firm or excluding the firm and its segment, while the firm and supporting stakeholders amplify the boundaries of the industry by including firms in adjacent high-legitimacy sectors. Second, we show that, in assessing organizational legitimacy, the firm and its stakeholders differ in the way they approach distinctiveness between the identities of the industry and the firm. Detracting stakeholders differentiate the firm from the rest of the industry and isolate it, while the firm and supporting stakeholders re-identify the firm with the industry, embedding the firm within it. Overall, our findings illuminate the effects that catastrophic innovation failure has over high-order dynamics that affect the evolution of nascent industries

How Catastrophic Innovation Failure Affects Organizational and Industry Legitimacy: The 2014 Virgin Galactic Test Flight Crash

We examine how catastrophic innovation failure affects organizational and industry legitimacy in nascent sectors by analyzing the interactions between Virgin Galactic and stakeholders in the space community in the aftermath of the firm’s 2014 test flight crash. Following catastrophic innovation failure, we find that industry participants use their interpretations of the failure to either uphold or challenge the legitimacy of the firm while maintaining the legitimacy of the industry. These dynamics yield two interesting effects. First, we show that, in upholding the legitimacy of the industry, different industry participants rhetorically redraw the boundaries of the industry to selectively include players they consider legitimate and exclude those they view as illegitimate: detracting stakeholders constrain the boundaries of the industry by excluding the firm or excluding the firm and its segment, whereas the firm and supporting stakeholders amplify the boundaries of the industry by including firms in adjacent high-legitimacy sectors. Second, we show that, in assessing organizational legitimacy, the firm and its stakeholders differ in the way they approach distinctiveness between the identities of the industry and the firm. Detracting stakeholders differentiate the firm from the rest of the industry and isolate it, whereas the firm and supporting stakeholders reidentify the firm with the industry, embedding the firm within it. Overall, our findings illuminate the effects that catastrophic innovation failure has over high-order dynamics that affect the evolution of nascent industries

Determination of Intrinsic Ferroelectric Polarization in Orthorhombic Manganites with E-type Spin Order

By directly measuring electrical hysteresis loops using the Positive-Up Negative-Down (PUND) method, we accurately determined the remanent ferroelectric polarization Pr of orthorhombic RMnO3 (R = Ho, Tm, Yb, and Lu) compounds below their E-type spin ordering temperatures. We found that LuMnO3 has the largest Pr of 0.17 uC/cm^2 at 6 K in the series, indicating that its single-crystal form can produce a Pr of at least 0.6 \muuC/cm^2 at 0 K. Furthermore, at a fixed temperature, Pr decreases systematically with increasing rare earth ion radius from R = Lu to Ho, exhibiting a strong correlation with the variations in the in-plane Mn-O-Mn bond angle and Mn-O distances. Our experimental results suggest that the contribution of the Mn t2g orbitals dominates the ferroelectric polarization.Comment: 16 pages, 4 figure

The activation energy for GaAs/AlGaAs interdiffusion

Copyright 1997 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. This article appeared in Journal of Applied Physics 82, 4842 (1997) and may be found at

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

ProactiveCrowd: modeling proactive steering behaviours for agent-based crowd simulation

How to realistically model an agent's steering behavior is a critical issue in agent-based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behavior-based modeling framework is first introduced to model the process of how humans select and execute a proactive steering strategies in crowded situations and execute the corresponding behavior accordingly. We then propose behavior models for two inter-related proactive steering behaviors, namely gap seeking and following. These behaviors can be frequently observed in real-life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real-world data and comparing the performance of our model with that of another state-of-the-art crowd model. The results show that the performance of our model is better or at least comparable to the compared model in terms of the realism at both individual and crowd level