Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control ...(MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and may not generalize beyond the attractor where models are trained. These factors limit their use for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics. In this work, we extend the recent sparse identification of nonlinear dynamics (SINDY) modelling procedure to include the effects of actuation and demonstrate the ability of these models to enhance the performance of MPC, based on limited, noisy data. SINDY models are parsimonious, identifying the fewest terms in the model needed to explain the data, making them interpretable and generalizable. We show that the resulting SINDY-MPC framework has higher performance, requires significantly less data, and is more computationally efficient and robust to noise than NN models, making it viable for online training and execution in response to rapid system changes. SINDY-MPC also shows improved performance over linear data-driven models, although linear models may provide a stopgap until enough data is available for SINDY. SINDY-MPC is demonstrated on a variety of dynamical systems with different challenges, including the chaotic Lorenz system, a simple model for flight control of an F8 aircraft, and an HIV model incorporating drug treatment.
•This paper proposes a framework to jointly estimate parameters of nonlinear finite element (FE) models of civil structures and input excitations using output-only response data.•The unscented Kalman ...filter is used to solve the nonlinear estimation problem; therefore calculation of derivatives of structural responses is not required.•Uncertainties in the estimated model parameters and input excitations are also quantified.•The use of heterogeneous sensor arrays, including acceleration, displacement, and strain measurements, is investigated.•The nonlinear FE model updated with the estimated model parameters and subjected to the estimated input excitation(s) can be used to identify damage in the structure from the global to the local level.
A methodology is proposed to update mechanics-based nonlinear finite element (FE) models of civil structures subjected to unknown input excitation. The approach allows to jointly estimate unknown time-invariant model parameters of a nonlinear FE model of the structure and the unknown time histories of input excitations using spatially-sparse output response measurements recorded during an earthquake event. The unscented Kalman filter, which circumvents the computation of FE response sensitivities with respect to the unknown model parameters and unknown input excitations by using a deterministic sampling approach, is employed as the estimation tool. The use of measurement data obtained from arrays of heterogeneous sensors, including accelerometers, displacement sensors, and strain gauges is investigated. Based on the estimated FE model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess damage in the structure. Numerically simulated response data of a three-dimensional 4-story 2-by-1 bay steel frame structure with six unknown model parameters subjected to unknown bi-directional horizontal seismic excitation, and a three-dimensional 5-story 2-by-1 bay reinforced concrete frame structure with nine unknown model parameters subjected to unknown bi-directional horizontal seismic excitation are used to illustrate and validate the proposed methodology. The results of the validation studies show the excellent performance and robustness of the proposed algorithm to jointly estimate unknown FE model parameters and unknown input excitations.
The present paper aims at testing a model to predict personal involvement with a social object which was inspired by the social psychological triangle proposed by Moscovici. The triangle bridges ...three essential aspects of social psychology: the individual, the Other and a social object. It was operationalized as an empirical model to explain personal involvement with a social topic from two predictors: perceived collective involvement of group members with the same topic and group identification. The sample was formed by 805 Brazilian undergraduates. The participants completed scales that measured their identification with university students, their perception of students’ involvement with two social objects, university course or job, and their own personal involvement with those topics. Regression analyses supported the hypothesis that group identification, perceived collective involvement and their interaction maintained positive relations with personal involvement. Discussion focuses on the relativity of results to specific objects, the complexity of determinant factors of personal involvement and the pertinence of the triangular look to characterize social psychological research.
This study aims to investigate the effects of customers' perception of corporate social responsibility (CSR) on their CSR participation intention via customer–company identification (C–C ...identification). The authors also examine how CSR credibility strengthens the customers' CSR perception–C–C identification relationship and the indirect relationship between CSR perception and CSR participation intention through C–C identification. We conducted a survey of 567 South Korean bank customers and performed structural equation modeling to test our hypotheses. C–C identification partially mediated the relationship between customers' CSR perception and CSR participation intention. The positive association between customers' CSR perception and C–C identification was more pronounced when CSR credibility was higher than when it was lower. CSR credibility further moderated the indirect effect of customers' CSR perception and CSR participation intention through C‐C identification. This study deepens CSR research by showing how a cognitive CSR perception leads to a behavioral CSR participation based on a research model.
With coverage on all the marine mammals of the world, authors Jefferson, Webber, and Pitman have created a user-friendly guide to identify marine mammals alive in nature (at sea or on the beach), ...dead specimens "in hand", and also to identify marine mammals based on features of the skull. This handy guide provides marine biologists and interested lay people with detailed descriptions of diagnostic features, illustrations of external appearance, beautiful photographs, dichotomous keys, and more. Full color illustrations and vivid photographs of every living marine mammal species are incorporated, as well as comprehendible maps showing a range of information. For readers who desire further consultation, authors have included a list of literature references at the end of each species account. For an enhanced understanding of habitation, this guide also includes recognizable geographic forms described separately with colorful paintings and photographs. All of these essential tools provided make Marine Mammals of the World the most detailed and authoritative guide available!* Contains superb photographs of every species of marine mammal for accurate identification * Authors' collective experience adds up to 80 years, and have seen nearly all of the species and distinctive geographic forms described in the guide * Provides the most detailed and anatomically accurate illustrations currently available * Special emphasis is placed on the identification of species in "problem groups, " such as the beaked whales, long-beaked oceanic dolphin, and southern fur seals * Includes a detailed list of sources for more information at the back of the book.
•This study is the first to propose end-to-end pipelines for comprehensive modal identification of a bridge using moving sensors within vehicles.•Both proposed methods are successful to remove ...vehicle suspension effects and roughness-induced vibrations from the collected data within the vehicle cabin in order to extract bridge dynamic response.•The study indicates that the FRF-based method is able to identify the bridge with high accuracy as well as an acceptable estimation of the roughness profile.•The EEMD-based method does not need any a priori information of the vehicle, leading a practically more flexible platform.•In a numerical 2D case study, first four modes are fully identified using the proposed pipelines. MAC values are all above 0.94. Natural frequencies and damping ratios are also estimated accurately for the majority of cases.
Vehicles commuting over bridge structures respond dynamically to the bridge’s vibrations. An acceleration signal collected within a moving vehicle contains a trace of the bridge’s structural response, but also includes other sources such as the vehicle suspension system and surface roughness-induced vibrations. This paper introduces two general methods for the bridge system identification using data exclusively collected by a network of moving vehicles. The contributions of the vehicle suspension system are removed by deconvolving the vehicle response in frequency domain. The first approach utilizes the vehicle transfer function, and the second uses ensemble empirical modal decomposition (EEMD). Next, roughness-induced vibrations are extracted through a novel application of second-order blind identification (SOBI) method. After these two processes, the resulting signal is equivalent to the readings of mobile sensors that scan the bridge’s dynamic response. Structural modal identification using mobile sensor data has been recently made possible with the extended structural modal identification using expectation maximization (STRIDEX) algorithm. The processed mobile sensor data is analyzed using STRIDEX to identify the modal properties of the bridge. The performance of the methods are validated on numerical case studies of a long single-span bridge with a network of moving vehicles collecting data while in motion. The analyses consider three road surface roughness patterns. Results show that for long-span bridges with medium- to high-ongoing traffic volume, the proposed algorithms are successful in extracting pure bridge vibrations, and produce accurate and comprehensive modal properties of the bridge. The study shows that the proposed transfer function method can efficiently deconvolve the linear dynamics of a moving vehicle. EEMD method is able to extract vehicle dynamic response without a priori information about the vehicle. In addition, proposed identification methods provide secondary information about the roughness pattern and the vehicle. This study is the first proposed methodology for complete bridge modal identification, including operational natural frequencies, mode shapes and damping ratios using moving vehicles sensor data.
This study proposes two identification cuing factors (i. e., CSR associations and CSR participation) to understand how corporate social responsibility (CSR) relates to employees' identification with ...their firm.The results reveal that a firm's CSR initiatives increase employee-company identification (E-C identification).E-C identification, in turn, influences employees' commitment to their company. However, CSR associations do not directly influence employees' identification with a firm, but rather influence their identification through perceived external prestige (PEP). Compared to CSR associations, CSR participation has a direct influence on E-C identification. On the basis of these findings, it is argued that CSR performance can be an effective way for companies to maintain a positive relationship with their employees.
This research addresses: (1) the salience of employees’ social (organizational, sub-organizational, group, micro-group), interpersonal, and personal identifications and their dimensions (cognitive ...and affective); (2) and the relationship and structure of the identifications of employees in different areas of professional activity. The study was conducted on independent samples of employees in the socio-economic sphere (241 participants), in the law enforcement agency (265), and in higher education (172). To assess the respective identification foci and dimensions, the study employed four questionnaires. The personal identification was the weakest and the micro-group identification was the strongest for both dimensions in all samples. The affective dimension prevails over the cognitive in all identifications, except for interpersonal. Social identifications were significantly positively correlated to each other in all samples whereas personal identification was significantly negatively correlated with all social identifications (on the affective dimension) in two samples. The results expand our understanding of the identifications of employees in organizations.
In order to obtain the information of the vehicle tags in adverse traffic conditions, we proposed a novel reservation framework named reservation to cancel idle-dynamic frame slotted ALOHA ...(RTCI-DFSA) algorithm. Firstly, the framework employed reservation mechanism to remove idle slot, and thus improve the system identification efficiency. Secondly, the vehicle information was identified by the tag serialization polling identification method. The experimental results showed that the proposed RTCI-DFSA algorithm performed better than the traditional frame slotted ALOHA (FSA) and dynamic frame slotted ALOHA (DFSA) algorithms. More specifically, the tag loss rate of the proposed framework is significantly lower than the frame length fixed and conventional dynamic vehicle identification algorithms. In addition, the experimental results demonstrated that the throughput rate of the proposed algorithm increased from 0.368 to 0.6. Besides, the identification efficiency and applicability of the proposed framework were both higher than other tag identification algorithms.
This article offers a new perspective on customer–company identification (CCI) by focusing on CCI’s underlying self-motives: self-uncertainty and self-enhancement. More precisely, an ...operationalization is proposed in which cognitive (CCI
Cog
) and affective (CCI
Aff
) dimensions of CCI are driven by different self-motives: CCI
Cog
by self-uncertainty and CCI
Aff
by self-enhancement. Focusing on these self-motives reveals that CCI
Cog
and CCI
Aff
affect some customer attitudes and behaviors in opposite ways but affect other attitudes and behaviors similarly. A cross-sectional survey that examines outcomes of CCI
Cog
and CCI
Aff
supports the proposed conceptualization of CCI and suggests the dimensions differ in how each impacts customer–company relationships. Furthermore, the study suggests that combining the dimensions together in higher order constructs or examining only one dimension can lead to misleading conclusions.
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