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In this study, the authors present an overview of closed-loop subspace identification methods found in the recent literature. Since a significant number of algorithms has appeared over the last decade, the authors highlight some of the key algorithms that can be shown to have a common origin in autoregressive modelling. Many of the algorithms found in the literature are variants on the algorithms that are discussed here. In this study, the aim is to give a clear overview of some of the more successful methods presented throughout the last decade. Furthermore, the authors retrace these methods to a common origin and show how they differ. The methods are compared both on the basis of simulation examples and real data. Although the main focus in the literature has been on the identification of discrete-time models, identification of continuous-time models is also of practical interest. Hence, the authors also provide an overview of the continuous-time formulation of the identification framework.

This study examines the relative effectiveness of traditional advertising, impressions generated through firm-to-consumer (F2C) messages on Facebook, and the volume and valence of consumer-to-consumer (C2C) messages on Twitter and web forums for brand-building and customer acquisition efforts. The authors apply vector autoregressive modeling to a unique data set from a European telecom firm. This modeling approach allows them to consider the interrelations among traditional advertising, F2C impressions, and volume and valence of C2C social messages. The results show that traditional advertising is most effective for both brand building and customer acquisition. Impressions generated through F2C social messages complement traditional advertising efforts. Thus, thoroughly orchestrating traditional advertising and a firm's social media activities may improve a firm's performance with respect to building the brand and encouraging customer acquisition. Moreover, firms can stimulate the volume and valence of C2C messages through traditional advertising that in turn influences brand building and acquisition. These findings can help managers leverage the different types of messages more adequately.

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor’s current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura–Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude.

Aim This study uses a high-resolution simulation of the Last Glacial Maximum (LGM) climate to assess: (1) whether LGM climate still affects the geographical species richness patterns in the European tree flora and (2) the relative importance of modern and LGM climate as controls of tree species richness in Europe. Location The parts of Europe that were unglaciated during the LGM. Methods Atlas data on the distributions of 55 tree species were linked with data on modern and LGM climate and climatic heterogeneity in a geographical information system with a 60-km grid. Four measures of species richness were computed: total richness, and richness of the 18 most restricted species, 19 species of medium incidence (intermediate species) and 18 most widespread species. We used ordinary least-squares regression and spatial autoregressive modelling to test and estimate the richness-climate relationships. Results LGM climate constituted the best single set of explanatory variables for richness of restricted species, while modern climate and climatic heterogeneity was best for total and widespread species richness and richness of intermediate species, respectively. The autoregressive model with all climatic predictors was supported for all richness measures using an information-theoretic approach, albeit only weakly so for total species richness. Among the strongest relationships were increases in total and intermediate richness with climatic heterogeneity and in restricted richness with LGM growing-degree-days. Partial regression showed that climatic heterogeneity accounted for the largest unique variation fraction for intermediate richness, while LGM climate was particularly important for restricted richness. Main conclusions LGM climate appears to still affect geographical patterns of tree species richness in Europe, albeit the relative importance of modern and LGM climate depends on range size. Notably, LGM climate is a strong richness control for species with a restricted range, which appear to still be associated with their glacial refugia.

Tropospheric radio refractivity is a significant atmospheric phenomenon that affects the propagation of radio signals, and can impact the design and operation of wireless communication systems. This study focuses on the development of an autoregressive model of tropospheric radio refractivity in Nigeria using artificial neural networks (ANNs). The proposed model utilizes atmospheric variables—temperature, pressure, and humidity—as inputs and predicts refractivity values with high accuracy. Descriptive statistics and data visualization techniques were used to gain insights into the relationships between the atmospheric variables and computed radio refractivity. It could be deduced from the results obtained that the developed ANN model accurately predicts tropospheric radio refractivity, with satisfactory performance indicators that include standard error (SE), root mean square error (RMSE), and correlation coefficient (R). It also demonstrates the reliability and robustness of the developed model, which could play an important role in improving the preparation and implementation routines of wireless communication systems. The study also identifies areas for further study, such as data availability, model complexity, and interpretability. Lastly, this work has further validated the suitability of applying ANNs to tropospheric radio refractivity model optimization, as it provides insights into the potential of the non-linear autoregressive modeling (NARX-ANN) approach for improving wireless communication systems.

Electrocardiogram (ECG) is a most primitive and important test to analyse the status of the heart functioning. During this test, different types of noises and artefacts get involved in the captured electrical signal which affects the performance of overall diagnosis. In general, computer aided decision system (CADS) performs three operations viz . pre-processing, feature extraction and classification to reach a decision for analyzing an ECG signal. Among three waves of an ECG signal, QRS-complex is to be examined most critically to diagnose existence of a possible cardiovascular disease. But detection of exact locations of QRS complexes is still a challenging task as they are hidden by various noises and artefacts. Therefore, in this paper emerging tools such as wavelet transform (WT), adaptive autoregressive modelling (AARM) and vector machines (VMs) like support vector machine (SVM) and relevance vector machine (RVM) are proposed to be used for pre-processing, feature extraction and classification, respectively for utilizing distinct advantages of each. For instance, WT provides better time–frequency resolution, AARM possesses parameters that vary with time leading to the measurement of time-varying spectra and VMs models the non-linear data stably. Also, RVM has been proposed to be used for the first time here for ECG signal analysis as it needs much less kernel functions. SVM has been used for comparison purpose only. The performance of the proposed methodology is evaluated on the basis of widely used performance parameters such as sensitivity (Se), positive predictivity (Pp), accuracy (Acc) and detection rate (Dr). Highlight of the proposed methodology is that it yields consistently high values of all the widely used and critical performance parameters i.e. Se of 99.95%, Pp of 99.95%, Dr of 99.95%, and Acc of 99.93%. These results are highest amongst other techniques existing in the literature, indicating its usefulness for handling real-time heart related emergent cases.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment’s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental and operational conditions that such structures are subjected to along their lifespan. To address this challenge, this paper proposes a novel damage identification approach exploiting continuously extracted time series of autoregressive (AR) coefficients from strain data with moving train loads as highly sensitive damage features. Through a statistical pattern recognition algorithm involving data clustering and quality control charts, the proposed approach offers a set of sensor-level damage indicators with damage detection, quantification, and localization capabilities. The effectiveness of the developed approach is appraised through two case studies, involving a theoretical simply supported beam and a real-world in-operation railway bridge. The latter corresponds to the Mascarat Viaduct, a 20th century historical steel truss railway bridge that remains active in TRAM line 9 in the province of Alicante, Spain. A detailed 3D finite element model (FEM) of the viaduct was defined and experimentally validated. On this basis, an extensive synthetic dataset was constructed accounting for both environmental and operational conditions, as well as a variety of damage scenarios of increasing severity. Overall, the presented results and discussion evidence the superior performance of strain measurements over acceleration, offering great potential for unsupervised damage detection with full damage identification capabilities (detection, quantification, and localization).

Generating expressive and diverse human gestures from audio is crucial in fields like human–computer interaction, virtual reality, and animation. While existing methods have achieved remarkable performance, they often exhibit limitations due to constrained dataset diversity and the restricted amount of information derived from audio inputs. To address these challenges, we present VarGes, a novel variation-driven framework designed to enhance co-speech gesture generation by integrating visual stylistic cues while maintaining naturalness. Our approach begins with a variation-enhanced feature extraction module, which seamlessly incorporates style-reference video data into a 3D human pose estimation network to extract StyleCLIPS, thereby enriching the input with stylistic information. Subsequently, we employ a variation-compensation style encoder, a transformer-style encoder equipped with an additive attention mechanism pooling layer, to robustly encode diverse StyleCLIPS representations and effectively manage stylistic variations. Finally, a variation-driven gesture predictor module fuses MFCC audio features with StyleCLIPS encodings via cross-attention, injecting this fused data into a cross-conditional autoregressive model to modulate 3D human gesture generation based on audio input and stylistic clues. The efficacy of our approach is validated on benchmark datasets, on which it outperforms existing methods in terms of gesture diversity and naturalness. Our code and video results are publicly available at https://github.com/mookerr/VarGES/.

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.