Classical Identification Methods Research Articles

Systematic and structural analysis of forest mycobiota with the participation of Abies alba Mill. in the Pokutsko-Bukovyna Carpathians

The value and uniqueness of forest ecosystems are determined by their species diversity and biotic resistance to negative environmental impacts. The aim of the study is to determine the species composition, systematic position, trophic specialization, as well as the occurrence and abundance of forest mycobiota with Abies alba in the Pokutsko-Bukovyna Carpathians. The study was conducted in 2019-2023 within the forests of the branches “Kutske Forestry”, “Ivano-Frankivsk Forestry” and “Beregomet Forestry” of the State Specialized Enterprise “Forests of Ukraine”, as well as the forests of the National Nature Park “Vyzhnytskyi” and the National Nature Park “Hutsulshchyna”. Classical mycological identification methods were used in the study. Information from the interactive databases Index Fungorum, MycoBank, and World of Fungi of Ukraine was used to clarify the current systematic position and current names of taxa. It was found that the consortial relationships of the fungal biota of the Pokutsko-Bukovyna Carpathians forests are most closely related to the woody plants Picea abies, Abies alba, and Fagus sylvatica. During the mycological expedition, 54 species of representatives of the kingdom Fungi were identified, 81.5% of which belong to the order Basidiomycota and 18.5% to the order Ascomycota. It was found that the most numerous is the order Polyporales, and the least numerous are the orders Corticiales, Gomphales, Phallales, Pucciniales and Tremellales. It is shown that, according to trophic specialization, representatives of the studied mycobiota of the Pokutsko-Bukovyna Carpathians belong to the following groups: xylotrophic facultative saprotroph, xylotrophic facultative pathogen, xylotrophic obligate saprotroph, obligate pathogen and aboveground saprotroph. It is noted that the most widely represented are saproxylytrophs, which generally develop on dead and fallen wood. Thus, the systemic and structural analysis of the fungal biota of the Pokutsko-Bukovyna Carpathians forests allows for assessing the biocenotic value of fungi as bioindicators of the current state of stands, as well as identifying reference and valuable areas characterized by high biodiversity

Linear time periodic system approximation based on Floquet and Fourier transformations for operational modal analysis and damage detection of wind turbine

Operational Modal Analysis (OMA) identifies modal properties of mechanical structures from vibration data collected from a few sensors under operation conditions. These methods are widely used to monitor civil engineering structures that are modeled as time-invariant systems. However, when dealing with wind turbines and rotating machines, many dedicated OMA techniques were developed to deal with the time-periodic behavior. Existing methods pre-process the data to adapt them to classical identification techniques. Yet, these methods present limitations as either requiring a high number of measured rotation periods, assuming the assumption of an isotropic rotor (i.e. undamaged), or the knowledge of the rotational speed. This work proposes to lift these difficulties by proving that the application of the classical system identification methods can produce meaningful estimates for anisotropy monitoring. This observation is based on directly approximating the time-periodic dynamic behavior of the wind turbine as a properly defined time-invariant system under periodic inputs. It results in the possibility of using classical identification methods without modification to retrieve the system matrices of the approximated time-invariant system. This approach does not require knowing the rotor speed, requiring relatively fewer measurements and it is not restricted to isotropic rotors. The identified modes can be used reliably for the monitoring of operating wind turbines and more especially for fault detection, at the expense of losing a part of the complete description of the periodic system. The resulting anisotropy monitoring approach and its capacities are illustrated in two cases. Firstly, on an academic model of a wind turbine and then on an aero-servo-elastic numerical model of a rotating 10 MW wind turbine to demonstrate the ability of the method to deal with realistic and representative data. It is shown for the latest case, from sensors located on the blades of the wind turbine, the approach is able to identify correctly the system properties (frequencies and mode shapes) and is also able to detect efficiently a fault on a blade.

A practical approach for transfer function estimation using short, transient process data

Increased productivity in the industrial sector requires optimal closed loop performance. This can be solely achieved through optimal tuning of the controller parameters, which requires reliable and efficient process data, further employed in process estimations or high quality models. Most of the classical identification methods require long input-output data sets. In this paper we propose a technique to extract process information from short transient process data. The method is based on short sine test which can be superimposed on nominal process inputs, and estimates the process impulse response, consequently providing a band limited, but highly accurate, frequency response. This in turn is used to accurately estimate a high order plus dead time model. The method is reliable and requires less effort compared to classical step based methods. The main features of the proposed method are: 1) it uses a short duration test based on a sine input; 2) it produces accurate models in the presence of stochastic disturbances; and 3) it can be executed in open or closed loop. Numerical examples are provided to validate the theoretical approach and demonstrate the accuracy of the estimation.

The Effect of Upstream Unsteadiness on the Unstarting of a Supersonic Inlet Turbine

Abstract A 15% increase in thermal efficiency at medium pressure ratios is promised by rotating detonation engine technology over conventional Joule–Bryton cycles. A supersonic inlet turbine is a viable option to extract substantial work from the highly fluctuating and supersonic flow delivered by the detonating combustor. However, an additional unstarting mechanism based on the generation of a collective shock from the coalescence of leading-edge bow-shock waves further restricts the available design space. First, the effect of unsteady inlet conditions with variable frequency, amplitude, and mean value on collective shock generation is investigated. Then, a fast and cost-effective tool was developed and verified to predict bow-shock wave motion from a prescribed inlet Mach trend; the parameters of the transfer function were estimated with a classical identification method based on a step-response computational fluid dynamics (CFD) simulation. The model was employed to rapidly calculate the maximum amplitude of fluctuations accepted by the supersonic blade row for each frequency and average inlet Mach number. Finally, the influence of the inlet geometric angle, pitch to leading-edge thickness ratio, and static temperature on the model parameters is studied. The preliminary observations of the parametric analysis were confirmed by the rigorous quantitative approach of a global sensitivity analysis based on Sobol sensitivity indices.

Cross-correlation-guided operational modal identification of bridge considering asynchronous signals

The accurate identification of modal parameters is crucial for bridge health monitoring. In practical scenarios, there is often an asynchronization in the multi-channel vibration signals obtained from wireless sensors or offline measurements without temporal data. This asynchronization can lead to significant bias in the mode shapes when using the classical operational modal identification method. To address this issue, a new cross-correlation (CC) coefficient-based time lag detection is proposed. This technique aims to determine whether the multi-channel signals are synchronous and to prepare the reliably synchronous signals for modal identification. Because this detection technique specifically focuses on detecting the time lag between single degree-of-freedom (DOF) asynchronous signals, a multivariate variational mode decomposition (VMD) technique is developed to handle the multiple DOF signals of a bridge. To ensure that the decomposed components are not distorted or mixed, the penalty parameter in the multivariate VMD is optimized by minimizing the proposed similarity index. The proposed time lag detection process is validated using numerical and actual bridge examples. The results demonstrate that the new CC coefficient-based method is more effective than the classical CC coefficient or phase-based method, regardless of the employed mode or the amount of time lag present.

Supervised Contrastive Learning for RFF Identification With Limited Samples

Radio frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary dataset and the target dataset often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary datasets, in this paper, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN”. First, we analyze the causes of RFF, and model the RFF identification problem with augmented dataset. A non-auxiliary data augmentation method is proposed to acquire an extended dataset, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long-short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

Post Reclamation Evaluation of Residual Hydrocarbons in Crude Oil Contaminated Soil Using Gas Chromatographic Techniques and Plant Growth Indices

The present study was undertaken to evaluate the post-reclamation of crude oil-contaminated soil using gas chromatographic technique and plant growth indices. Enrichment techniques, turbidometric test, plasmid curing test, classical identification method, gas chromatographic flame ionization detection technique and plant growth indices were carried out using standard procedures. The results indicated that 22 of the isolates were observed to have high pollutant degrading potentials (A600nm > 0.3) due to their crude oil utilization ability. The results also indicated that isolates which were able to degrade crude oil belonged to genera Bacillus, Pseudomanas sp., Ochrobacterium sp. and Enterobacter sp. Moreso, the remediation percentages recorded were 98 %, 80 %, 21 % and 98 %, 78 % and 22 % for the respective crude oil pollution (3 %, 5 % and 10 %) for soil inoculated plasmid borne bacteria whereas soil inoculated with plasmid cured bacterial cultures had 65 %, 52 %, 11 % and 65 %, 50 % and 20 % degradation of the same concentration of the total petroleum hydrocarbon after 60 days for both Phaseolus vulgaris and Glycine max plant seeds, respectively. The remediation percentages in the uninoculated controls include 3 %, 5 %, 10 % and 12 %, 9 % and 5 % across the pollutions for both plant seeds. A significant (P < 0.05) increase in the growth parameters of the leguminous plants like percentage germination, mean plant shoot length, mean plant root length, plant fresh weight and number of root nodules were seen more in the soil microcosms inoculated with plasmid-borne bacteria than microcosm inoculated with culture of plasmid cured bacteria across all crude oil dilutions. Crude oil had a dose-dependent negative effect on the mean bacterial counts of each of the polluted soil containing the two different plants. Thus, the study clearly suggests that the selected hydrocarbon bacterial degraders could be effectively exploited in the remediation of crude oil-polluted sites.

Koopman Operator Based Predictive Control With a Data Archive of Observables

Abstract The control of complex systems is often challenging due to high-dimensional nonlinear models, unmodeled phenomena, and parameter uncertainty. The increasing ubiquity of sensors measuring such systems and increased computational resources has led to an interest in purely data-driven control methods, particularly using the Koopman operator. In this paper, we elucidate the construction of a linear predictor based on a sequence of time realizations of observables drawn from a data archive of different trajectories combined with subspace identification methods for linear systems. This approach is free of any predefined set of basis functions but instead depends on the time realization of these basis functions. The prediction and control are demonstrated with examples. The basis functions can be constructed using time-delayed coordinates of the outputs, enabling the application to purely data-driven systems. The paper thus shows the link between Koopman operator-based control methods and classical subspace identification methods. The approach in this paper can be extended to adaptive online learning and control.

Edge-weights-based method to identify influential spreaders in complex networks

Identifying influential nodes has drawn great attention in recent years. In this paper, a novel method for identifying influential spreaders based on potential edge weights (WDK for simplicity) for both undirected and unweighted networks is proposed. Degree and k-shell of a node and its neighbors are considered simultaneously, which are regarded as the weight of the edge directly connected to the node. The algorithm considers not only the local information of nodes but also their location information. The proposed method not only improves the accuracy of node mining but also has approximately linear time complexity, which indicates that the proposed method is suitable for large-scale networks. In order to validate the effectiveness of the proposed method, different evaluation indexes are introduced in nine real networks. Compared with five classical key nodes identification methods, the experimental results show that the proposed method performs optimally in all networks.

Detection of Leptospira wolffii in Water and Soil on Livestock Farms in Kelantan After a Massive Flood

Pathogenic leptospires cause leptospirosis in both animals and humans around the world. Humans can be infected by leptospires through direct contact with infected animals or indirect contact with leptospires contaminated water or soil. Outbreaks of leptospirosis usually occur after heavy rainfall and flooding, particularly in endemic areas. The aim of this study was to detect the presence of leptospires in water and soil in livestock farms after a massive flood in Kelantan. Samples were collected from 28 livestock farms in Kelantan, comprising 62 water samples and 62 soil samples, respectively. The samples were filtered and inoculated into a semisolid EMJH medium containing 5-FU for the isolation of Leptospira spp. The Leptospira spp. isolates were then identified using classical methods (1M NaCl, 8-azaguanine, and 13 °C), serology (MAT), multiplex polymerase chain reaction (mPCR), and DNA sequencing. The classical identification methods showed varying results and failed to differentiate between pathogenic and non-pathogenic leptospires. MAT showed the isolates reactions against serovars Autumnalis, Hebdomadis, Pyrogenes, Bataviae, Patoc, and Wolffii. However, mPCR showed that all isolates were non-pathogenic Leptospira spp. Further identification using DNA sequencing found that all isolates were identified as Leptospira wolffii, an intermediate species of Leptospira. The presence of L. wolffii in water and soil provides evidence that this species is circulating in the environment and could potentially transmit to humans and animals.

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Accurate mapping of rice-growing areas is essential to ascertain the spatial distribution of rice fields, and ensure food security. It is a challenging task to timely and accurate identify rice under the complex terrain due to its diversified land cover, small- or middle-sized rice fields with fragmented distribution. In this paper, the time series VV and VH backscatter coefficient datasets were first constructed based on 411 sentinel-1 synthetic aperture radar (SAR) images in Chongqing city with complex terrain. Then, the rice multi-characteristic parameters, including SAR backscatter features, composite features, rice phenological parameters, texture features and topographic features, were generated. On this basis, the homogeneous image objects were produced. Furthermore, a rice identification algorithm combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization SAR (MPHO-DPSAR) was established. The research demonstrated that the MPHO-DPSAR algorithm can achieve accurate mapping of small and medium-sized and fragmented rice fields in regions under complex terrain according to the accuracy evaluation at three levels and the comparison with other three classical rice identification methods. The suitability and limitations of proposed MPHO-DPSAR algorithm were also discussed from the aspects of SAR data temporal and spatial resolution, rice phenology, and surface landscape complexity.

Analytical quality control of wines and wine materials

Ensuring food safety of the quality of sold products is one of the urgent problems of the wine industry. The trend of falsification of well-known brands of domestic and imported wines is a characteristic feature of the modern market of wine products. Known methodological bases for assessing the quality of wine products in most cases do not have the potential to recognize many modern «technological solutions». To solve this problem an analytical method can be proposed on modern equipment "FTIR BACCHUS 3". This analyzer allows you to determine the chemical substances and the range characteristics of these substances. This method has been chosen for the identification of wine samples and not by chance, when using it, the analysis time is significantly reduced, and the range of chemical elements determined by the analyzer is quite wide, and reagents are not needed for this equipment. The method does not require preliminary preparation of samples; it is also quite new, not popular in Russia. The article presents a comparative review of classical wine identification methods. The presented material indicates the relevance of creating a database on the chemical composition of wines that determine their authenticity using new instrumental techniques. The proposed multi-parameter wine analyzer FTIR BACCHUS 3 is a system for the analysis of worts and wines using chemometric methods, based on the acquisition of the mid-IR spectrum (FTIR). The method is based on chemometrics, i.e. obtaining chemical data using mathematical methods of data processing and extraction, which will increase the reliability of assessing the authenticity and place of origin of wine products.

Analysis and identification of suspected snake venom samples using nano-ultra-high performance liquid chromatography-high resolution mass spectrometry

Snake venom is a complex mixture secreted from the glands of poisonous snakes， which contains proteins， peptides， lipids， nucleosides， sugars， amino acids， amines， metal ions， and other components. According to the toxicological classification， snake venoms can be classified as neurotoxins， anticoagulants and procoagulant toxins， cardiac toxins， other toxin proteins， and enzymes. Proteins and peptides are the key components of snake venom. The establishment of rapid， accurate analysis and identification methods for proteins in snake venom is a prerequisite for snake venom-related forensic identification， intoxication events， and pharmaceutical development. Until now， the classical analysis and identification methods have mainly been biochemical or immunoassays for DNA or proteins， such as polymerase chain reaction， agglutination test， enzyme-linked immunosorbent assay， fluorescent immunoassay， and various biosensing approaches. These methods have some limitations such as a high false-positive ratio， low sensitivity， poor anti-interference ability， and limited species discrimination capability. In recent years， with the rapid development of mass spectrometry （MS） techniques， the proteomics of snake venom has also attracted much attention and has contributed to the identification of snake species， in which non-targeted and targeted proteomics represent two main divisions. However， species identification via proteomics is in its infancy in forensic science. First， the tandem MS spectra of peptide sequences are highly complex， which poses a great challenge for the strict and accurate matching of peptides based on the rational speculation of MS fragmentation rules and theoretical calculations in non-targeted proteomics. Second， for the confirmation and identification of unknown substances， reference substances are commonly needed， but those for snake venom are lacking. Proteomics in snake venom identification is still in progress to improve the identification confidence and clarify the identification rules. In this work， a method based on nano-ultra-high performance liquid chromatography-quadrupole-orbitrap high-resolution mass spectrometry （Nano LC-MS/HRMS） and size exclusion chromatography （SEC） was developed for identifying proteins and their source species， with strict rules for five suspected snake venom samples and their contamination in one case. Three SEC elution peaks were obtained from each of the five samples， which were lyophilized and treated with trypsin in solution， and then separated and analyzed by Nano LC-MS/HRMS. First， the Full MS/dd MS2 mode was used for the non-targeted acquisition of peptide information in the samples， and after submission to the Swiss-Prot database， the protein databases of Serpentes， Colubroidea， Elapidae， Elapinae， and Naja were contracted stepwise and compared. A total of 32 proteins from Naja atra were identified under the conditions of both peptide spectrum match and false discovery rate less than 1%， and number of characteristic peptides greater than or equal to two. All of these were derived from ten families of Naja atra， mainly three-finger toxins， metalloproteinases， and phospholipase A2. Proteins D3TTC2， D5LMJ3， Q7T1K6， Q9DEQ3， and Q9YGI4 were the most common among the five samples. Finally， the parallel reaction monitoring mode was adopted to select two unique peptides for each protein for targeted verification. It was considered that a protein in the samples was truly identified when it met the strict standard "the Δm/z of at least 75% y+ and b+ ions of each unique peptide was less than 5 ppm". After these consequently procedures， we identified that all five samples contained the venom of the Naja atra. Our identification method is a systematic and strict example that can provide effective technical support for the forensic identification of snake venom poisoning， as well as for pharmaceutical development toward snake venoms.

Data-driven nonlinear system identification of blood glucose behaviour in Type I diabetics

Data-driven nonlinear system identification with sparse regression is a promising method to represent nonlinear dynamics in the form of a rigorous model description. Therefore, nonlinear functional structure identification and parameter estimation are performed simultaneously. Classical identification methods require functional structures that are manually derived using process knowledge either from first principles or practical experience. However, the effort required to provide these structures is time-consuming, labour-intensive, and in connection with operational trials in production plants, also associated with high costs. In addition, the latest sparse regression solution for nonlinear system identification does not offer an analytical solution due to the properties of the L1 norm. For this reason, sparse regression with smoothed L1 regularisation is proposed for nonlinear system identification. For this purpose, a nonlinear library function is first constructed based on the extended dynamic mode decomposition theory (eDMD), which contains all possible nonlinear bijective function candidates. For the process description, the most suitable functions with the related weighting parameters are selected using the regularisation properties. The performance of the method is demonstrated using the blood glucose behaviour from Type I Diabetes. The validation of the method is performed for a simulation study with and without noise influence and applied to experimental data of two patients in a Python simulation. It can be shown that the identification is successful for both studies with a performance limit for a signal-to-noise ratio (SNR) of 0.45 (3.46 dB).

Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations

Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the growing variety of available materials. Besides the classical iterative optimization-based parameter identification method, novel machine learning-based methods represent promising alternatives, especially in terms of efficiency. However, the machine learning algorithms, architectures, and settings significantly affect the resulting accuracy. This work presents a comparative study of different machine learning algorithms based on virtual datasets with varying settings for the direct inverse material parameter identification method. Multilayer perceptrons, convolutional neural networks, and Bayesian neural networks are compared; and their resulting prediction accuracies are investigated. Furthermore, advantages in material parameter identification by uncertainty quantification using the Bayesian probabilistic approach are examined and discussed. The results show increased prediction quality when using convolutional neural networks instead of multilayer perceptrons. The assessment of the aleatoric and epistemic uncertainties when using Bayesian neural networks also demonstrated advantages in evaluating the reliability of the predicted material parameters and their influences on the subsequent finite element simulations.

Identifying potential microRNA biomarkers for colon cancer and colorectal cancer through bound nuclear norm regularization.

Colon cancer and colorectal cancer are two common cancer-related deaths worldwide. Identification of potential biomarkers for the two cancers can help us to evaluate their initiation, progression and therapeutic response. In this study, we propose a new microRNA-disease association identification method, BNNRMDA, to discover potential microRNA biomarkers for the two cancers. BNNRMDA better combines disease semantic similarity and Gaussian Association Profile Kernel (GAPK) similarity, microRNA function similarity and GAPK similarity, and the bound nuclear norm regularization model. Compared to other five classical microRNA-disease association identification methods (MIDPE, MIDP, RLSMDA, GRNMF, AND LPLNS), BNNRMDA obtains the highest AUC of 0.9071, demonstrating its strong microRNA-disease association identification performance. BNNRMDA is applied to discover possible microRNA biomarkers for colon cancer and colorectal cancer. The results show that all 73 known microRNAs associated with colon cancer in the HMDD database have the highest association scores with colon cancer and are ranked as top 73. Among 137 known microRNAs associated with colorectal cancer in the HMDD database, 129 microRNAs have the highest association scores with colorectal cancer and are ranked as top 129. In addition, we predict that hsa-miR-103a could be a potential biomarker of colon cancer and hsa-mir-193b and hsa-mir-7days could be potential biomarkers of colorectal cancer.

System response modeling of HMCVT for tractors and the comparative research on system identification methods

The HMCVT (Hydro-mechanical Continuously Variable Transmission) is widely applied in agricultural and engineering machinery such as tractors. Getting a correct system response model is the key to HMCVT control strategy implementation and controller design. The study decomposes and analyzes the HMCVT system and builds response models. The study proposes to decompose the HMCVT system into three dynamic response stages including the electronic proportional pressure relief valve response, the variable plunger displacement response and the pump-motor system speed ratio response, and builds the response models of three stages respectively and then obtains the general form of transfer function of HMCVT system using a tandem method. The study proposes a system identification method based on the prior-model-heuristic-intelligent-optimization algorithm, and makes a comparative analysis between the classical system identification method (the area method) and the three heuristic intelligent optimization algorithms (the FI-SA, the I-SA and the I-AFSA) in terms of system identification result. The study compares the results of identification methods through a step response test of a model of HMCVT for tractors, and verifies the correctness of the transfer function model of HMCVT system proposed. Research results show that the general form of HMCVT system response model should be a 4-order denominator 1-order numerator transfer function model (the mean identification precision of 15 groups of measurement tests is about 0.9849). The area method is affected by the sampling frequency in the response measurement and requires a complete response process measured. The system identification based on heuristic intelligent optimization algorithm has low dependence on sampling frequency and time consumed and high precision. According to the results of the calculation example in the study, the coefficient of determination about the identified model is 1 under the measurement frequency of 4 Hz and the measurement duration of 4 s by using the FI-SA, the I-SA, and the I-AFSA. The precision is improved by 4.93 % compared with using the area method. According to the actual test results, the coefficients of determination about the FI-SA and the area method are 0.9939 and 0.9834, respectively. Comparing the three heuristic intelligent optimization algorithms, the FI-SA proposed has the fastest identification speed. The results of 20 groups measurement tests show that the average iteration number of FI-SA are 43.58 % and 17.95 % lower than those of I-SA and I-AFSA, and the average time consumption of FI-SA is 43.36 % and 84.76 % lower than that of I-SA and I-AFSA.

Local-Forest Method for Superspreaders Identification in Online Social Networks.

Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.

Validation of a dynamic non-linear grinding circuit model for process control

A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for process control, to steady-state process data of an industrial single-stage grinding mill circuit. Step-test data from the industrial plant is used to validate the response of the non-linear model. The results indicate that the model provides a qualitatively accurate response of the main process variables. Because the non-linear model parameters can be calculated from steady-state data, it provides an advantage over classical system identification methods as it does not require an expensive and disruptive step-test campaign to develop linear transfer function models. The model is ideal for model-based predictive process control.

A frequency domain approach for local module identification in dynamic networks

In classical approaches of dynamic network identification, in order to identify a system (module) embedded in a dynamic network, one has to formulate a Multi-input-Single-output (MISO) identification problem that requires identification of a parametric model for all the modules constituting the MISO setup including (possibly) the noise model, and determine their model order. This requirement leads to model order selection steps for modules that are of no interest to the experimenter which increases the computational complexity for large-sized networks. Also, identification using a parametric noise model (like BJ method) can suffer from local minima, however neglecting the noise model has its impact on the variance of the estimates. In this paper, we provide a two-step identification approach to avoid these issues such that the complexity of the problem is independent of the complexity of the network. The first step involves performing a non-parametric indirect approach for a MISO identification problem to get the non-parametric frequency response function (FRF) estimates and its variance as a function of frequency. In the second step, the estimated non-parametric FRF of the target module is smoothed using a parametric frequency domain estimator with the estimated variance from the previous step as the non-parametric noise model. The developed approach is practical with weak assumptions on noise, uses the available toolbox, requires a parametric model only for the target module of interest, and uses a non-parametric noise model to reduce the variance of the estimates. Numerical simulations illustrate the potentials of the introduced method in comparison with the classical identification methods.