Two-step Algorithm Research Articles

Critical clearing time estimation of multi-machine power system transient stability using fuzzy logic

<span lang="EN-US">Studying network stability requires determining the best critical clearing time (CCT) for the network after the fault has occurred. CCT is an essential issue for transient stability assessment (TSA) in the operation, security, and maintenance of an electrical power system. This paper proposes an algorithm to obtain CCT based on fuzzy logic (FL) under fault conditions, for a multi-machine power system. CCT was estimated using a two-step fuzzy logic algorithm: the first step is to calculate Δt, which represents the output of the FL, while maximum angle deviation (δmax) represents the input. The second step is to classify the system if it is a stable or unstable system, based on two inputs for FL, the first mechanical input power (Pm), the second average accelerations (Aav). The results of the proposed method were compared with the time domain simulation (TDS) method. The results showed the accuracy and speed of the estimation using the FL method, with an error rate not exceeding 5%, and reduced the performance time by about half the time. The proposed approach is tested on both IEEE-9 bus and IEEE-39 bus systems using simulation in MATLAB.</span>

Performance Boosting for Two-Step Localization Methods in Distributed MIMO Radar via a Robust Post-Processing Scheme

In radar target localization using time delay (TD) measurements, reliable TD extraction is an important prerequisite for conventional two-step localization methods. In the distributed multiple-input multiple-output (MIMO) radar, the low-quality TD measurements (i.e., outliers) always occur due to the target scattering fluctuations in various transmitter-receiver paths, which results in serious performance degradation to the generic two-step localization methods. To this end, we propose a simple yet effective performance boosting scheme in this letter. It constructs a robust estimator based on the Huber loss function and solves this problem iteratively by using the majorization-minimization (MM) technique. This proposal is a general post-processing module, and thus it can be combined with any existing two-step algorithms to achieve performance improvement in the presence of outliers. The simulations verify the effectiveness of the proposed scheme by comparing with the derived theoretical lower bound. In addition, the parameter robustness and the computational burden are also evaluated.

Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data

BackgroundLocal governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA.MethodsThe first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County.ResultsThe ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates.ConclusionsThese SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.

Clinical Evaluation of IDH Mutation Status in Formalin-Fixed Paraffin-Embedded Tissue in Gliomas.

Determination of isocitrate dehydrogenase (IDH) 1/2 mutational status is crucial for a glioma diagnosis. It is common for IDH mutational status to be determined via a two-step algorithm that utilizes immunohistochemistry studies for IDH1 R132H, the most frequent variant, followed by next-generation sequencing studies for immunohistochemistry-negative or immunohistochemistry-equivocal cases.The objective of this study was to evaluate addinga rapid real-time polymerase chain reaction (RT-PCR) assay to the testing algorithm. METHODS: We validated a modified, commercial, qualitative, RT-PCR assay with the ability to detect 14 variants in IDH1/2 in formalin-fixed paraffin-embedded glioma tumor specimens. The assay was validated using 51 tumor formalin-fixed paraffin-embedded specimens. During clinical implementation of this assay, 48 brain tumor specimens were assessed for IDH result concordance and turnaround time to result. Concordance between the RT-PCR and sequencing and IHC studies was 100%. This RT-PCR assay also showed concordant results with IHC for IDH1 R132H for 11 of the 12 (92%) tumor specimens with IDH mutations. The RT-PCR assay yielded faster results (average 2.6 days turnaround time) in comparison to sequencing studies (17.9 days), with complete concordance. In summary, we report that this RT-PCR assay can reliably be performed on formalin-fixed paraffin-embedded specimens and has a faster turnaround time than sequencing assays and can be clinically implemented for determination of IDH mutation status for patients with glioma.

<i>Clostridioides difficile</i> – a new name, the old problems with diagnosis and treatment

Clostridium difficile was reclassified in 2016 under a new name of Clostridioides difficile. In 2021 to 2022, some changes were implemented into the main clinical guidelines on the diagnosis and treatment of infections caused by this pathogen. A two-step algorithm based on identification of C. difficile and then its toxins is still recommended for the diagnosis. The treatment regimens for the first episode and relapses of C. difficile-associated infection are based on metronidazole, vancomycin and fidaxomycin. Additional effectiveness of therapy has been associated with the use of microbiocenosis-oriented technologies aimed at restoration of the colon microbiota. In the Russian Federation, all widely applied diagnostic methods are available and all recommended antimicrobials, except fidaxomycin, as well as bezlotoxumab and fecal microbiota transplantation.

On the Stability and Acceleration of Projection Algorithms

The focus of this paper is the presentation of a new type of mapping called projection Jungck zn- Suzuki generalized and also defining new algorithms of various types (one-step and two-step algorithms) (projection Jungck-normal N algorithm, projection Jungck-Picard algorithm, projection Jungck-Krasnoselskii algorithm, and projection Jungck-Thianwan algorithm). The convergence of these algorithms has been studied, and it was discovered that they all converge to a fixed point. Furthermore, using the previous three conditions for the lemma, we demonstrated that the difference between any two sequences is zero. These algorithms' stability was demonstrated using projection Jungck Suzuki generalized mapping. In contrast, the rate of convergence of these algorithms was demonstrated by contrasting the rates of convergence of the various algorithms, leading us to conclude that the projection Jungck-normal algorithm is the fastest of all the algorithms mentioned above.

A Two-Step Simulated Annealing Algorithm for Spectral Data Feature Extraction

To address the shortcomings in many traditional spectral feature extraction algorithms in practical application of low modeling accuracy and poor stability, this paper introduces the "Boruta algorithm-based local optimization process" based on the traditional simulated annealing algorithm and proposes the "two-step simulated annealing algorithm (TSSA)". This algorithm combines global optimization and local optimization. The Boruta algorithm ensures that the feature extraction results are all strongly correlated with the dependent variable, reducing data redundancy. The accuracy and stability of the algorithm model are significantly improved. The experimental results show that compared with the traditional feature extraction method, the accuracy indexes of the inversion model established by using the TSSA algorithm for feature extraction were significantly improved, with the determination coefficient R2 of 0.9654, the root mean square error (RMSE) of 3.6723 μg/L, and the mean absolute error (MAE) of 3.1461 μg/L.

Estimation of Dynamical Thermoacoustic Modes Using an Output Only Observer Kalman Filter-Based Identification Algorithm

Abstract Thermoacoustic instabilities have plagued the operation of gas turbine engines for years and significant research is being conducted in detecting and understanding them. In this paper, an output only identification technique is employed for a noise induced dynamical system representing combustion instability behavior. This approach is called the output only observer Kalman filter identification (O3KID) and its first step solves for least squares from a set of algebraic equations constructed from just the measured output. The least squares solution gives the Markov parameters (impulse response) and the output residuals. The subsequent step takes the Markov parameters or the residuals to solve for the system matrices using any deterministic subspace identification method. In using this direct noniterative two-step algorithm, it is possible to estimate the eigenmodes and damping coefficients from output measured data. To validate the algorithm, a system of independent harmonic oscillators, excited by random noise is used to generate surrogate data representing pressure oscillations in a combustor prior to an instability. The error in estimating the eigen frequencies and damping are <1%. This fast direct approach could be used to provide an early warning indicator in industrial gas turbines by tracking the rate of damping of dominant eigenmodes. Additionally, saving the state space parameters periodically can serve as a data-lean option to track changes of the dynamics and across a gas turbine fleet.

Vertical characterization of fine and coarse dust particles during an intense Saharan dust outbreak over the Iberian Peninsula in springtime 2021

Abstract. An intense and long-lasting Saharan dust outbreak crossed the Iberian Peninsula (IP) from the southwest (SW) to the northeast (NE) from 25 March until 7 April 2021. This work aims to assess the optical and mass contribution of both fine and coarse dust particles along their transport. Five Iberian lidar stations were monitoring the transport and evolution of the Saharan dust particles, i.e. El Arenosillo/Huelva, Granada, Torrejón/Madrid and Barcelona in Spain, and Évora in Portugal. The particular meteorological conditions determined the aerosol scenario along the overall dust event, differing in the first part of the event (25–31 March), in which the strongest dust incidence occurred on 29–31 March at the south and central stations and 1 April at Barcelona, from the second one (1–7 April). The use of the two-step POLIPHON algorithm showed the relevance of using polarized lidar measurements for separating the aerosol properties of dust fine and coarse particles as an added value. Both the fine dust (Df) and coarse dust (Dc) components of the total particle backscatter coefficient (total dust, DD = Dc + Df) were separately derived. The dust plume was well-mixed with height and no significant differences were found in the vertical structure of both the Dc and Df particle backscatter coefficients. From the beginning of the dust outbreak until 1 April, the vertical Df / DD mass ratio was nearly constant in time at each station and also in altitude with values of ∼ 10 %. Moreover, the mean dust optical depth at 532 nm was decreasing along that dust pathway, reporting values from SW to NE stations of 0.34 at El Arenosillo/Huelva, 0.28 at Granada, 0.20 at Évora, 0.28 at Torrejón/Madrid, and 0.14 at Barcelona, although its Df / DD ratio remained almost constant (28 %–30 %). A similar pattern was found for the total dust mass loading and its Df / DD ratio, i.e. mostly decreasing mean mass values were reported, being constant in its Df / DD ratio (∼ 10 %) along the SW–NE dust pathway. In addition, the episode-mean centre-of-mass height increased with latitude overall, showing a high variability, being greater than 0.5 km at the southern sites (El Arenosillo/Huelva, Granada, Évora) and ∼ 1.0 km at Torrejón/Madrid and Barcelona. However, despite the relatively high intensity of the dust intrusion, the expected ageing of the dust particles was hardly observed, by taking into account the minor changes found in the contribution and properties of the coarse and fine dust particles. This is on the basis that the IP is relatively close to the Saharan dust sources and then, under certain dust transport conditions, any potential ageing processes in the dust particles remained unappreciated. The following must be highlighted: the different relative contribution of the fine dust particles to the total dust found for their optical properties (∼ 30 %) associated with the radiative effect of dust, with respect to that for the mass features (∼ 10 %) linked to air quality issues, along the overall dust event by crossing the IP.

Two-step Domain Adaptive Semantic Segmentation Algorithm of Driving Scene

Semantic segmentation of driving scenes is an important task in the field of automatic driving perception. The existing semantic segmentation based on deep learning method needs a lot of manpower cost to label data, and the changeable driving scenes will also lead to the performance degradation of semantic segmentation model in practical application. Therefore, in this paper, labeled computer-generated images are used as source domain data, unlabeled real driving scene images are used as target domain data, and unsupervised domain adaptive method is used to transfer knowledge when the target domain data is unlabeled. Based on the one-step domain adaptive semantic segmentation algorithm, a two-step domain adaptive semantic segmentation algorithm is proposed, and an image quality evaluation module is introduced to optimize the algorithm, so as to improve the performance index of cross-domain semantic segmentation tasks to some extent and reduce the dependence on data labeling and labeling cost.

Energy-Efficient Sparsity-Driven Speech Enhancement in Wireless Acoustic Sensor Networks

Wireless acoustic sensor network (WASN) has shown a superiority over conventional microphone arrays in many aspects. There exists an important tradeoff between the performance and power consumption, as usually the sensors are power driven with a limited amount of battery resource. Given a prescribed performance bound, in literature sensor selection (SS) and rate allocation (RA) methods can be leveraged to optimize the energy efficiency. In this work, we propose a joint rate allocation and sensor selection (RASS) approach to simultaneously optimize the sensor subset and rate distribution, which is formulated by minimizing the total transmission power in terms of selection and bit-rate variables and constraining the residual noise power. It can be shown that under a set of linear constraints on beamforming, the linearly-constrained minimum variance (LCMV) beamformer is the optimal noise reduction filter. Based on this, the RASS reduces to a mixed semi-definite and bilinear programming problem, which is then solved using a two-step algorithm. As the selection and bit-rate unknowns are bilinear, we first consider to optimize their product, resulting in an upper bound of RASS. Then, we use McCormick envelopes to relax the bilinear constraint, resulting in a linear program. The final selection and bit-rate solutions are obtained by posterior randomized rounding. It can be shown that SS and RA are special cases of the proposed RASS. Numerical results using simulated WASNs validate the power efficiency of the proposed method as well as the robustness against dynamic factors.

Inference of 3D genome architecture by modeling overdispersion of Hi-C data.

We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. We first confirm the presence of overdispersion in several real Hi-C datasets, and we show that the overdispersion arises even in simulated datasets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms, both MDS-based and statistical methods. We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD license. Supplementary data are available at Bioinformatics online.

Soft Sensing of NOx Emissions From Thermal Power Units Based on Adaptive GMM Two-Step Clustering Algorithm and Ensemble Learning

The accurate measurement of NOx concentration is the basis of accurate ammonia injection in selective catalytic reduction (SCR) system of thermal power unit. Excessive or too little ammonia injection will cause ammonia escape or exceeding the standard of NOx emission, and cause environmental pollution. There exist a large measurement lag and errors during the purging process in the measurement of NOx concentration in the SCR system of thermal power units. Therefore, to enable the intelligent control of the SCR system, it is necessary to carry out soft sensing of NOx emissions. This paper proposes a soft sensing algorithm for NOx emissions of thermal power units based on an adaptive Gaussian Mixture Model (GMM) two-step clustering algorithm and ensemble learning. Adaptive GMM two-step clustering (AGTSC) algorithm is used to softly divide the historical data into working conditions. Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM) is also adopted to construct individual learners for each working condition. Here we use the parameter regression algorithm to train the combiner based on the output of the individual learner and the membership signal of the working condition as the input of the combiner. The clustering algorithm is tested with three datasets. The results show that this algorithm addresses the shortcomings of GMM including not being applicable to the clustering of non-convex datasets and manual selection of the number of clusters. The soft sensing algorithm is verified by using the historical data of the SCR system of 1000 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MW</i> thermal power units. The results show that the proposed method has higher accuracy and stronger generalization ability than the conventional method, hence providing an effective method for soft sensing of NOx emissions of thermal power units.

LPI-Based Transmit Resource Scheduling for Target Tracking with Distributed MIMO Radar Systems

This paper explores the effect of the low probability of intercept (LPI) performance optimization on target tracking in distributed MIMO (D-MIMO) radar systems, where an intercept receiver is equipped on the target. The aim of the proposed LPI-based transmit resource scheduling (LPI-TRS) strategy is to optimize the LPI performance of the D-MIMO systems meanwhile satisfy the target tracking accuracy requirement by the proper transmit resource management design. Different from the existing research, we introduce a cascading mathematical model to describe the intercept process with the specific intercept receiver. A cascading product of the probabilities of interception and detection deduces the probability of report <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ P_{R}$</tex-math></inline-formula> for the interceptor, to evaluate the LPI performance of one transmit radar node. Accordingly, the maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ P_{R}$</tex-math></inline-formula> is employed, for the overall D-MIMO radar systems, to perform as the global LPI performance metric. Based on the min-max criterion, by minimizing the global LPI metric, a novel non-convex optimization problem is formulated wherein the requirements for target tracking performance and the system resource limitation are considered as the constraints. Wherein the tracking task is fulfilled by the two-tier hybrid processing framework. Meanwhile, we introduce a convex relaxation-based approach, so the sub-optimal results are acquired via a two-step fast computational search algorithm. Subsequently, the closed-loop scheme is set up to achieve LPI-based resource awareness in D-MIMO radar systems. Several numerical results demonstrate the theoretical analysis and show that the proposed LPI-TRS leads to significant LPI performance improvements.

User Association for Reconfigurable Intelligent Surfaces Aided Cell-Free Networks

Cell-free (CF) network can efficiently suppress inter-cell interference and enable multiple base stations (BSs) to serve users cooperatively without being affected by cell boundaries. However, to further increase the network capacity, a large number of BSs need to be deployed, resulting in high cost and power consumption. To tackle this problem, reconfigurable intelligent surface (RIS) has emerged as a cost-effective technology to enhance energy and spectral efficiency of wireless communications. In this paper, we consider an RIS-aided CF network by exploiting their complementary advantages where each user assisted by a dedicated RIS can associate with multiple BSs. We first characterize each user's average signal-to-interference-plus-noise ratio (SINR) in the RIS-CF network. Then, we formulate an SINR balancing problem to maximize the minimum SINR among all users by designing the BS-user associations. Despite of the non-convexity and combinatorial feature of this problem, a two-step fractional linearization algorithm is developed to solve it optimally. Specifically, we transform a nested mixed-integer linear fractional program (MILFP) into a solvable mixed-integer linear programming (MILP) problem through the generalized fractional programming and reformulation-linearization method. Finally, we provide numerical results to validate the effectiveness of our proposed algorithm compared to other benchmark schemes.

A Clustering Algorithm for Polygonal Data Applied to Scientific Journal Profiles.

Millions of papers are submitted and published every year, but researchers often do not have much information about the journals that interest them. In this paper, we introduced the first dynamical clustering algorithm for symbolic polygonal data and this was applied to build scientific journals profiles. Dynamic clustering algorithms are a family of iterative two-step relocation algorithms involving the construction of clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between clusters and their corresponding prototypes The application gives a powerful vision to understand the main variables that describe journals. Symbolic polygonal data can represent summarized extensive datasets taking into account variability. In addition, we developed cluster and partition interpretation indices for polygonal data that have the ability to extract insights about clustering results. From these indices, we discovered, e.g., that the number of difficult words in abstract is fundamental to building journal profiles.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data, which can provide excellent accuracy and interpretability. In addition, a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model. The results of the study show that the model error rate is reduced by 6.22% and the recall rate is improved by 11.03% compared to the algorithms commonly used in credit risk research, significantly improving the ability to identify defaulters. Finally, the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Two-step inertial adaptive iterative algorithm for solving the split common fixed point problem of directed operators

Two-step inertial adaptive iterative algorithm for solving the split common fixed point problem of directed operators

Low-Latency Oriented Resource Allocation for MEC-Assisted Adaptive Bitrate Video Streaming

This paper focuses on low-latency transmissions for mobile edge computing (MEC)-assisted adaptive bitrate (ABR) video streaming. With the aim to realize minimum video service latency, an optimization problem is formulated by jointly designing caching, computing and power allocation, subject to diverse video version downloading rate requirements and the limited resources of MEC servers. The formulated optimization problem turns out to be a mixed integer nonlinear programming (MINLP) problem. To solve the problem and satisfy the low-latency service requirement of ABR video streaming, a low-complexity two-step iterative algorithm is proposed by decomposing the MINLP problem into two subproblems, a joint caching and computing subproblem and a power allocation subproblem. Due to the highly coupled nature of the caching and computing, the first subproblem is an integer nonlinear programming (INLP) problem. To solve it efficiently, the INLP problem is equivalently transformed into an integer linear programming (ILP) problem, which is thereafter solved by MATLAB intlinprog function. The second subproblem is a convex problem and is solved by low-complexity power allocation (LCPA) algorithm. Intensive simulation results show that the proposed algorithm converges in a very short time, and can significantly reduce the average latency compared to the state-of-the-art baselines.

Assessing the Soldier Survivability Tradespace Using a Single IMU

Soldier burden is influenced by the environment, metabolic demands, equipment properties, and psychological stressors; however, much of our knowledge of soldier burden is in the context of body-borne load mass in controlled laboratory environments. Thus, to further our understanding of how all aspects of soldier burden affect the survivability tradespace (i.e., performance, health, and susceptibility to enemy action), field-based motion capture methods are needed. We developed a human activity recognition method using the deep convolutional long short-term memory neural network architecture, trained using a single inertial measurement unit on the upper back, to identify eleven tactical movement patterns commonly performed by soldiers. Using a two-step logical algorithm, real-world constraints are forced, and class labels are expanded to 19 movements. Presented are three models based on Indoor, Section Attack (outdoors), and a General approach. Across all three approaches, we obtained an average accuracy of 90.0%. Further, we used these predictions to calculate meaningful tradespace metrics, which had an excellent agreement with calculations using the true labels. Military leaders and defence scientists can use this approach to quantify tradespace metrics in the field, as a preprocessing tool to supplement other technology, and make data-driven decisions that can help improve performance, decrease susceptibility, and increase overall mission success.