Vector Selection Research Articles

Analysis and classification of arrhythmia types using improved firefly optimization algorithm and autoencoder model

In the present scenario, Electrocardiogram (ECG) is an effective non-invasive clinical tool, which reveals the functionality and rhythm of the heart. The non-stationary nature of ECG signal, noise existence, and heartbeat abnormality makes it difficult for clinicians to diagnose arrhythmia. The most of the existing models concentrate only on classification accuracy. In this manuscript, an automated model is introduced that concentrates on arrhythmia type classification using ECG signals, and also focuses on computational complexity and time. After collecting the signals from the MIT-BIH database, the signal transformation and decomposition are performed by Multiscale Local Polynomial Transform (MLPT) and Ensemble Empirical Mode Decomposition (EEMD). The decomposed ECG signals are given to the feature extraction phase for extracting features. The feature extraction phase includes six techniques: standard deviation, zero crossing rate, mean curve length, Hjorth parameters, mean Teager energy, and log energy entropy. Next, the feature dimensionality reduction and arrhythmia classification are performed utilizing the improved Firefly Optimization Algorithm and autoencoder. The selection of optimal feature vectors by the improved Firefly Optimization Algorithm reduces the computational complexity to linear and consumes computational time of 18.23 seconds. The improved Firefly Optimization Algorithm and autoencoder model achieved 98.96% of accuracy in the arrhythmia type classification, which is higher than the comparative models.

A Simplified Virtual-Vector-Based Model Predictive Control Technique With a Control Factor for Three-Phase SPMSM Drives

Compared with the single vector-based model predictive control (MPC) technique, the multiple vector-based MPC technique gains interests in improving the total harmonic distortion (THD) of the stator currents and reducing electromagnetic torque and stator flux ripples for a three-phase surface permanent magnet synchronous motor (SPMSM). However, the complicated enumeration and operation in vector selection and duty cycle calculation increase the computational burden and limit its peneration. Hence, in this paper, a simplified virtual vector-based MPC with a control factor for a three-phase SPMSM is proposed. Without sector identification and enumeration operations, the proposed technique utilizes the control factor to automatically generate a large number of virtual vectors to achieve equivalent performance effectiveness with multiple vector-based MPC technique with lower sampling frequency. Subsequently, regardless of the number of virtual vectors, the candidate virtual vectors are directly identified in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> coordinate system to greatly reduce the computational burden. In addition, the duration of the selected optimal virtual vector and the resultant switching sequences are easily obtained and arranged to even the switching losses among the switching devices and further optimize the THD of the stator current. The proposed technique is simplified and flexible. Its control effectiveness is verified by experimental results.

The simulation analysis of stator flux droop minimization in direct torque control open-end winding induction machine

&lt;span lang="EN-US"&gt;Direct torque control (DTC) using dual-inverter technique is one of the best topologies for electric vehicle (EV) as it offers abundant selection of voltage vectors to drive the induction machine (IM). This dual-inverter technique also more reassuring as the system still workable even any of its voltage supply is disrupted or the power pack is drained. However, during the uneven voltage supply, the movement of voltage vectors is interrupted and will move obliquely especially in medium voltage vectors. This situation will lead to the faulty movement of the voltage vectors in the default sector definitions and lead to huge flux droop, which later could impose to distort phase current. This paper proposes an optimal sector definition based on the preset voltage ratio between the two inverters. The voltage vectors can be mapped tangentially to the flux vector, minimizing the flux droop and improving the phase current waveform when the proposed sector is utilized. The effectiveness of the proposed sector is tested using MATLAB/Simulink software and the exact parameter from the induction machine.&lt;/span&gt;

Optimal Switching Sequence Model Predictive Control for Modular Multilevel Converter

This paper proposes a new optimal switching sequence model predictive control method for modular multilevel converter (MMC). In this method, the common mode voltage (CMV) is cosidered, which makes the model more accurate. The proposed method realizes the optimization of multiple control objectives of output current tracking, circulating current suppression (CCS), and submodule voltage balancing. The sequence that allows the output current to track its reference value is calculated in the <inline-formula><tex-math notation="LaTeX">$\alpha -\beta$</tex-math></inline-formula> coordinate system. And the virtual vectors aim to suppress the circulating current without influencing the output current are inserted. Capacitor voltage balancing is achieved by redundant vectors selection and tranditional selection algorithm. These control objectives are calculated independently, thus weighting factor is not required. At the same time, the computation burden is reduced by restricting the voltage mutation. This method has a satisfactory dynamic response and steady-state performance, the simulation and experimental results are given to verify the effectiveness of the method.

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-Massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide good directional measurement results, the rapid power iterative polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with the polynomial solution method to obtain the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPI-PR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to the Cramér–Rao Lower Bound (CRLB). Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance with respect to the computational complexity.

A tertiary centre experience of routine exercise testing as part of subcutaneous ICD vector mapping

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The pre-implant patient screening process (vector mapping) for subcutaneous defibrillators (S-ICD) seeks to determine eligibility for implantation. It broadly requires at least one of three available vectors (primary, secondary or alternate) to pass under all conditions to mitigate risk of inappropriate device therapies. Vector mapping protocols currently require only supine and sitting/standing ECG. However, the standard operating procedure (SOP) in our centre also includes mapping at peak exercise, actioned after audit data highlighted an association between exercise and sensing complications (T and R wave changes, rate-related bundle branch block and noise/myopotential over-sensing). Aim To determine whether routine exercise testing during screening alters the outcome of vector mapping, and whether this correlates with inappropriate therapies post-implant. Method This was a single-centre retrospective study of 121 patients (79% male, mean BMI 29) who completed S-ICD vector mapping between October 2016 and August 2022. 10 patients were excluded as they were unable to tolerate exercise. Emblem automated screening tool (AST) and AST 2.0 was used to complete vector mapping and a revised SOP was devised in conjunction with their technical support team. Post- implant vector programming was based on device optimisation algorithms rather than exercise-inclusive screening recommendation, which provided additional observational data only. Results 75% were primary prevention devices and the 3 most prevalent aetiologies were dilated (22%), ischaemic (20%) and hypertrophic (18%) cardiomyopathies. In 29.7% (33/111) of participant’s, inclusion of exercise screening altered vector mapping success: The alternate vector saw the largest decrease (23.2%) in successful mapping once exercise was incorporated (69 participants passed supine and sitting/standing, 16 of those failed post-exercise), followed by secondary 13.1% (13/99) then primary 3.9% (4/103) – see Figure 1. Further retrospective analysis showed that in 16.2% (18/111) of patients, post-implant vector selection opposed screening recommendation. 6 of these patients experienced exercise-induced sensing complications with 4 leading to inappropriate shocks. After reprogramming in-line with screening, all but 1 patient have had no further issues. Conclusions Incorporation of exercise mapping into S-ICD screening protocols may improve vector selection to reduce inappropriate device therapies.

Every little bit helps: A single-residue switch in a vascular AAV enables blood-brain barrier penetration

Every little bit helps: A single-residue switch in a vascular AAV enables blood-brain barrier penetration

Impact of Space Vector Selection and Input Displacement Angle on Switch Voltage Stress of Current Source Rectifier

Three-phase current source rectifier (CSR) is concerned due to its flexible output voltage regulation, high reliability and fast dynamic response. Excessive voltage stress on the switch will increase the cost of the rectifier and reduce the reliability of the system. Therefore, it is essential to optimize the voltage stress of the switch. In this letter, the voltage stress of switch caused by each current vector in space vector modulation (SVM) is analyzed. Then, the vectors with low voltage stress are selected for combination to reduce the voltage stress of the switch without changing the topology. In addition, the vectors are re-optimized considering the effects of displacement angle. The re-optimized SVM can reduce the voltage stress by up to 13% under the leading or lagging power factor operation. Furthermore, the dc-link current ripple and modulation index range of different modulation methods are compared, and the effectiveness of the proposed modulation schemes are verified by experiments.

Hybrid space vector modulation for low‐order common‐mode voltage reduction in current‐source rectifier for transformerless medium voltage drives

AbstractFor transformerless current‐fed medium voltage drives, low‐order harmonics of common‐mode voltages (CMVs) are more concerned with than peak values. To mitigate low‐order harmonics, the average values of CMVs should be suppressed. A hybrid space vector modulation (SVM) is proposed in this study to further reduce CMV average values generated by a current‐source rectifier (CSR) compared to existing zero‐state vector selection modulation. The proposed SVM is a combination of zero‐state vector selection and active zero‐state modulations. The transition points between two modulations are defined based on various modulating index. Power losses and efficiency of the proposed hybrid SVM are analysed in detail. Simulation on a medium‐voltage CSR (1 MW/4160 V/139 A) and experiments on a scale‐down CSR (2.5 kW/208 V/6.94 A) are conducted under various working conditions, which validates the effectiveness of the proposed SVM to further mitigate 180 Hz and other low‐order harmonics in CMVs in contrast to that by conventional zero‐state vector selection modulation.

Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

AbstractCritical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

Common Mode Voltage Minimization for Generalized Dual Inverter-Fed Aircraft Starter/Generator: Realization and Sub-Load Asymmetry Influence Analysis

In multi-stages aircraft starter/generator system, the main machine (MM) and main exciter (ME) are fed by a generalized dual-inverter configuration, allowing for common-mode voltage (CMV) elimination. However, the distinction in load impedances and voltage requirements between the two sub-inverters has evidently contributed to the sub-load asymmetry, which may impair the suppression effect of system common mode (CM) noises. This paper presents a CMV minimization method for the generalized dual-inverter application. The available freedom, such as the selection and action sequence of voltage vectors, is appropriately arranged in the suggested implementation to eliminate the total CMV, restrict the sub-CMVs and fulfill the voltage requirements of sub-inverters all at the same time. In the meanwhile, a theoretical study is performed to evaluate the CM noise features of zero CMV technique in the generalized dual inverter-fed system. In the situation of sub-load asymmetry, the premise and main influence element for CM noise reduction are assessed. Finally, these findings are verified by simulation and experiments.

A Time-Frequency Domain Detection Method for Measurement Data of Non-Stationary Signals Based on Optimized Hilbert-Huang Transform

Aiming at the phenomenon of mode mixing and information redundancy when Hilbert-Huang transform (HHT) is used for non-stationary signal measurement data processing, an optimized HHT algorithm is proposed in the study. The processing effect is improved by setting complementary ensemble empirical mode decomposition instead of empirical mode decomposition, using a frequency-domain smoothing vector to smooth and marginal spectrum feedback to optimize the time-frequency spectrum. The optimized algorithm is applied to the measurement data processing of acoustic signals of Penaeus vannamei. The duration, the range of the frequency, and the relative intensity of the frequency within 0~24 kHz of the signals are obtained. Mean-while, the optimized time-frequency spectrums obtained by processing the signals and the distribution diagrams of the number of key information points obtained under different smoothing vectors and feedback times prove that the optimized performance of the algorithm is affected by the signal quality and the selection of smoothing vectors. Besides, the primary and secondary feedback results need to be integrated when extracting signal features.

PD19-02 INVOLVEMENT OF HIPPO/TAZ PATHWAY IN METABOLIC ALTERATIONS DURING RENAL FIBROSIS

You have accessJournal of UrologyCME1 Apr 2023PD19-02 INVOLVEMENT OF HIPPO/TAZ PATHWAY IN METABOLIC ALTERATIONS DURING RENAL FIBROSIS Derek Friedman and Rohan Samarakoon Derek FriedmanDerek Friedman More articles by this author and Rohan SamarakoonRohan Samarakoon More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003285.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Chronic Kidney Disease (CKD) affects 10-15% of the world population, and 30-40 million people in the United States alone. Medicare costs for patients with CKD in 2017 were $84 billion. There are no effective medications available to treat renal fibrosis, which leads to CKD. Current renal replacement therapies, dialysis or transplantation, are costly and are a lifelong detriment to patient quality of life. The goal of this research is to elucidate biochemical pathways involved in the metabolic alterations seen in renal fibrosis in order to identify potential therapeutic target molecules. METHODS: Lentiviral infection with TAZ vector and stable selection was used to create HK-2 renal epithelial cells that mimic the TAZ upregulation seen in renal fibrosis. Western blot analysis was then used to identify changes in metabolic enzymes in TAZ overexpressed cells compared to controls. Western blot was also used to analyze the effect of TAZ overexpression on Rac-1 activity, and how the pharmacologic inhibition of Rac-1 with EHT1684 affects TAZ driven metabolic changes. RESULTS: TAZ overexpression in renal epithelial cells leads to upregulation of enzymes involved in the non-reversible steps of glycolysis including Hexokinase, Phosphofructokinase, and Pyruvate Kinase. TAZ overexpression was also associated with increased levels of the constitutively active Rac-1 isoform, Rac-1b. The TAZ driven upregulation of fibrotic markers including Fibronectin, Collagen-1, and CTGF was attenuated by the pharmacologic inhibition of Rac-1 with EHT1684. Rac-1 inhibition with EHT1684 also attenuated TAZ-driven glycolytic reprogramming of renal epithelial cells. CONCLUSIONS: Renal tubular TAZ expression promotes fibrotic and glycolytic reprogramming, as well as the activation of Rac. Pharmacological inhibition of Rac expression attenuates glycolytic enzyme expression and fibrotic phenotype. TAZ and Rac may both prove to be novel targets for therapeutic intervention in CKD. Source of Funding: This research received no specific grant funding © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e577 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Derek Friedman More articles by this author Rohan Samarakoon More articles by this author Expand All Advertisement PDF downloadLoading ...

Restraint of Common-Mode Voltage for PMSM-Inverter Systems With Current Ripple Constraint Based on Voltage-Vector MPC

At present, the common-mode voltage (CMV) interference is a general problem in motor drive system with three-phase voltage source inverters. When the zero vector is applied, the inverter will generate high CMV, which will bring some negative effects to permanent magnet synchronous motor. Removing the zero vector directly can suppress CMV, but will lead to large current ripple. Aiming to balance these two factors, this article proposes a new method based on finite control set model predictive control (MPC) to suppress the CMV and ensure the high quality of the operating current. The method divides the traditional two-level space voltage vector area into active vector selection area and zero vector selection area, and the tradeoff between CMV suppression and current ripple can be realized by adjusting the size of each selection area reasonably. On this basis, a fast-double vector MPC (FDV-MPC) is proposed to further reduce the current ripple. FDV-MPC directly selects the optimal double vectors without the optimization process, which greatly reduces the computation of the algorithm. The influence of CMV generated by the FDV-MPC during dead-time is analyzed. Finally, the experimental results verify the effectiveness of FDV-MPC and new CMV suppression method.

Predictive discrete‐time sliding mode based direct power control for grid‐connected energy conversion systems

AbstractA method for digital direct power control design for grid‐connected energy conversion systems was proposed in this paper. The active and reactive powers are directly controlled with constant switching frequency by selecting an appropriate switching control sequence of voltage source converter as a key part of energy conversion systems. The control design combines a discrete‐time sliding mode control and predictive control. The selection of feasible switching control vectors is a direct result of the discrete‐time sliding mode control approach. These vectors satisfy the existence conditions of the sliding mode, but each of them induces system motion with different properties. The task of predictive control is to select an appropriate switching control sequence to reduce the chattering phenomenon, steady‐state error, and overshoot. The cost function of the prediction process is defined as the average value of both power errors on the prediction horizon of three sampling periods. Simulation results, experimental results, and results of a comparative analysis are presented in order to show the performance and effectiveness of the proposed control design.

Development of a patients’ satisfaction analysis system using machine learning and lexicon-based methods

BackgroundPatients’ rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients’ messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients’ messages.MethodsThe level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency–inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients’ messages, was implemented by the lexicon-based method.ResultsThe best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared.ConclusionOur results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients’ comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients’ satisfaction in different wards and to remove conventional assessments by the evaluator.

TS-LSSVM: Triple sparse least squares support vector machine for residual oxygen concentration detection of encapsulated pharmaceutical vials

Accurate measurement of residual oxygen concentration in encapsulated pharmaceutical vials is adequate to ensure the quality of inner sterile preparations. However, the critical characteristic signal is feeble and covered by enormous environmental interference in the actual production. Inspired by structural sparse learning, we propose a novel prediction model in this paper, triple sparse least squares support vector machine (TS-LSSVM), in which the production priors are deeply excavated, and the feature, sample, and structure sparsity are realized simultaneously by redefining the objective function. In addition, the selection of support vectors can be adjusted adaptively according to the time-varying environmental noise, so as to ensure the reliability of long-term operation. First, the time–frequency components containing the prior knowledge are extracted based on the synchro squeezing wavelet transform (SSWT). Then, a triple sparse learning strategy is designed, which can accurately eliminate redundant wavelet coefficients and adaptively select training samples. Finally, a strict and fast optimization solution is proposed under the alternating direction method of multipliers (ADMM) framework. Experimental results on public and practical datasets prove the superiority of TS-LSSVM.

A general averaging method for count data with overdispersion and/or excess zeros in biomedicine.

With the aim of providing better estimation for count data with overdispersion and/or excess zeros, we develop a novel estimation method-optimal weighting based on cross-validation-for the zero-inflated negative binomial model, where the Poisson, negative binomial, and zero-inflated Poisson models are all included as its special cases. To facilitate the selection of the optimal weight vector, a -fold cross-validation technique is adopted. Unlike the jackknife model averaging discussed in Hansen and Racine (2012), the proposed method deletes one group of observations rather than only one observation to enhance the computational efficiency. Furthermore, we also theoretically prove the asymptotic optimality of the newly developed optimal weighting based on cross-validation method. Simulation studies and three empirical applications indicate the superiority of the presented optimal weighting based on cross-validation method when compared with the three commonly used information-based model selection methods and their model averaging counterparts.

Correlation analysis of deep learning methods in S-ICD screening.

Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study explored the potential use of deep learning methods in S-ICD screening. This was a retrospective study. A deep learning tool was used to provide descriptive analysis of the T:R ratios over 24 h recordings of S-ICD vectors. Spearman's rank correlation test was used to compare the results statistically to those of a "gold standard" S-ICD simulator. A total of 14 patients (mean age: 63.7± 5.2 years, 71.4% male) were recruited and 28 vectors were analyzed. Mean T:R, standard deviation of T:R, and favorable ratio time (FVR)-a new concept introduced in this study-for all vectors combined were 0.21 ± 0.11, 0.08 ± 0.04, and 79 ± 30%, respectively. There were statistically significant strong correlations between the outcomes of our novel tool and the S-ICD simulator (p< .001). Deep learning methods could provide a practical software solution to analyze data acquired for longer durations than current S-ICD screening practices. This could help select patients better suited for S-ICD therapy as well as guide vector selection in S-ICD eligible patients. Further work is needed before this could be translated into clinical practice.

Research on Optimization SVPWM Method of Nine-switch Inverter for Bearingless Motor Based on Reducing Switching Times

The traditional bearingless motor system controlled by two three-phase inverters has a high cost and volume. In this paper, the nine-switch converter is applied to the bearingless motor control system, which can control the torque winding and suspension winding of the bearingless motor at the same time to save three MOSFETs. This paper proposes a nine-switch inverter SVPWM method to reduce switching loss. This method is no longer limited to only considering the action sequence of voltage vectors but reduces the switching number from selecting voltage vectors. The optimal nine-switch inverter basic voltage vector selection principle is determined by comparing the switching times of each sector combination with different voltage vector combinations. Finally, the simulation results verify the effectiveness of this method.