Performance Improvement Research Articles

Multi-apodization with cross-correlation combined with generalized sidelobe canceller applied to ultrasound imaging.

Beamforming is vital for medical ultrasound imaging systems. The generalized sidelobe canceller (GSC) beamforming can improve the image quality of lateral resolution, but its performance improvement in contrast and robustness is limited. This paper proposes an improved generalized sidelobe canceller algorithm based on multi-apodization with cross-correlation (MAXB-IGSC), which aims to improve the contrast and robustness of ultrasound imaging while maintaining the high image resolution and background speckle quality of GSC. The proposed MAXB-IGSC uses multiple pairs of complementary received apodization functions to process the echo data individually to obtain multiple pairs of data sets. The average of their normalized cross-correlation coefficients is then calculated and utilized to determine the adaptive subarray length of the GSC covariance matrix and weights the output of the improved GSC. The MAXB-IGSC improves the contrast ratio (CR) by 171.18% in anechoic cyst simulation and by 91.23%/130.97%/171.76% in geabr_0 (a dataset from the University of Michigan) experiment compared with GSC, respectively. Furthermore, MAXB-IGSC exhibits significant noise immunity, which greatly improves the robustness of the imaging. The technology also maintains the brightness and uniformity of the background speckle. The proposed MAXB-IGSC has potential for obtaining high-quality ultrasound images in clinical applications.

Enhancement of combustion, performance and emission characteristics of diesel engines fuelled with jatropha-karanja biodiesel using EGM and TGME as additive

The oxygenated additives can improve the physical properties of biodiesel blends to achieve enhanced characteristics of a diesel engine. The present work has investigated ethylene glycol monoacetate (EGM) and tri-ethylene glycol mono-methyl ether (TGME) as potential additives for the jatropha-karanja dual blended biodiesel for improved characteristics of diesel engines. The experiments have been conducted with a single cylinder four stroke diesel engine operating at constant speed of 1500 rpm. The performance, emission and combustion parameters of the engine were investigated with pure diesel, jatropha-karanja dual blended biodiesel having 20 % of methyl ester and the biodiesel added with 2, 4, and 6 % of both the additives respectively. The doping of additives improved the brake thermal efficiency by 4.1 and 4.7 % with brake specific fuel consumption dropped by 0.18 and 0.2 kg/kWh. The additives reduced the hydrocarbon emission by 29.8 % and carbon monoxide by 40 % with a slight increase in NOx emission by 5.2 %. The cylinder pressure attained value near to pure diesel while the heat release rate increased by 12.6 % indicating proper fuel combustion inside the engine cylinder. Thus, the oxygenated additives can suitably be adopted as performance improvers in biodiesel fuels for automotive engines.

Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution.

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

Characterization and Performance Improvement of Face-to-Face Double Stack REBCO Coated Conductors

Characterization and Performance Improvement of Face-to-Face Double Stack REBCO Coated Conductors

Comprehensive Performance Improvement of SRG by Turn-ON Angle Optimization Using Linear Normalized Model

Comprehensive Performance Improvement of SRG by Turn-ON Angle Optimization Using Linear Normalized Model

Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection.

Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer solutions. To alleviate this issue, this article suggests a new multiobjective multitasking evolutionary algorithm (MMTEA) with decomposition-based transfer selection, called MMTEA-DTS. In this algorithm, all tasks are first decomposed into a set of subproblems, and then the transfer potential of each solution can be quantified based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected to promote knowledge transfer. Moreover, to diversify the transfer of search experiences, a hybrid transfer evolution method is designed in this article. In this way, more diverse search experiences are transferred from high-potential solutions across different tasks to speed up their convergence. Three well-known benchmark suites suggested in the competition of evolutionary MTO and one real-world problem suite are used to verify the effectiveness of MMTEA-DTS. The experiments validate its advantages in solving most of the test problems when compared to five recently proposed MMTEAs.

Data Augmentation for Sample Efficient and Robust Document Ranking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this article, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.

Fake Voice Detection System

Fake Voice Detection System

Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model

ABSTRACT Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins.

Bioinspired Fluid Dynamic Designs of Vertical-Axis Turbines: State-of-the-Art Review and the Way Forward

Abstract With the increasing popularity of vertical axis turbines (VATs), researchers are now focusing on their performance improvement. Instead of adopting conventional means of performance improvements such as augmentation techniques and exhaustive parametric design optimization, the bio-inspired turbine designs have become a center of attraction, especially during the last decade. This review article attempts to compile the bio-inspired designs belonging to the VATs. Bio-inspired designs implemented in Savonius, Darrieus, Nautilus, and Seed-inspired turbines are elaborated besides giving a detailed explanation of the corresponding bio-organism and natural phenomenon. How the working principles of bio-organisms emulated in the form of fluid dynamic design are explained thoroughly in this paper. The bio-inspired designs for VATs are then classified pragmatically for the future designs. Research gaps are highlighted for the aspiring researchers, and this is followed by the important strategies and allied challenges.

A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability.

Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world. Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver's actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6. The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features. These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods.

The engagement chasm: Time for competency-based training in employee engagement for healthcare and beyond.

Employee disengagement in healthcare and business is currently at unexceptionally high levels worldwide. Disengagement negatively impacts productivity, profitability, efficiency (waste reduction), innovation, quality, customer satisfaction and experience, staff well-being, safety, mortality, staff attendance, and turnover. Despite its serious detrimental impacts, no dedicated competency-based training curriculum exists for engagement as a competency. To develop a competency-based educational curriculum for an Engagement Competency. A curricular roadmap comprising the following steps was observed 1. Identifying the desired outcomes needed of trainees. These must help fulfill all the Q12 Gallup survey engagement items. 2. Explicitly defining the required Competencies, Entrustable Professional Activities, and Milestones, 3. Selecting the educational activities, and instructional methods, 4. Selecting the tools to assess progress along the milestones, and finally, 5. Designing an evaluation system to assess the outcomes of the engagement competency program. We developed an Engagement Competency Framework with 7 Entrustable Professional activities "rationally and practically" arranged. These are: Envision E1, Embrace E2, Empower E3, Enlighten E4, Empathize E5, Energize E6, and Evaluate E7 (the 7Es). The unfortunate global issue of disengagement in healthcare and in the business arena may be practically tackled by introducing Engagement Competency and training. It should be compulsory for all in the "leadership role". Such training may lead to remarkable performance improvement and a happier, more prosperous, and safer world.

Enhancing Axial Flow Fan Performance in Air-Cooled Condensers: Tip Vortex Manipulators and Comparative Analysis of Numerical Simulation and Experimental Testing

Abstract Direct dry-cooled power plants typically operate numerous large-diameter axial flow fans in air-cooled condensers (ACCs) to facilitate the condensation of steam in the plant's thermodynamic cycle. Enhanced fan efficiency may, at scale, lead to significant parasitic power reductions and subsequent increases in plant power output. The performance of these fans may be increased by adding blade-tip modifications, which act to control blade-tip leakage flow. Previous studies have shown that manipulating the tip leakage vortex (TLV) increases the blade-to-air momentum transfer and stabilizes the air flow structures as it traverses the fan blade. This paper takes a step toward the development of improved tip vortex manipulators for an ACC fan. Three-dimensional computational fluid dynamics (CFD) simulations, and experimental testing of the modified and unmodified fan within an ISO test facility are performed. The results are then utilized to assess the accuracy of the simulation model, visualize the manipulated flow structures at the blade tip, and ultimately quantify the effect of the endplate designs on fan performance. Excellent correlation between numerical and test results for the unmodified fan is observed, and the benefit of blade-tip modification on fan performance is again confirmed experimentally. The simulation model, however, fails to predict improvements in fan performance under modification, and simulated tip leakage flows are investigated for clarification. It is hypothesized that deflection of tip endplates under operation account for differences between simulation and experiment, and that fluid–structure interaction analyses could potentially resolve discrepancies.

Synchronous Regulation of D-Band Centers in Zn Substrates and Weakening Pauli Repulsion of Zn Ions Using the Ascorbic Acid Additive for Reversible Zinc Anodes.

The advanced aqueous zinc-ion batteries (AZIBs) are still challenging due to the harmful reactions including hydrogen evolution and corrosion. Here, a natural small molecule acid vitamin C (Vc) as an aqueous electrolyte additive has been selectively identified. The small molecule Vc can adjust the d band center of Zn substrate which fixes the active H+ so that the hydrogen evolution reaction (HER) is restrained. Simultaneously, it could also fine-tune the solvation structure of Zn ions due to the enhanced electrostatics and reduced Pauli repulsion verified by energy decomposition analysis (EDA). Hence, the cell retains an ultra-long cycle performance of over 1300 cycles and a superior Coulombic efficiency (CE) of 99.5 %. The prepared full cells display increased rate capability, cycle lifetime, and self-discharge suppression. Our results shed light on the mechanistic principle of electrolyte additives on the performance improvement of ZIBs, which is anticipated to render a new round of studies.

Boosted Second Harmonic Generation of a Chiral Hybrid Lead Halide Resonant to Charge Transfer Exciton from Metal Halide Octahedra to Ligand.

Chiral hybrid organic-inorganic metal halides (HOMHs) offer an ideal platform for the advancement of second-order nonlinear optical (NLO) materials owing to their inherent noncentrosymmetric structures. The enhancement of optical nonlinearity of chiral HOMHs could be achieved by matching the free exciton and/or self-trapped exciton energy levels with desired NLO frequencies. However, the current scarcity of resonance modes and low resonance ratio hamper the further improvements of NLO performance. Herein, we propose a new resonant channel of charge transfer (CT) excited states from metal halide polyhedra to organic ligand to boost the second-order optical nonlinearity of chiral HOMHs. The model lead halide (C7H10N)PbBr3 (C7H10N=1-ethylpyridinium) exhibits a drastically enhanced second harmonic generation in resonance to the deep CT exciton energy, with intensity of up to 111.0 times that of KDP and 10.9 times that of urea. The effective NLO coefficient has been determined to be as high as ~40.2 pm V-1, balanced with a large polarization ratio and high laser damage threshold. This work highlights the contribution of organic ligands in the construction of a resonant channel for enhancing second-order NLO coefficients of metal halides, and thus provides guidelines for designing new chiral HOMHs materials for advanced nonlinear photonic applications.

The Contribution of Lower-Body Kinematics to Pitching and Hitting Performance in Baseball: Utilizing the OpenBiomechanics Project.

As markerless motion capture systems become more affordable than ever, it is becoming far easier to collect and analyze kinematic data on baseballers. To ensure this data can be used impactfully, coaches and practitioners should possess a good understanding of specific technique characteristics that are associated with enhanced performance in pitchers and hitters. This study used the open-source data provided by Driveline Baseball's OpenBiomechanics Project to evaluate the contribution of lower-body kinematics to pitch velocity and bat speed. In addition to correlational analyses to examine the association between discrete kinematic variables and performance, statistical nonparametric mapping was used to compare slow and fast velocity groups across the entirety of pitching and hitting motions from peak knee height to ball release/contact. It was found that rotation of the trail leg and extension of the lead knee were both associated with performance in pitchers and hitters. Consequently, coaches and practitioners should ensure that individuals possess an adequate level of strength and flexibility to facilitate optimal movement of the trail hip and lead knee during pitching and hitting movements. If deficiencies exist, then training programs should be designed to address these issues and eventually bring about an improvement in performance.

LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semisupervised Classification.

Semisupervised learning (SSL) has received a lot of recent attention as it alleviates the need for large amounts of labeled data which can often be expensive, requires expert knowledge, and be time consuming to collect. Recent developments in deep semisupervised classification have reached unprecedented performance and the gap between supervised and SSL is ever-decreasing. This improvement in performance has been based on the inclusion of numerous technical tricks, strong augmentation techniques, and costly optimization schemes with multiterm loss functions. We propose a new framework, LaplaceNet, for deep semisupervised classification that has a greatly reduced model complexity. We utilize a hybrid approach where pseudolabels are produced by minimizing the Laplacian energy on a graph. These pseudolabels are then used to iteratively train a neural-network backbone. Our model outperforms state-of-the-art methods for deep semisupervised classification, over several benchmark datasets. Furthermore, we consider the application of strong augmentations to neural networks theoretically and justify the use of a multisampling approach for SSL. We demonstrate, through rigorous experimentation, that a multisampling augmentation approach improves generalization and reduces the sensitivity of the network to augmentation.

Ensemble of Domain Adaptation-Based Knowledge Transfer for Evolutionary Multitasking

Recently, a number of domain adaptation methods have been proposed for knowledge transfer in evolutionary multitasking (EMT). However, the learnt mappings in these methods often have unique biases in representing the connection between source and target tasks. Few studies have paid attention to the complementarity of different mappings in knowledge transfer. To fill this research gap, this paper proposes an ensemble method to combine multiple domain adaptation methods for knowledge transfer in EMT by considering the efficacy and diversity of these methods. First, a hierarchical clustering method is used to divide the population of each task into multiple clusters. Then, when two parental solutions are selected for knowledge transfer across tasks, the solutions within the same cluster are checked. In particular, if none of these solutions has been transferred before, the efficacy of domain adaptation methods is considered first by using roulette wheel selection based on the corresponding performance improvements in the evolutionary optimization process. Otherwise, the diversity of domain adaptation methods is emphasized by randomly selecting one of the domain adaptation methods for knowledge transfer. The effectiveness of our proposed ensemble method is validated by embedding it into existing state-of-the-art EMT algorithms, and the experimental results show that our algorithm outperforms several recently proposed EMT algorithms on most cases of two multitasking benchmark suites and one practical case.

Leveraging Adversarial Augmentation on Imbalance Data for Online Trading Fraud Detection

Nowadays, the emergence of online trading greatly facilitates people’s life. Meanwhile, online trading also brings hidden dangers, such as online fraudulent trading. To solve the issue, researchers have proposed many different detection models. However, in actual business scenarios, fraudulent transactions usually only account for a small portion of normal transactions, resulting in extremely imbalanced data. Besides, the concealment of fraud is reflected in that the fraudsters are imitating the normal transactions of users, posing a huge challenge for fraudulent transaction detection modeling. Inspired by generative adversarial networks (GANs), we propose a GAN-based framework to detect online banking fraud on extremely imbalanced data, called BalanceGAN. A fraud detection model is first pretrained using the data generated by the generator and then the model is fine-tuned using transfer learning on real-world datasets, by using this approach to address data imbalances. Compared with the conventional methods for solving imbalanced data, our BalanceGAN can avoid over-fitting of the model relatively, experiments on two real datasets show that our BalanceGAN has more than 10% performance improvement in Precision and Recall.