Performance Improvement Research Articles

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.

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

Fake Voice Detection System

This research presents an advanced Voice Detection System tailored for cutting-edge audio analytics applications. Utilizing a fusion of artificial intelligence and intricate algorithms, the system excels in recognizing a diverse range of voice characteristics and subtleties. Its architecture integrates machine learning for continuous improvement and acoustic signal processing for robust performance in varying environments. The system's adaptability and scalability make it compatible with diverse infrastructures, promising seamless integration and expansion. This paper offers a detailed exploration of the system's design, theoretical foundations, and real-world applications. Aimed at stakeholders in security and communication sectors, it serves as a comprehensive guide to understanding the potential impact and implementation strategies of this innovative technology.

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.

Discrete Morse Sandwich: Fast Computation of Persistence Diagrams for Scalar Data - An Algorithm and A Benchmark.

This paper introduces an efficient algorithm for persistence diagram computation, given an input piecewise linear scalar field f defined on a d-dimensional simplicial complex K, with d ≤ 3. Our work revisits the seminal algorithm "PairSimplices" [31], [103] with discrete Morse theory (DMT) [34], [80], which greatly reduces the number of input simplices to consider. Further, we also extend to DMT and accelerate the stratification strategy described in "PairSimplices" [31], [103] for the fast computation of the 0th and (d-1)th diagrams, noted D0(f) and Dd-1(f). Minima-saddle persistence pairs ( D0(f)) and saddle-maximum persistence pairs ( Dd-1(f)) are efficiently computed by processing , with a Union-Find , the unstable sets of 1-saddles and the stable sets of (d-1)-saddles. We provide a detailed description of the (optional) handling of the boundary component of K when processing (d-1)-saddles. This fast pre-computation for the dimensions 0 and (d-1) enables an aggressive specialization of [4] to the 3D case, which results in a drastic reduction of the number of input simplices for the computation of D1(f), the intermediate layer of the sandwich. Finally, we document several performance improvements via shared-memory parallelism. We provide an open-source implementation of our algorithm for reproducibility purposes. We also contribute a reproducible benchmark package, which exploits three-dimensional data from a public repository and compares our algorithm to a variety of publicly available implementations. Extensive experiments indicate that our algorithm improves by two orders of magnitude the time performance of the seminal "PairSimplices" algorithm it extends. Moreover, it also improves memory footprint and time performance over a selection of 14 competing approaches, with a substantial gain over the fastest available approaches, while producing a strictly identical output. We illustrate the utility of our contributions with an application to the fast and robust extraction of persistent 1-dimensional generators on surfaces, volume data and high-dimensional point clouds.

Cache Update and Delivery of Dynamic Contents: A Stochastic Game Approach

In this paper, we propose a cache update policy for wireless networks considering dynamic popularity for file requests. Our network scenario is a wireless network comprised of some cache-equipped access points (APs) which are deployed densely in an area and have connections with core network servers. We model the dynamics of file requests as Markov-modulated Poisson processes. Considering the congestion-dependent delay of APs' services and their overlapping coverage areas, we model the cache service for the cache-missed requests, as a stochastic game in which a Vickrey–Clarke–Groves (VCG) mechanism is exploited at each stage of the game as our proposed file routing policy, and the APs decide independently how to update their caches regarding the routed files. To this end, the APs' utilities are defined based on long-term average file delivery delay including the queueing delay in APs. By finding the Nash equilibrium of the game, we propose policies for both delivery and cache update of dynamic contents. Finally, by comparing numerical results of our proposed scheme with some conventional caching schemes, we show significant improvements in network performance in terms of file delivery delay in different conditions.

Ambulatory Long Block: A Model of Precision Education and Assessment for Internal Medicine Residents.

High-quality precision education (PE) aims to enhance outcomes for learners and society by incorporating longitudinal data and analytics to shape personalized learning strategies. However, existing educational data collection methods often suffer from fragmentation, leading to gaps in understanding learner and program performance. In this article, the authors present a novel approach to PE at the University of Cincinnati, focusing on the Ambulatory Long Block, a year-long continuous ambulatory group-practice experience. Over the last 17 years, the Ambulatory Long Block has evolved into a sophisticated data collection and analysis system that integrates feedback from various stakeholders, as well as learner self-assessment, electronic health record utilization information, and clinical throughput metrics. The authors detail their approach to data prioritization, collection, analysis, visualization, and feedback, providing a practical example of PE in action. This model has been associated with improvements in both learner performance and patient care outcomes. The authors also highlight the potential for real-time data review through automation and emphasize the importance of collaboration in advancing PE. Generalizable principles include designing learning environments with continuity as a central feature, gathering both quantitative and qualitative performance data from interprofessional assessors, using this information to supplement traditional workplace-based assessments, and pairing it with self-assessments. The authors advocate for criterion referencing over normative comparisons, using user-friendly data visualizations, and employing tailored coaching strategies for individual learners. The Ambulatory Long Block model underscores the potential of PE to drive improvements in medical education and health care outcomes.

Few-Shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings

Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance toward new targets for which the model has only seen a few relevant samples during training. To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce cross-target text-net (CT-TN), a novel model that aggregates multimodal embeddings derived from both textual and network features of the data. We conduct experiments in a few-shot cross-target scenario on six different combinations of source-destination target pairs. By comparing CT-TN with state-of-the-art cross-target stance detection models, we demonstrate the effectiveness of our model by achieving average performance improvements ranging from 11% to 21% across different baseline models. Experiments with different numbers of shots show that CT-TN can outperform other models after seeing 300 instances of the destination target. Further, ablation experiments demonstrate the positive contribution of each of the components of CT-TN toward the final performance. We further analyze the network interactions between social media users, which reveal the potential of using social features for cross-target stance detection.

Mutual coupling reduction between antennas array for 5G mobile applications

This paper introduces the design of a multiple-input-multiple-output (MIMO) antenna optimized for low-profile applications supporting sub-6 GHz fifth-generation (5G) wireless applications. We have started the design from a single antenna with a square patch shape, each antenna array is composed from 4-element radiators fed by using power dividers and quarter microstrip lines. Mounted on a single Rogers RT5880 substrate, the MIMO antenna functions at 3.5 GHz. In order to miniature and to decrease the mutual coupling between the both antennas array we have optimised a magnetic wall based on periodic structures permitting to decrease the mutual coupling between the both antenna array. The unit element from the wall was optimised, studied and validated in order to absorb the surface current and to enhance the isolation between the different radiating elements. The dimensions of the proposed MIMO antenna are 154×220×0.578 mm³. The MIMO antenna final circuit achieves a peak gain of 9 dBi and an isolation around -30 dB. The introduction of the magnetic wall permits to enhance the isolation between the antenna array from -20 dB to -30 dB at 3.5 GHz band. This advancement contributes to the overall performance improvement of the MIMO antenna system.

WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks.

Network representation learning, also known as network embedding, aims to learn the low-dimensional representations of vertices while capturing and preserving the network structure. For real-world networks, the edges that represent some important relationships between the vertices of a network may be missed and may result in degenerated performance. The existing methods usually treat missing edges as negative samples, thereby ignoring the true connections between two vertices in a network. To capture the true network structure effectively, we propose a novel network representation learning method called WalkGAN, where random walk scheme and generative adversarial networks (GAN) are incorporated into a network embedding framework. Specifically, WalkGAN leverages GAN to generate the synthetic sequences of the vertices that sufficiently simulate random walk on a network and further learn vertex representations from these vertex sequences. Thus, the unobserved links between the vertices are inferred with high probability instead of treating them as nonexistence. Experimental results on the benchmark network datasets demonstrate that WalkGAN achieves significant performance improvements for vertex classification, link prediction, and visualization tasks.

Smooth Low-Speed Sensorless Control of Switched Reluctance Machine With Heavy Load

For the switched reluctance machine (SRM) with heavy load, the inductance of the conduction phase is nonlinear, which makes the sensorless control method based on the linear region of the phase inductance no longer applicable. Meanwhile, in the case of light or no load, the estimated rotor position will abruptly change at the connection points between neighboring segments, which has an impact on the estimation of the speed. In this paper, a low-speed sensorless control method for the SRM with heavy load is proposed. The idle-phase inductance is modeled over a wide rotor position range for position estimation, and a proportional gain decreasing type second-order generalized integrator based on a frequency-locked loop (SOGI-FLL) is constructed to estimate the speed while performing rotor position filtering. The effectiveness of the proposed method is experimentally verified with a 3-phase 12/8-pole SRM prototype, and the results show that the estimated rotor position and speed are accurate and smooth, which is beneficial to performance improvement of the sensorless control.

Linguistic Models: Optimization With the Use of Conditional Fuzzy C-Means

Most fuzzy models are just numeric. In this study, we revisit, explore and augment a concept of linguistic models, viz., fuzzy models producing results that are information granules, and, specifically, intervals or fuzzy sets. The proposed architecture is formed by constructing a network of linked fuzzy sets (information granules) ininput and output spaces with the aid of a context-based Fuzzy C-Means clustering method. The user centricity of such clustering method is implied by the explicit formulation of fuzzy sets in the output space. The resulting information granules constructed in the input space are conditioned by the corresponding fuzzy sets in the output space. This arrangement can increase the interpretability of the model and represent the model as a collection of logically arranged associations among information granules. The model's overall design process is discussed along with a detailed algorithmic structure. Its experimental evaluations are provided by using both synthetic and publicly datasets. For the former, the model brings the performance improvement ranging from 91% to 250% over the models with information granules uniformly distributed in output space. For the latter, such improvement ranges from 6% to 94%. Finally, a thorough discussion is provided together with guidelines on how to develop such a linguistic model in different contexts.

Analysis and Comparison of Machine Learning Techniques for DDoS Attack Classification in Network Environments

This research presents a comparative analysis of machine learning techniques for classifying Distributed Denial of Service (DDoS) attacks within network traffic. We evaluated the performance of three algorithms: Logistic Regression, Decision Tree, and Random Forest, including their scaled-feature counterparts. The study utilized a robust methodology incorporating advanced data preprocessing, feature engineering, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The models were rigorously tested using a cross-validation framework, assessing their accuracy, precision, recall, and F1 score. Results indicated that the Random Forest algorithm outperformed the others, demonstrating superior predictive accuracy and consistency, albeit with higher computational costs. Logistic Regression, when feature-scaled, showed significant improvement in performance, highlighting the importance of data normalization in models sensitive to feature scaling. Decision Trees provided a quick and interpretable model, though slightly less accurate than the Random Forest. The research findings highlight the trade-offs between predictive performance and computational efficiency in selecting machine learning models for cybersecurity applications. The study contributes to the cybersecurity domain by elucidating the efficacy of ensemble techniques in DDoS attack classification and underscores the potential for model improvement through scaling and data balancing.