Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Xianjing Lin, Wenyong Liu, Zuhao Jiang et al.

This paper presents a compact broadband circularly polarized (CP) filtering patch antenna with multi-element coupling structure. The antenna consists of two substrate layers. The upper substrate layer incorporates a main radiating patch with an etched rectangular slot, a pair of unequal-sized L-shaped parasitic stubs placed along the + 45&#x00B0; diagonal of the main patch, and surrounding microstrip patches. The lower substrate features a ground plane etched with an asymmetric U-shaped slot on its top surface and a microstrip feedline on the bottom. The signal enters the port, propagates along the microstrip feedline, and couples to the main radiating patch through the asymmetric U-shaped slot, generating CP radiation. Further coupling between the main patch and the surrounding microstrip patches produces two additional CP modes, enabling wideband CP operation. Without the need for additional filtering circuits, the antenna achieves bandpass filtering characteristics by introducing two controllable radiation nulls near 2.0 GHz and its second harmonic at 4.0 GHz through the unequal L-shaped parasitic stubs. A third radiation null near 3.6 GHz is generated by the rectangular slot etched on the main patch. The proposed design maintains a compact profile (<inline-formula> <tex-math notation="LaTeX">$0.54 \lambda_0 \times 0.54 \lambda_0 \times 0.07 \lambda_0$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> corresponds to the lowest operating frequency) while realizing integrated wideband CP and filtering performance. The measured results demonstrate an impedance bandwidth of 23.1% (2.64&#x2013;3.33 GHz), an axial ratio (AR) bandwidth of 11% (2.8&#x2013;3.125 GHz) and a stable in-band gain of approximately 7 dB. The out-of-band suppression levels reach 17 dB and 24 dB in the lower and upper stopbands, respectively, confirming good CP filtering performance.

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh et al.

Bearing faults are a critical concern in electrical machines, particularly permanent magnet synchronous motors (PMSMs), commonly used in electric vehicles. Early and accurate classification of bearing fault severity is essential for predictive maintenance, as it enhances cost-effectiveness, ensures safety, and extends product life. Although vibration-based monitoring offers rich diagnostic information, it remains costly and requires excess modifications. In contrast, current-based non-invasive techniques offer advantages in cost and integration but face challenges with accuracy due to operational complexities. This study presents six distinct artificial neural networks (ANNs)-based cascaded classification schemes for bearing fault severity classification. Discrete wavelet transform (DWT) with Symlet (Sym) is used for multi-resolution feature extraction in currents, combined with motor speed data to generate multi-channel features. These features are fed into an ANN-based level I algorithm using various fusion techniques, offering a more interpretable algorithmic framework. One approach employs a multi-input ANN for level I, integrated with an ANN-based level II for refined severity classification. This two-level cascaded approach achieves an accuracy over 99% on the Paderborn University dataset in various operational scenarios. The model is trained and analyzed using MATLAB. The proposed cascaded algorithms outperform single-stage models, and enhanced signal preprocessing improves accuracy and noise resilience. Additionally, the proposed risk-based performance indicator offers insights into maintenance strategies, while an optimum algorithm selection framework identifies an algorithm by considering a trade-off between computational complexity and accuracy.

Viktoria Falkowski, Wilhelm Pfleging

Changing the topography of electrodes by ultrafast laser ablation has shown great potential in enhancing electrochemical performance in lithium-ion batteries. The generation of microstructured channels within the electrodes creates shorter pathways for lithium-ion diffusion and mitigates strain from volume expansion during electrochemical cycling. The topography modification enables faster charging, improved rate capability, and the potential to combine high-power and high-energy properties. In this study, we present a preliminary exploration of this approach for sodium-ion battery technology, focusing on the impact of laser-generated channels on hard carbon electrodes in sodium-metal half-cells. The performance was analyzed by employing different conditions, including different electrolytes, separators, and electrodes with varying compaction degrees. To identify key factors contributing to rate capability improvements, we conducted a comparative analysis of laser-structured and unstructured electrodes using methods including scanning electron microscopy, laser-induced breakdown spectroscopy, and electrochemical cycling. Despite being based on a limited sample size, the data reveal promising trends and serve as a basis for further optimization. Our findings suggest that laser structuring can enhance rate capability, particularly under conditions of limited electrolyte wetting or increased electrode density. This highlights the potential of laser structuring to optimize electrode design for next-generation sodium-ion batteries and other post-lithium technologies.

Ming Han, Ge Sun, Paul F. Nealey et al.

Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu et al.

We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Fabian C. Peña

Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.

E. Shahri, P. Pedreiras, L. Almeida

The increasing prominence of concepts such as Smart Production and Industrial Internet of Things (IIoT) within the context of Industry 4.0 has introduced a new set of requirements for the engineering of industrial systems, including support for dynamic environments, timeliness guarantees, support for heterogeneity, interoperability and reliability. These requirements are further exacerbated at the network level by the notable rise in the number and variety of devices involved. To stay competitive in this ever-changing industrial landscape while boosting productivity, it is vital to meet those requirements, combining established protocols with emerging technologies. Software-Defined Networking (SDN) is the forefront traffic management paradigm that offers flexibility for complex industrial networks, enabling efficient resource allocation and dynamic reconfiguration. Message Queuing Telemetry Transport (MQTT) is a low-overhead protocol of the application layer that is gaining popularity in the scope of the IoT and IIoT. However, its Quality-of-Service (QoS) policies do not support timeliness requirements. This article presents a framework that seamlessly integrates SDN and MQTT, enhancing network management flexibility while satisfying real-time requirements found in industrial environments. It leverages the User Properties of MQTTv5 to allow specifying real-time requirements. MQTT traffic is intercepted by a Network Manager that extracts real-time information and instructs an SDN controller to deploy corresponding network reservations. MQTT traffic across multiple edge networks is propagated by selected brokers using multicasting. Extensive experiments validate the proposed approach, demonstrating its superiority over MQTT and Direct Multicast-MQTT (DM-MQTT) DM-MQTT in latency reduction. A response time analysis, validated experimentally, emphasizes robust performance across metrics.

Xianzhe Yang

Presents corrections to the paper, Industrial Expert Systems Review: A Comprehensive Analysis of Typical Applications.

Bobok I.I., Kobozeva A.A.

. The widespread introduction of information technologies into all spheres of society, the crea-tion of a significant amount of confidential and critical data in digital form leads to an increase in the priority of information security tasks everywhere, including in the energy sector, which relates to the critical infrastructure of any state. The purpose of the work is to develop the men-tioned approach to ensure the possibility of increasing the efficiency of information security methods based on it. The goal was achieved through a detailed study of disturbances in the val-ues of formal parameters that uniquely determine the matrix that is assigned to the information security system under conditions of active attacks (disturbances) on the system. Singular num-bers and singular vectors of the matrix are considered as such parameters. The most important result of the work is the substantiation of the existence and establishment of interconnected re-gions of stabilization of disturbances of singular numbers and singular vectors of the system ma-trix, while the region of stabilization of singular numbers corresponds to the region of monoto-nous decrease in their disturbances with increasing numbers, while the stabilization of singular vectors corresponds to the region in which their disturbances are comparable with 90 degrees. It is shown that the stabilization process is determined by the mathematical properties of the pa-rameters under consideration. The significance of the obtained result lies in the possibility of using it to improve various information security systems that were built or studied using a gen-eral approach to analyzing their state, both theoretically and practically. The work provides ex-amples of such use.

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy et al.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.

Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi et al.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.

Zheng Li, Rajiv Ranjan

By seamlessly integrating everyday objects and by changing the way we interact with our surroundings, Internet of Things (IoT) is drastically improving the life quality of households and enhancing the productivity of businesses. Given the unique IoT characteristics, IoT applications have emerged distinctively from the mainstream application types. Inspired by the outlook of a programmable world, we further foresee an IoT-native trend in designing, developing, deploying, and maintaining software systems. However, although the challenges of IoT software projects are frequently discussed, addressing those challenges are still in the "crossing the chasm" period. By participating in a few various IoT projects, we gradually distilled three fundamental principles for engineering IoT-native software systems, such as just enough, just in time, and just for "me". These principles target the challenges that are associated with the most typical features of IoT environments, ranging from resource limits to technology heterogeneity of IoT devices. We expect this research to trigger dedicated efforts, techniques and theories for the topic IoT-native software engineering.

saeedeh zebhi, Seyed Mohammad Taghi AlModaressi, Vahid Abootalebi

In this paper, two different methods of human activity recognition based on video signals are introduced. The first method explores the effectiveness of combining feature descriptors obtained by local descriptors and artificial neural network classifier. It is used in the traditional approach and the local descriptors extract interest points or local patches from the videos, and the feature vectors are later constructed based on the intrests, and eventually feature vectors are used as the input of a two-layer feed-forward artificial neural network (ANN). Experimental results show that using the HOG3D descriptor with ANN gives the best performance. On the other hand, deep learning architectures have attracted much consideration for automatic feature extraction in the last years, so an improved 3D convolutional neural network architecture is also designed as the second method. They are implemented and compared with state-of-the-art approaches on two data sets. The results exhibit that method 1 is superior when the shortage of sample data is the main restriction. It respectively achieves recognition accuracies of 97.8% and 99.8% for the Weizmann and KTH action data sets. In addition, method 2 is considerable for its automatic features extraction, and achieves an acceptable result with lots of original training data. As a result, it gets recognition accuracy of 92% for the KTH data set while this value is drastically reduced for the Weizmann data set.

Sanghoon Jeon, Joonwoo Son, Myoungouk Park et al.

Despite any doubts about driving safety, many stroke drivers drive again due to the absence of valid screening tools. The on-road test is considered a formal assessment, but there are safety issues in testing directly on stroke patients who are not fully capable of driving. A driving simulator is a promising tool since it provides meaningful information for identifying hazards to driving safety across different driver populations and driving conditions. Using the advantages of a driving simulator, we propose a Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS is designed not only to pre-screen invalid stroke drivers before the on-road test but also to provide problematic driving items for the use in driving rehabilitation. To design assessment classifiers, i.e., the core engine of Driving-PASS, we collect driving data from a total of twenty-seven participants in thirteen driving scenarios. Thereafter, we get subjective assessment results from ten driving evaluators in eleven assessment items. By using driving data and subjective assessment results, we construct eleven assessment classifiers for ten driving ability items and one driving suitability item. We addressed the technical challenges such as handcrafted features and imbalanced dataset by a feature extraction method using pre-trained CNN models and a resampling method. Through comprehensive performance evaluation, we build eleven accurate assessment classifiers in Driving-PASS by carefully selecting deep features in each assessment item. We envision that Driving-PASS can be used as a pre-screening tool for evaluating stroke drivers and will ultimately improve road safety.