Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Seungnam Yu, Geegum Lee, Jeongmok Kim

This study presents a VR-based teleoperation framework enhancing collaborative robot stability and manipulability via hand-tracking, adaptive control, and dual-modality haptic feedback. It addresses critical synchronization challenges (singularity avoidance, tracking responsiveness, and workspace constraints), which are especially problematic in first-person VR where kinematic limits are not directly perceivable. The framework employs Adaptive Damped Least Squares (A-DLS) to maintain manipulability near singular configurations, workspace impedance control to enforce boundary constraints, and vibrotactile feedback delivered through a haptic glove to convey both workspace limits (fingertip vibration) and path deviation information (wrist vibration) to operators. Key features include real-time hand-tracking, workspace calibration, and adaptive controls to ensure seamless coordination between virtual and real robot workspaces. Experimental validation through two complementary studies demonstrates the system’s effectiveness. Experiment 1 evaluated singularity management and workspace stability, showing that the A-DLS algorithm maintained manipulability above critical thresholds for 92% of operational time versus 78% without adaptive damping. Experiment 2 assessed trajectory tracking accuracy through a path-following task with 10 participants. Results demonstrate that haptic-enabled control achieves a 24.7% reduction in mean path-following error (from 10.03 mm to 7.55 mm, p = 0.001) compared to haptic-disabled conditions, indicating improvements in both accuracy and consistency. Although haptic guidance modestly increases task time due to higher precision focus, the resulting gains in accuracy and stability make this framework ideal for precision-critical tasks. By ensuring stability near workspace boundaries, the system effectively facilitates VR-based teleoperation for applications like painting, polishing, and contour-following.

Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez et al.

Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system. Our recent study (Vijayarangan et al. 2023) reported that an AE combined with neural ODE (NODE) as a surrogate reduced order model (ROM) for the integration of stiff chemically reacting systems led to a significant reduction in the temporal stiffness, and the behavior was attributed to the identification of a slow invariant manifold by the nonlinear projection using the AE. The present work offers a fundamental understanding of the mechanism of formation of a non-stiff latent space and stiffness reduction by employing concepts from information theory and better mixing. The learning mechanisms of both the encoder and the decoder are explained by plotting the evolution of mutual information and identifying two different phases. Subsequently, the density distribution is plotted for the physical and latent variables, which shows the transformation of the rare event in the physical space to a highly likely (more probable) event in the latent space provided by the nonlinear autoencoder. Finally, the nonlinear transformation leading to density redistribution is explained using concepts from information theory and probability.

Dileep Kumar Kana Padinharu, Guang‐Jin Li, Guang‐Bo Zhang et al.

ABSTRACT This paper proposes a Vernier machine with an improved stator design that adopts open stator slots and permanent magnets installed on both the rotor and stator. Compared to an existing Vernier machine in the literature, referred to as Design 1, the exclusive stator slots for permanent magnets in the proposed machine help mitigate demagnetisation issues by physically isolating the windings and the magnets. Additionally, the open stator slot design facilitates the installation of form‐wound coils which is desirable for large generators used in direct‐drive wind power applications. Using 2‐dimensional finite element analysis, the proposed design is compared with a conventional surface‐mounted permanent magnet machine, a conventional Vernier machine and Design 1. The findings indicate that the proposed Vernier machine uses both odd and even harmonics to generate torque, and it can exhibit superior electromagnetic performance, including torque and efficiency, compared to the conventional surface‐mounted permanent magnet machine and conventional Vernier machines and demonstrate comparable electromagnetic performance to Design 1. Furthermore, to enhance the torque‐to‐mass ratio of the proposed Vernier machine, through‐slots below the stator magnets are introduced and found to be effective without significantly compromising torque and efficiency. The simulations have been validated by experiments based on a prototype.

TANG Pingping, ZHANG Hui, DONG Yuning et al.

Considering the problem of traditional fractal (TF) features being difficult to achieve both high accuracy and fast speed in network traffic cognition, the idea of space-time separation was proposed on the basis of fractal theory. With space-time fractal (SF) features generated by the space-time separation, a new traffic cognition system called the space-time fractal model (SFM) was established. In order to obtain SF, the spatial and temporal sequences were observed, and further constructed to generate vectors by Legendre transformation, which were mapped into dual space. The physical significance of SF lied in capturing the characteristics of traffic bursts at different scales of space and time, while TF were the fusion of SF across spatial and temporal scales. Compared with TF, SF represented network traffic more comprehensively and thus were able to identify traffic more accurately. Moreover, SF were more computationally efficient than TF, enabling SFM to achieve high cognition speed as well as strong cognition accuracy. The experimental results show that the cognition performance of SFM is superior to other methods.

Alex Gu, Naman Jain, Wen-Ding Li et al.

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.

Balint Bicski, Adrian Pekar

This paper presents a comprehensive study on the identification and analysis of Service Degradation (SD) events within a university dormitory network, leveraging LAN data to develop a robust methodology applicable to diverse networking environments. Employing statistical techniques, such as Interquartile Range (IQR) and Z-score analyses, we detect significant deviations in network performance—specifically, extreme delays and jitter—that indicate potential SD. The methodology was rigorously validated in various settings, demonstrating minimal deviations in results and reinforcing the approach’s consistency and reliability. Initial tests conducted in a university dormitory environment suggest the model’s potential applicability in both residential and enterprise networks, thus broadening its utility. By refining the detection and understanding of SD indicators, this research contributes systematic methodological applications and a valuable annotated dataset to the field. This groundwork enables network administrators to enhance service quality preemptively, offering significant implications for future research and practical applications in network management.

Bradley P. Allen, Filip Ilievski

Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting, together with its paradigms such as expert systems, semantic web, and language modeling. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns, building on top of existing knowledge engineering workflows and boxologies. We provide a six-step roadmap that can enable the development of such an architecture, providing an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities.

Mridul Shukla, D. Singh, Ashwani Yadav et al.

This paper presents a new method for determining the best locations for electric vehicle charging stations in cities. The proposed optimization model uses data analysis and machine learning techniques to predict the demand for charging stations based on various factors, including driving patterns, population density, and the distribution of commercial and residential areas. An optimization algorithm then identifies the optimal placement of charging stations that can meet the predicted demand while minimizing infrastructure costs. Simulation studies demonstrate that the proposed model provides a more efficient and cost-effective deployment of charging stations when compared to existing approaches. This practical and innovative solution can inform decision-making processes for charging station deployment, reduce the cost and complexity of charging infrastructure installation. The proposed data-driven approach can provide valuable insights for policymakers, helping to guide infrastructure deployment and reduce costs. It also advances the state of the art in electrical and electronics engineering, introducing a novel and practical method to optimize EV charging infrastructure deployment in urban areas

CHEN Qibo, ZHAO Yongwu, BIAN Da

In order to improve the corrosion resistance of 40Cr steel surface,bis-[7-(triethoxy silicon)propyl]-tetrasulfide(BTESPT),zirconium nitrate and phytic acid were used to prepare silane zirconium salt composite conversion coating with excellent corrosion resistance on the surface of 40Cr steel.The film forming process conditions of silane zirconium salt composite conversion solution were determined by orthogonal tests.The corrosion resistance,morphology,composition and potential characteristics of the composite coating were analyzed by copper sulfate titration,scanning electron microscopy(SEM),Fourier transform infrared spectroscopy(FTIR)and electrochemical tests.Results showed that the optimum process of silane zirconium salt composite coating was as follows:silane concentration was 5(volume fraction),zirconium nitrate concentration was 0.75%(mass fraction),pH value of solution was 4,hydrolysis temperature was 25 C,and reaction time was 50 s.Through the results of copper sulfate drop test and electrochemical test,it was found that the corrosion resistance of the composite convesion coating doped with phytic acid was significantly improved compared with that of the silane coating and the silane zirconium salt coating.Through the observation of micro morphology,it was indicated that the addition of phytic acid made up for the defects of the coating,hindered the diffusion of corrosive medium,therefore,enhancing the corrosion resistance of the coating.

Marx Viana, Paulo Alencar, Everton Guimaraes et al.

The demand for creating increasingly dynamic, autonomous and proactive software systems is challenging for the traditional Multi-agent Systems (MASs) approaches. Such requirement has given rise to adaptive software agents approaches. At the same time, norm is an essential and challenging feature that still tends to be addressed in adaptive MAS. In fact, norms to regulate agent behavior is still a vague concept that has not been properly investigated in terms of modeling and implementation. Even though many researchers have proposed modeling languages to deal with different abstractions, these languages fail to support the modeling of abstractions, such as adaptation and norms. Even more severe is the fact that little has been done to support the systematic design of Adaptive Normative Multi-Agent Systems (ANMASs). To facilitate the design and development of ANMASs, this paper presents a new metamodel, as well as language support, as means to provide tools to enable software developers. The proposed metamodel fosters a better understanding of the way agents are able to change their behaviors to deal with norms and captures interactions between agent’s norms and adaptation. To this end, our research is organized into five steps: (i) a literature review to identify the limitations of existing approaches related to ANMAS modeling; (ii) propose a new metamodel to support adaptative and normative concepts; (iii) propose a new language for modeling ANMASs; (iv) perform a qualitative and quantitative evaluation of the proposed language using a real case scenario, and (v) an empirical evaluation. The proposed metamodel and its associated modeling language advances the state of the art in modeling MASs and the approach is assessed in terms of correctness, time and difficulty. Our initial results revealed that our approach can be feasibly applied in a real world application, and is less difficult to apply and requires less time in comparison with a traditional approach. As software applications become more dynamic and adaptive, we believe it is essential to support developers to model MASs with abstractions such as adaptive agents, norms and their relationships. Such information can be foundational to steer future research on modeling adaptive agents capable of understanding and dealing with norms and adaptation.

Hakje Yoo, Taeyong Sim

Coarse registration is the first step in determining the accuracy of surgical navigation. The purpose of this study was to present an automated coarse registration (ACR) methodology to improve the convenience and accuracy. For this purpose, a deep learning model based on a convolutional neural network was used. The input variable used for learning was virtual patient point-cloud (VPPC) generated based on medical image. Output variables were values of coordinate transformation obtained in the process of sending the VPPC to the surrounding space of a medical image. The ACR model consisted of a step of extracting global features of point-clouds from medical image and patient space and a step of predicting the information of 3-dimensional coordinate transformation through global features. The coefficients of determination that evaluated the similarity between predicted and actual rotation values on the x, y, and z axes were 0.993, 0.989, and 0.990, respectively. The coefficients of determination of the predicted and actual translation values on x, y, and z were 0.993, 0.989, and 0.994, respectively. As a result of coarse registration of three phantoms using the ACR, the registration errors between the patient and the computed tomography point-cloud were <inline-formula> <tex-math notation="LaTeX">$3.813~\pm ~0.792$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$3.786~\pm ~0.734$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$3.653~\pm ~0.668$ </tex-math></inline-formula> mm, which were significantly improved over the conventional method&#x2019;s registration error (<inline-formula> <tex-math notation="LaTeX">$4.671~\pm ~0.738$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$4.865~\pm ~0.776$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$4.670~\pm ~0.455$ </tex-math></inline-formula> mm). The proposed method can provide convenience in the pre-operative preparation stage by automating coarse registration. It is expected that repeatability and reproducibility can be provided by eliminating random errors that might occur by the operator.

Robert Bocchino, Arne Nordmann, Allison Thackston et al.

Robotics software is pushing the limits of software engineering practice. The 3rd International Workshop on Robotics Software Engineering held a panel on "the best practices for robotic software engineering". This article shares the key takeaways that emerged from the discussion among the panelists and the workshop, ranging from architecting practices at the NASA/Caltech Jet Propulsion Laboratory, model-driven development at Bosch, development and testing of autonomous driving systems at Waymo, and testing of robotics software at XITASO. Researchers and practitioners can build on the contents of this paper to gain a fresh perspective on their activities and focus on the most pressing practices and challenges in developing robotics software today.

Olga Movilla Miangolarra, Amirhossein Taghvaei, Yongxin Chen et al.

The purpose of this work is to present the concept of an autonomous Stirling-like engine powered by anisotropy of thermodynamic fluctuations. Specifically, simultaneous contact of a thermodynamic system with two heat baths along coupled degrees of freedom generates torque and circulatory currents -- an arrangement referred to as a Brownian gyrator. The embodiment that constitutes the engine includes an inertial wheel to sustain rotary motion and average out the generated fluctuating torque, ultimately delivering power to an external load. We detail an electrical model for such an engine that consists of two resistors in different temperatures and three reactive elements in the form of variable capacitors. The resistors generate Johnson-Nyquist current fluctuations that power the engine, while the capacitors generate driving forces via a coupling of their dielectric material with the inertial wheel. A proof-of-concept is established via stability analysis to ensure the existence of a stable periodic orbit generating sustained power output. We conclude by drawing a connection to the dynamics of a damped pendulum with constant torque and to those of a macroscopic Stirling engine. The sought insights aim at nano-engines and biological processes that are similarly powered by anisotropy in temperature and chemical potentials.

Tobias Lorey, Paul Ralph, Michael Felderer

As software engineering research becomes more concerned with the psychological, sociological and managerial aspects of software development, relevant theories from reference disciplines are increasingly important for understanding the field's core phenomena of interest. However, the degree to which software engineering research draws on relevant social sciences remains unclear. This study therefore investigates the use of social science theories in five influential software engineering journals over 13 years. It analyzes not only the extent of theory use but also what, how and where these theories are used. While 87 different theories are used, less than two percent of papers use a social science theory, most theories are used in only one paper, most social sciences are ignored, and the theories are rarely tested for applicability to software engineering contexts. Ignoring relevant social science theories may (1) undermine the community's ability to generate, elaborate and maintain a cumulative body of knowledge; and (2) lead to oversimplified models of software engineering phenomena. More attention to theory is needed for software engineering to mature as a scientific discipline.

Y. Kuramitsu, Takumi Minami, T. Hihara et al.

Atomically thin graphene is a transparent, highly electrically and thermally conductive, light-weight, and the strongest material. To date, graphene has found applications in many aspects including transport, medicine, electronics, energy, defense, and desalination. We demonstrate another disruptive application of graphene in the field of laser-ion acceleration, in which the unique features of graphene play indispensable role. Laser driven ion sources have been widely investigated for pure science, plasma diagnostics, medical and engineering applications. Recent developments of laser technologies allow us to access radiation regime of laser ion acceleration with relatively thin targets. However, the thinner target is the less durable and can be easily broken by the pedestal or prepulse through impact and heating prior to the main laser arrival. One of the solutions to avoid this is plasma mirror, which is a surface plasma created by the foot of the laser pulse on an optically transparent material working as an effective mirror only for the main laser peak. So far diamond like carbon (DLC) is used to explore the ion acceleration in extremely thin target regime (< 10 nm) with plasma mirrors, and it is necessary to use plasma mirrors even in moderately thin target regime (10-100 nm) to realize energetic ion generation. However, firstly DLC is not 2D material, and therefore, it is very expensive to make it thin and flat. Moreover, graphene is stronger than diamond at extremely thin regime, and much more reasonable for mass-production. Furthermore, installing and operating plasma mirrors at high repetition rate is also costly. Here we show another direct solution using graphene as the thinnest and strongest target ever made. We develop a facile transfer method to fabricate large-area suspended graphene (LSG) as target for laser ion acceleration with precision down to a single atomic layer. Direct irradiation of the LSG targets with an ultra intense laser generates energetic carbons and protons evidently showing the durability of graphene without plasma mirror. This extends the new frontier of science on graphene under extreme electromagnetic field, such as energy frontier and nuclear fusion.

Boyu Ni, Xiaoyang Dong, Keting Jia et al.

Simpira v2 is an AES-based permutation proposed by Gueron and Mouha at ASIACRYPT 2016. In this paper, we build an improved MILP model to count the differential and linear active Sboxes for Simpira v2, which achieves tighter bounds of the minimum number of active Sboxes for a few versions of Simpira v2. Then, based on the new model, we find some new truncated differentials for Simpira v2 and give a series (quantum) collision attacks on two versions of reduced Simpira v2.

Jun Tang, Tian Guo, Jung Sik Kim et al.

A current-mode four-phase synchronous buck converter with a dynamic dead-time control (DDTC) method is presented in this work. A brief analysis of the multiphase buck converter power efficiency in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is performed to provide design guidelines for minimizing power losses. In synchronous converters, the power efficiency can always be improved by optimizing the dead-time. Therefore, a gate driver with DDTC is designed to optimize the dead-time in every switching cycle, thereby improving power efficiency particularly under heavy load conditions. The proposed buck converter has the ability to deliver a maximum load current of 6.0 A at a typical output voltage of 1.2 V from a power supply of 3.0 V. A power efficiency improvement of over 1.0&#x0025; is achieved when the load current is over 2.0 A, and an improvement of about 2.4&#x0025; is obtained at a load current of 4.0 A. A peak power efficiency of 92.8&#x0025; is measured at an output voltage of 1.8 V.

Zhiqiang Gong, Weien Zhou, Jun Zhang et al.

Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components. However, prior methods, which mainly use the interpolate estimation to reconstruct the temperature field from limited monitoring points, require large amounts of temperature tensors for an accurate estimation. This may decrease the availability and reliability of the system and sharply increase the monitoring cost. To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly. First, we define the TFR-HSS task mathematically, and numerically model the task, and hence transform the task as an image-to-image regression problem. Then this work develops the deep reversible regression model which can better learn the physical information, especially over the boundary. Finally, considering the physical characteristics of heat conduction as well as the boundary conditions, this work proposes the physics-informed reconstruction loss including four training losses and jointly learns the deep surrogate model with these losses unsupervisedly. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness of the proposed method.

Aijun Hu, Chujin Li, Jing Wu

Large-scale data presents great challenges to data analysis due to the limited computer storage capacity and the heterogeneous data structure. In this article, we propose a distributed expectile regression model to resolve the challenges of large-scale data by designing a surrogate loss function and using the Iterative Local Alternating Direction Method of the Multipliers (IL-ADMM) algorithm, which is developed for the calculation of the proposed estimator. To obtain nice performance only after fewer rounds of communications, the proposed method only needs to solve an M-estimation problem on the master machine while the other working machines only to compute the gradients based on local data. Moreover, we show the consistency and the asymptotic normality of the proposed estimator, and illustrate the efficient proof by numerical simulations and positive analysis on the superconductor data.

Wubin Shan, Bangyan Li, Shigang Qin et al.

Nonlinear bending and nonlinear free vibration analysis are presented for FG nanobeams based on physical neutral surface concept and high-order shear deformation beam theory with a von Kármán-type equations and including thermal effects. The material properties are temperature-dependent and vary in the thickness direction. Nonlinear bending approximate solutions and free vibration solutions for present model with fixed supported boundary conditions are given out by a two-step perturbation method. Some comparisons are presented to valid the reliability of the present study. In numerical analysis, the effects of the volume fraction, nonlocal parameter, strain gradient parameter, and temperature changes on nonlinear bending and vibration are investigated.