Hasil untuk "Electric apparatus and materials. Electric circuits. Electric networks"

Minseob Shim, Donghyun Kim, Jong-Hun Kim et al.

This article provides a comprehensive review of power management circuit techniques for millimeter-scale biomedical sensing systems, which operate under strict power and energy constraints. It begins by introducing a miniature sensing platform and outlining the key challenges associated with limited energy availability in such ultrasmall devices. The discussion then highlights advances in circuit design for efficient power conversion, battery management, ambient energy harvesting, and wireless power transfer. By examining these techniques, this article aims to clarify the major design challenges and emerging solutions that are driving the development of next-generation miniature biomedical electronics.

M. J. Lynch, R. Jacobs, G. A. Bruno et al.

M. Ajay Kumar, Cian O'Mahoney, Pedro Kreutz Werle et al.

Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Existing accelerator-generation tools, such as AMD&#x2019;s FINN, do not adequately address extreme resource limitations faced by IoT endpoints operating in bare-metal environments without an operating system (OS). To overcome these constraints, we propose MARVEL&#x2013;an automated, end-to-end framework that generates custom RISC-V ISA extensions tailored to specific DNN model classes, with a primary focus on convolutional neural networks (CNNs). The proposed method profiles high-level DNN representations in Python and generates an ISA-extended RISC-V core with associated compiler tools for efficient deployment. The flow leverages (1) Apache TVM for translating high-level Python-based DNN models into optimized C code, (2) Synopsys ASIP Designer for identifying compute-intensive kernels, modeling, and generating a custom RISC-V and (3) Xilinx Vivado for FPGA implementation. Beyond a model-class specific RISC-V, our approach produces an optimized bare-metal C implementation, eliminating the need for an OS or extensive software dependencies. Unlike conventional deployment pipelines relying on TensorFlow/PyTorch runtimes, our solution enables seamless execution in highly resource-constrained environments. We evaluated the flow on popular DNN models such as LeNet-5*, MobileNetV1, ResNet50, VGG16, MobileNetV2 and DenseNet121 using the Synopsys trv32p3 RISC-V core as a baseline. Results show a <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> speedup in inference and upto <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> reduction in energy per inference at a 28.23&#x0025; area overhead when implemented on an AMD Zynq UltraScale+ ZCU104 FPGA platform.

Jiming Zhou, Kang Yun, Lehua Qi

Under the background of global climate change and carbon neutrality for sustainable development, advanced materials that combine lightweight properties with high strength and modulus are crucial for diminishing energy usage and carbon emissions within the fields of transportation and aerospace. Magnesium alloys stand out as promising options, given their low density and superior specific strength. The elastic moduli of Mg alloys can be significantly improved through the incorporation of high-modulus reinforcements, which is essential for enhancing materials performance and reducing environment impact. This work reviews the international and domestic advancements in magnesium matrix composites involving the elastic modulus over the past several decades, encompassing the prediction models for elastic modulus and their applications, the liquid fabrication process, and the influence of various reinforcement types on the composite's elastic modulus. Finally, this review forecasts the future trends in the development of high-modulus magnesium matrix composites, with the intention of offering theoretical insights and experimental references for research, development, and utilization of the materials that are lightweight, high-strength, and high-modulus.

Jun‐Su Eun, Jinuk Lee, Jeong‐Hyeon Na et al.

Abstract Metal‐oxide thin‐film transistors (TFTs) have garnered much attention because of their advantages such as high transparency, low leakage current, and low processing temperature. However, there is a need to continuously improve their mobility and bias stability for application to next‐generation advanced electronics. In this study, the thickness of bilayer semiconductors is finely controlled to enhance the charge transport characteristics and bias stability in solution‐processed heterojunction oxide TFTs. The thicknesses of the top and bottom layers in the bilayer are individually adjusted by controlling solution molarity. The introduction of a bilayer channel improved the electrical performance of oxide TFTs via effective charge transport. However, trap‐limited conduction becomes dominant in the bilayer with an excessively thick top layer, thereby leading to a significant reduction in mobility and positive bias stability. Meanwhile, although increasing the bottom layer thickness contributes to improved mobility and reliability, it causes a serious negative shift in threshold voltage (VTH). TFTs with an optimized bilayer structure show high mobility at a VTH close to 0 V and have particularly excellent positive bias stress stability. This study on bilayer channel thickness will be beneficial for developing advanced transistors with optimized bilayer or multilayer channels.

Mohammad Amini, Linghao Yan, Orlando J. Silveira et al.

Van der Waals heterostructures are a core tool in quantum material design. The recent addition of monolayer ferroelectrics expands the possibilities of designer materials. Ferroelectric domains can be manipulated using electric fields, thus opening a route for external control over material properties. In this paper we explore the possibility of engineering magneto-electric coupling in ferroelectric heterostructures by studying the interface of bilayer SnTe with iron phthalocyanine molecules as a model system. The molecules act as sensor spins, allowing us to sample the magneto-electric coupling with nanometer precision through scanning tunneling microscopy. Our measurements uncover a structural, and therefore material-independent and intrinsic, mechanism to couple electric and magnetic degrees of freedom at the nanoscale.

Steven Louis, Hannah Bradley, Vasyl Tyberkevych

Dynamics of a ferromagnetic macrospin (e.g., a free layer of a magnetic tunnel junction (MTJ)) can be described in terms of equivalent capacitor charge $Q$ and inductor flux $Φ$, in a manner similar to a standard electric LC circuit, but with strongly nonlinear and coupled capacitance and inductance. This description allows for the inclusion of Gilbert damping and spin transfer torques and yields a relatively simple equivalent electric circuit, which can be easily modeled in LTspice or other electrical engineering software. It allows one to easily simulate advanced electrical circuits containing MTJs and conventional electronic components in standard simulation software.

Nick R. Schwartz, Carlos A. Romero-Talamás, Marlene I. Patino et al.

The centrifugal mirror confinement scheme incorporates supersonic rotation into a magnetic mirror device, which stabilizes and heats the plasma. This concept is under investigation in the Centrifugal Mirror Fusion Experiment (CMFX) at the University of Maryland. Plasma rotation is driven by an axial magnetic field and a radial electric field that lead to velocity drifts in the azimuthal direction. An electrically insulating material is required to prevent the applied voltage from shorting on the grounded chamber. Hexagonal boron nitride (hBN) is a promising candidate material for plasma-facing components in future centrifugal mirrors due to its exceptional thermal and electrical properties. However, its performance under intense particle and heat fluxes characteristic of the plasma edge in fusion devices remains largely unexplored. Computational modeling for ion- and neutron-material interactions was carried out with RustBCA and OpenMC, respectively, and predicts relatively good performance in comparison to other insulating materials. Material coupons were then exposed to plasma in PISCES-A at UCSD and CMFX. A load-locked sample feedthrough was constructed and installed on CMFX to test coupons. Two erosion mechanisms were identified -- sputtering and grain ejection -- both of which were more apparent in silicon carbide than hBN.

Jiyan Zou

A transmission-level vacuum circuit breaker (TVCB) is a high-tech power device that major companies have been competing to develop in recent years. The technical bottleneck for the development comes mainly from the more severe electric field stress (E-stress) in the working scene and the dynamic insulation deterioration. The saturation effect of the breakdown voltage on the vacuum gap separation forces the electrode structure design to be magnified geometrically, causing new technical problems in the multi-physical field. In terms of enhancing the dynamic insulation level of TVCB, it is necessary to focus on the weak-point of breakdown (WP) to form a mechanism for strengthening the dielectric recovery. Combined measures to restrain WPs includes: re-examining the selection of contact materials and the structural design of the interrupter for intercepting WP, defining WP states parameters, forming a closed-loop control of deep conditioning, and finally, expanding dynamicconditioning procedures and in-service maintenance requirements.The tolerance of E-stress is the underlying logic from the perspective of the field source. Along the path of E-stress sharing, the traditional TVCB electrode system can be converted to a back-to-back series structure of a vacuum interrupter pair (VIP), with each separation of the pair set in the linear growth region of the breakdown/separation curve. So the axial E-stress sharing of the main gap can be completed under the unchanged spatial scale. Several floating potential shields are set up, together with the main shield, formed an auxiliary gap channel parallel to the main gap. It can further share the E-stress radically. In addition, the finely designed floating shields can also balance the distribution of TRV inside the VIP.Taking the VIP electric field design of 252kV-TVCB as an empirical research case, the modeling and simulation of the VIP equivalent electrical network were completed. By the analysis of the VIP dual-ended drive and the discussion of the multi-break synchronization control, the module framework design and VIP prototype for 252kV-TVCB have been completed. The high-voltage test device for the factory inspection of the vacuum interrupter is used to test the voltage parameters of the VIP prototype.The empirical research results show that, the E-stress sharing is a practical strategy to increase the working voltage of TVCB. The comprehensive management of WP can make up for the technical shortcoming of TVCB relative to SF6 in dynamic insulation. The vacuum interrupter pair form can increase the working voltage and dynamic insulation level. Unlike the conventional double-break VCB, the pair structure shares a break-node, which reduces the influence of the distribution parameters, so as to obtain higher series efficiency. Furthermore, the discrete welding of VIP can also significantly reduce the manufacturing difficulty and production cost of TVCB.

Ilario Triscari, Gabriel Lantz, Markus Abplanalp et al.

The behavior of the electric arc that is ignited in a circuit breaker during a short-circuit is hard to predict, due to the involvement of several different physical phenomena; arc simulation tools are therefore necessary to be able to minimize the trial and error process during the design of a product. In this paper, we present results obtained using an arc simulation tool that implements fluid dynamics and electromagnetism together with metal and plastic ablation, taking also into account the dynamics of the moving parts; the tool also couples the simulated arc voltage to an electrical network to predict the influence of the arc on the short-circuit current.The comparison between the experimental and simulated arc voltages for different short-circuit performances of a miniature circuit breaker is shown, highlighting the key points of the modeling process (such as the mobile contact dynamics). The model is then used to investigate the contribution of the ablating materials to the pressure and the voltage.

Hongjung Kim, Changshin Jo

Rechargeable secondary batteries have been widely used in various energy storage applications, ranging from small electronic devices to large-scale energy systems. The rapid expansion of the electric vehicle (EV) market, driven by global climate policies, has led to an unprecedented increase in the demand for lithium-ion batteries (LIBs), which currently dominate the secondary battery market. The rising demand for energy has intensified the need for batteries with higher energy densities. However, commercial LIBs utilize graphite anodes, which suffer from significant limitations of low theoretical capacity of 372 mAh g⁻1, necessitating a transition to alternative materials. Lithium metal is considered the ultimate anode material, due to its high theoretical capacity of 3860 mAh g⁻1, a low redox potential (−3.04 V vs. SHE), and low density of 0.53 g cm⁻3. Despite these advantages, Li metal anodes face several critical challenges, including unstable interface properties that lead to dendrite formation during Li plating and stripping. This can result in internal short circuits and thermal runaway. Furthermore, the accumulation of dead Li during cycling increases cell resistance, leading to rapid capacity degradation. To mitigate these issues, numerous strategies have been developed. These include electrolyte optimization and the introduction of artificial layers for interface engineering. Yet, due to the infinite volumetric expansion of Li during plating, focusing solely on interface modification is insufficient. Therefore, it is crucial to provide space for accommodating Li metal, which can be achieved by incorporating three-dimensional (3D) hosts. These 3D hosts, characterized by high surface area and porosity, can buffer the mechanical stress caused by volumetric changes by storing Li within their internal voids. Moreover, according to Sand’s time theory, 3D hosts can lower the local current density, thereby restricting the growth of Li dendrites. Among the various materials, carbon-based frameworks have demonstrated significant potential as Li metal hosts due to their lightweight nature, excellent mechanical strength, and superior electrical conductivity. Nevertheless, the inherent lithiophobic properties of carbon can lead to uneven Li growth, necessitating additional surface modifications. Strategies such as heteroatom doping or the introduction of lithiophilic metal seeds on carbon surfaces have been proposed to address these challenges. However, the non-uniform distribution of metal seeds and excessively strong Li adsorption can restrict surface diffusion of Li, leading to localized Li accumulation at lithiophilic sites. To ensure uniform Li growth, precise design aimed at achieving an even distribution of lithiophilic seeds is crucial. In addition, an in-depth understanding of the properties and interactions of lithiophilic seeds is essential for optimizing their placement and functionality. Furthermore, to maximize energy density, systems that eliminate the copper foil current collector and utilize only freestanding carbon substrates as host materials are gaining attention. These carbon substrates simultaneously function as the Li metal host and the current collector. Developing scalable and efficient methods to fabricate such carbon films is critical for their practical implementation. In this study, we adopted cost-effective cellulose-based commercial textiles as the foundational framework. The oxygen functional groups on the textile surface facilitated ion adsorption, thereby ensuring the uniform growth of cobalt-based metal-organic frameworks (MOFs). Subsequently, the material was carbonized through heat treatment, resulting in carbon textiles with uniformly distributed cobalt nanoparticles. The obtained Co@c-Textile provided an interconnected 3D electronically conductive network and sufficient internal space for Li storage, while also exhibiting flexible film properties. Additionally, we conducted a profound investigation into the Li deposition behavior at the Co-carbon composite interface. While cobalt does not form Li alloys like some other metals do, its interaction with the carbon matrix effectively redistributes charge density, thereby enhancing Li affinity. Li preferentially deposits on charge-enriched carbon sites adjacent to cobalt, enabling uniform growth along the fiber surface. This controlled and uniform Li plating behavior allowed the Co@c-Textile@Li composite to exhibit outstanding cycling performance in full-cell configurations paired with an LFP cathode. Figure 1

Yue Ma, Jun Ma, G. Cui

Abstract Lithium-ion batteries are important energy storage devices and power sources for electric vehicles (EV) and hybrid electric vehicles (HEV). Electrodes in lithium-ion batteries consist of electrochemical-active materials, conductive agent and binder polymers. Binder works like a neural network connecting each part of electrode system and performs two major functions: the first one is to cohere active materials and conducting additive agent into integrity, as well as bind the matrix laminate and the current collector together. The second one is to build electron and ion circuits to guarantee the effective lithiation and delithiation. Therefore, binder, with minor content in electrode system, not only guarantees the integrity of electrode but also has enormous impacts on the stability and cyclic performance of electrodes. In this work, state of marketing and working mechanism of binder in electrode are introduced, conventional and multifunctional binders with rational tailor in latest years are reviewed, and the battery failure related to binders along with the maintaining challenges in battery field are discussed. What’s more, a general conclusion and instructive directions of binders for future research are presented.

Zhikai Tu, Weifeng Liu, Jin Wang et al.

Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10,000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategy for the fabrication of intelligent materials using easily available raw materials. Artificial muscles have a wide range of applications yet truly mimetic designs remain a challenge. Here, the authors use dynamic sacrificial bonds which are rearranged via a mechanical training process to optimise the characteristics of self-strengthening, strain-adaptive stiffening and actuation.

Adrian Diepolder, Mario Mueh, Susanne Brandl et al.

This article presents a novel transmission-only calibration technique for free-space quasi-optical material characterization, based on rotating the sample around its axis to vary the angle of incidence under which the sample is illuminated. In contrast to common time domain approaches, each frequency point is evaluated individually. Thus, no minimum bandwidth is required and artifacts due to time gating are prevented. In this article, two methods are presented: the first is based on self-calibration, such that all error terms are obtained by the measured sample itself. The second one, which is tailored for thin samples, requires two known standards. Since plane-wave illumination cannot be assumed for highly-focused beams, an analytical model for the coupling of arbitrary paraxial beams is developed, accounting for the lateral beam shift in case of angled samples. Thus, the presented methods are not restricted to free-space beams with high Gaussicity, allowing to employ a variety of feed antennas. Measurements in the frequency range from 220 GHz to 330 GHz of a well-known alumina sample verify the different calibration methods.

G. Muneeswari, Jhansi Bharathi Madavarapu, R. Ramani et al.

Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.

Zhenya Wang, Dmitri L. Danilov, Rüdiger‐A. Eichel et al.

Precise explanation and prediction of the aging behavior of lithium-ion batteries (LIBs) is essential for improving battery management systems. It is quickly becoming a hotspot in battery research. Solid electrolyte interphase (SEI) growth is regarded as the dominant factor of capacity losses in LIBs. However, the growth of SEI is yet to be understood in more detail due to its complexity. In the present paper, an advanced voltage-based aging model using an electron tunneling mechanism is proposed and validated by experiments. This model employs the electrode voltage as an input parameter for the first time with a tunneling mechanism, which is more flexible than existing energy-based approaches and can be used to predict the electron tunneling (dis)charge cycles. The proposed model is used to simulate tunneling current profiles during (dis)charging of graphite, LTO, and blend Si/C negative electrodes. The simulation results prove and explain that lower states-of-charge of LIBs mitigate electron tunneling and SEI growth, further reducing calendar aging. That work can be used to describe battery capacity losses better and it is crucial for predicting the state-of-health of LIBs.

Salmania Jesamine Putri, Divi Galih Prasetyo Putri, Widhy Hayuhardhika Nugraha Putra

Saat ini penggunaan perangkat lunak sudah mendominasi hampir seluruh bidang ilmu pengetahuan. Merupakan hal yang penting bagi pengembang untuk memastikan kualitas suatu perangkat lunak sehingga layak digunakan oleh khalayak umum. Kualitas perangkat lunak salah satunya dapat ditentukan dari output yang dihasilkan apakah sudah sesuai dengan kebutuhan pengguna. Pengujian perangkat lunak merupakan salah satu aktivitas yang penting dalam fase Software Development Life Cycle (SDLC) untuk memastikan perangkat lunak yang berkualitas. Terdapat berbagai metode yang mungkin digunakan dalam pengujian perangkat lunak, satu diantaranya adalah black box testing yang menguji fungsionalitas suatu sistem dan tidak mengharuskan penguji untuk memahami kode program. Dalam rangka mencapai hasil pengujian yang optimal, perlu menentukan perancangan kasus uji yang paling tepat digunakan pada suatu perangkat lunak. Equivalence Class Partitioning (ECP), Boundary Value Analysis (BVA), dan Decision Table (DT) merupakan teknik pengujian pada black box yang umum digunakan. Penelitian ini bertujuan untuk membandingkan tiga teknik tersebut, sehingga dapat menentukan teknik mana yang paling efektif diterapkan pada suatu perangkat lunak. Sampel yang digunakan untuk pengujian adalah website Lars yang merupakan aplikasi untuk membantu proses akreditasi rumah sakit. Hasil dari pengujian masing-masing teknik diukur menggunakan standard testing metrics untuk melihat teknik mana yang paling optimal. Hasil yang didapatkan penelitian ini adalah teknik ECP lebih unggul dalam menangkap kegagalan, diukur dari perhitungan matriks test case failed dengan persentase 51.8% dibandingkan teknik BVA dengan hasil 33.3% dan DT 46%.

Wei Wang, Wei Bai, Quan Zhang et al.

The boiler combustion process contains complex physicochemical changes, which is a nonlinear time-varying industrial process with strong interference and multivariate strong coupling. For this kind of boiler combustion process with typical nonlinear characteristics and complex mechanism, it is difficult to establish an accurate mechanism model by conventional modeling methods, so it is difficult to meet the new requirements for the optimal control of the boiler. The large amount of data accumulated in the operation process contains rich system information, and the data-driven modeling and control method provides an effective way for the operation optimization of the unit. Data dynamic characterization and control technology is an important means of data mining, coal-fired boiler data has obvious temporal and drift characteristics, for the current data tracking and supervision algorithms mostly lack of dynamics, real-time and stability issues, design an adaptive clustering model based on the improved growth of neural gas model (GNG), the establishment of nodes based on the probability, the range of the search, the average distance of node A node generation and deletion mechanism based on probability, range search and average distance of nodes is established to realize real-time monitoring of drift data. Finally, the experiments are carried out by analyzing the dynamic data of coal-fired boiler, and the experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can accurately and effectively monitor and control the dynamic data of coal-fired boiler.

Xiaobo Xu, Jin Shang

Facial recognition technology and recommendation systems are the main technologies in the construction of intelligent libraries, but both technologies face privacy breaches and credibility issues. Blockchain, as an emerging technology, is having significant impact in many fields. This article delves into the role and core application value of blockchain technology in the construction of smart libraries in universities, and proposes smart library service architecture based on blockchain technology. This architecture provides a value-added path for smart library services, including secure storage, resource sharing, and optimizing book borrowing and returning systems. It can improve the quality of resource services and meet the increasing service needs of readers. The effectiveness and practicality of this method have been verified through experiments.

Chien-Cheng Tseng, Su-Ling Lee

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method; the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter.