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

Xiangru Chen, Jien Wu, Xingyu Chen et al.

Non-Hermitian systems generally host complex spectra that bring unique spectral topologies, leading to the spectral braiding and non-Hermitian skin effect. The experimental exploration of non-Hermitian physics is mainly concentrated in artificial systems due to the flexibility in the introduction of the non-Hermiticity, but to date has focused only on the systems without gauge fields or with Abelian gauge fields. Here, we propose a non-Abelian Hatano-Nelson model with a nonreciprocal U(2) gauge field. The gauge field induces two non-Hermitian phenomena: the first is the Hopf-link-shaped complex energy braiding, and the second is the bipolar skin effect arising under the non-Abelian condition. The non-Abelian Hatano-Nelson model is implemented in electric circuits, and the Hopf-link-shaped admittance spectra and bipolar skin admittance modes are observed. Our work enriches the experimental non-Hermitian physics, and provides an approach to designing multifunctional non-Hermitian devices.

Babar Ali, Ghulam Murtaza, Hafiz Muhammad Bilal et al.

This work presents a cost-effective optical frequency comb generator (CEOFCG) solution for generating multiple, equally spaced carriers in wavelength-division-multiplexing coherent optical fiber communication systems (WDM-COFCS). It enables the replacement of multiple laser sources with a single continuous-wave laser, eliminating the need for additional amplification and filtering setups. The CEOFCG provides stable multicarrier spacing, broad phase coherence, and compatibility with advanced modulation formats, enhancing the performance of WDM-COFCS. Digital signal processing (DSP) techniques, including digital filtering, detection, and impairment compensation, contribute to high transmission and spectral efficiency (SE). The results demonstrate the potential of CEOFCG in achieving cost reduction, complexity reduction, high SE, and optimal utilization of optical fiber bandwidth, particularly in higher-order QAM-based COFCS.

Ahmed Hamouda Elsayed, Samir Abozyd, Abdelrahman Toraya et al.

On-chip optical accelerometers can be a promising alternative to capacitive, piezo-resistive, and piezo-electric accelerometers in some applications due to their immunity to electromagnetic interference and high sensitivity, which allow for robust operation in electromagnetically noisy environments. This paper focuses on the characterization of an easy-to-fabricate tri-axial fiber-free optical MEMS accelerometer, which employs a simple assembly consisting of a light emitting diode (LED), a quadrant photodetector (QPD), and a suspended proof mass, measuring acceleration through light power modulation. This configuration enables simple readout circuitry without the need for complex digital signal processing (DSP). Performance modeling was conducted to simulate the LED’s irradiance profile and its interaction with the proof mass and QPD. Additionally, experimental tests were performed to measure the device’s mechanical sensitivity and validate the mechanical model. Lateral mechanical sensitivity is obtained with acceptable discrepancy from that obtained from FEA simulations. This work consolidates the performance of the design adapted and demonstrates the accelerometer’s feasibility for practical applications.

T. Swathi Priyadarshini, Mohd Abdul Hameed

Our research aims to present a comprehensive study of machine learning algorithms and deep learning advancements in medical field systems for decision making. Present study examines the idea of extracting most important risk factors from given medical data, which has major impact in the increase of severity condition of heart stroke. Three experimental prediction models are developed when k-means clustering is collaborated with classification which includes machine learning algorithms like Naïve Bayes, Decision Tree and a deep learning algorithm Artificial Neural Network. A detailed comparison analysis is done by evaluating performance metrics like sensitivity, specificity, accuracy, and AUC-ROC scores. Out of the three, k-means with Artificial Neural Network model outperformed with sensitivity 0.89, specificity 0.89, and accuracy of 0.90 in comparison with machine learning classifiers. The challenges of perfect balancing of sensitivity and specificity is achieved by AUC-ROC score of 0.96, which is the best possible result till now.

Stoyan Stoyanov, Razia Sulthana, Tim Tilford et al.

Thermo-mechanical finite element (FE)-based simulation technology has been used extensively for virtual prototyping and to predict material degradation and thermal fatigue damage in electronics assembly materials. However, from an end-user point of view, the deployment of such high-fidelity modelling is not straightforward as it requires comprehensive device and material characterisation data that is not readily available through technical datasheets and must be gathered using costly and time-consuming bespoke characterisation tests and access to metrology instruments. In addition to that, FE modelling requires access to advanced software and specialised FE skill sets. Here, a novel physics-informed Machine Learning (ML) approach for developing computationally fast metamodels for predicting fatigue damage and its spatial distribution at common failure sites of power electronics components is developed, validated and demonstrated. The significance of this work is in the attributes and the capabilities of the proposed modelling technology that enable the end-users of power components to perform insightful model-based assessments of the thermal fatigue damage in the assembly materials due to different application-specific, qualification and user-defined load conditions, removing current requirements for comprehensive device characterisations and deploying complex finite element models. The proposed methodology is demonstrated with two different metamodel structures, a regression decision tree and a neural network, for the problem of predicting the thermal fatigue damage in wire bonds of insulated-gate bipolar transistor (IGBT) power electronics modules (PEMs) exposed to passive temperature cycling loads. The results confirmed that the proposed approach and the modelling technology could offer FE model substitution and the capability to spatially map highly nonlinear three-dimensional spatial distributions of the damage parameter over local sub-domains associated with material fatigue degradation and failure.

Yang Shibiao, Zhong Yu, Huang Lei et al.

During the transmission process, the stay wire tower plays an important role in supporting and stabilizing. By monitoring the tension of the stay wire, the deformation and abnormal force of the stay wire tower structure can be detected in a timely manner, and measures can be taken to adjust and repair it. In terms of wire tension monitoring, the main methods currently used include various monitoring technologies such as electronic, mechanical, and optical. However, traditional cable force monitoring methods have problems such as low monitoring accuracy and poor real-time performance, so it is necessary to develop a real-time detection technology based on sensors. This article uses computer technology to achieve real-time detection of transmission line pulling force based on sensors. Firstly, select a suitable sensor for measuring the pulling force. Then, the sensor data is transmitted to the computer system for real-time processing and analysis. Finally, the results are displayed through the interface, facilitating real-time monitoring and control by monitoring personnel. After experimental verification, the real-time detection technology based on sensors proposed in this article can accurately monitor the pulling force of transmission lines. The monitoring results show that this technology has high accuracy and good real-time performance, and can timely detect and handle abnormal wire force situations.

Peichen Zhong, Dongjin Kim, Daniel S. King et al.

Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO$_3$ perovskite. This work thus extends the capability of MLIPs to predict electrical response--without training on charges or polarization or BECs--and enables accurate modeling of electric-field-driven processes in diverse systems at scale.

Elshad Allahyarov, Hartmut Löwen

A nanocomposite film containing highly polarizable inclusions in a fluid background is explored when an external electric field is applied perpendicular to the planar film. For small electric fields, the induced dipole moments of the inclusions are all polarized in field direction, resulting in a mutual repulsion between the inclusions. Here we show that this becomes qualitatively different for high fields: the total system self-organizes into a state which contains both polarizations, parallel and antiparallel to the external field such that a fraction of the inclusions is counter-polarized to the electric field direction. We attribute this unexpected counter-polarization to the presence of neighboring dipoles which are highly polarized and locally revert the direction of the total electric field. Since dipoles with opposite moments are attractive, the system shows a wealth of novel equilibrium structures for varied inclusion density and electric field strength. These include fluids and solids with homogeneous polarizations as well as equilibrium clusters and demixed states with two different polarization signatures. Based on computer simulations of a linearized polarization model, our results can guide the control of nanocomposites for various applications, including sensing external fields, directing light within plasmonic materials, and controlling the functionality of biological membranes.

Yusheng Hou, Ruqian Wu

Two-dimensional (2D) magnetism in atomically thin van der Waals (vdW) monolayers and heterostructures has attracted significant attention due to its promising potential for next-generation spintronic and quantum technologies. A key factor in stabilizing long-range magnetic order in these systems is magnetic anisotropy, which plays a crucial role in overcoming the limitations imposed by the Mermin-Wagner theorem. This review provides a comprehensive theoretical and experimental overview of the importance of magnetic anisotropy in enabling intrinsic 2D magnetism and shaping the electronic, magnetic, and topological properties of 2D vdW materials. We begin by summarizing the fundamental mechanisms that determine magnetic anisotropy, emphasizing the contributions from strong ligand spin-orbit coupling of ligand atoms and unquenched orbital magnetic moments. We then examine a range of material engineering approaches, including alloying, doping, electrostatic gating, strain, and pressure, that have been employed to effectively tune magnetic anisotropy in these materials. Finally, we discuss open challenges and promising future directions in this rapidly advancing field. By presenting a broad perspective on the role of magnetic anisotropy in 2D magnetism, this review aims to stimulate ongoing efforts and new ideas toward the realization of robust, room-temperature applications based on 2D vdW magnetic materials and their heterostructures.

Jakob Schmitt, Ivo Horstkötter, Bernard Bäker

The novel trajectory correction hysteresis model (TCH) is based on measuring the first-order reversal branches (FORBs). As the enormous measurement effort required for parameterisation hinders a real-world application, this paper presents the data-efficient transfer fit (TF) method. The TF methodology is validated through two application cases: ageing update and cell chemistry adaptation. Remarkably, using only 12 measurement points on the open-circuit voltage (OCV) envelopes instead of hundreds of measurement data points, the ageing update TF model attains a mean absolute error (mae) of 4.1 mV, closely approaching the accuracy of a newly parameterised target model (3.6 mV mae). Similarly, adapting an NCA cell model to an NMC target cell using selected OCV envelope points yields a 5.3 mV mae, which further reduces to 3.2 mV with an additional discharge FORB starting at 10% SOC. In addition to the selective model adjustment using continuous OCV measurement trajectories, the much more realistic adaptation by measurement points randomly distributed within the hysteresis window was successfully demonstrated. The presented TF methodology overcomes the hurdle of data efficiency while maintaining model accuracy and paves the way for the future application of the TCH model for voltage-based SOC correction.

G.R. Nikhade, P. Khandelwal, Pravinkumar Sonsare et al.

Detecting cracks from images using embedded deep learning applications requires efficient and lightweight models in practice. To improve the computational efficiency of models, it is generally aim to reduce the model parameters as much as possible without compromising accuracy. Computational approaches ensure consistency in crack detection across different inspections and operators. Computational methods enable continuous monitoring, including real-time or periodic inspections. The proposed work seeks to leverage the latest deep-learning techniques to get the maximum information out of a minimum number of parameters. The present semantic segmentation-based model - CrackJPU, uses deep hierarchical feature learning convolution networks. Deeply-Supervised Nets (DSN) and JPU (Joint Pyramid Upsampling) modules are also used to supervise the model at multiple inner side-output layers and facilitate retrieval of lower resolution features at decoding layers respectively. To refine the prediction result, the guided filtering method is used. The proposed model has been trained on a standard dataset of annotated crack images. The experimental finding shows that, the model has less than 7 million parameters which are the least compared to recent work without losing performance. Also a mean I/U score of 98.78 and the best F-score is 86.4 is achieved with reduction model parameters. Crack detection is significant in various fields like infrastructure inspection, aerospace industry, Manufacturing Quality Control etc. due to its potential impact on safety, infrastructure integrity, and overall system reliability.

Zhihua Lin, Frederik Bettels, Taoran Li et al.

Abstract Strategies that aim to achieve highly stable lithium metal batteries (LMBs) are extensively explored. To date, the controlled formation of high‐quality inorganic SEI is still quite challenging, which requires a deep understanding and hence the fine‐tuning of solvation chemistry by using functional additives in the electrolyte. In this work, a high amine‐containing 1,2,4,5‐benzenetetramine tetrahydrochloride (BHCL) is developed as a dual‐function electrolyte additive for LMBs. The amine group with a high donor number increases the lithium affinity, while the phenyl group with a strong inductive effect prevents the decomposition of solvents, and the free chloride ions replace anions mediating the formation of the rigid inorganic LiCl‐rich SEI layer. The experimental results corroborate the theoretical findings. The modified Li||Li symmetric battery is stably cycled for over 2500 h at 1 mA cm−2 current density with an overpotential of ≈45 mV. The performances of the Li||Cu and Li||LFP cells are also significantly enhanced. Therefore, this work provides a promising design principle of multifunctional electrolyte additive.

Mehrshad Shokati, Parisa Mohammadi, Atoosa Amirinian

In response to global warming and energy shortages, there has been a significant shift towards integrating renewable energy sources, energy storage systems, and electric vehicles. Deploying electric vehicles within smart grids offers a promising solution to reduce carbon emissions. However, managing the charging and discharging processes of them as distributed power supplies present significant challenges. Additionally, the intermittent nature of renewable energy, uncertainties in electric vehicle-related parameters, fluctuating energy prices, and varying loads make maintaining stable power system operations more complex. Effective management systems for electric vehicle battery charging are crucial to coordinating these processes and ensuring a secure, efficient, and reliable power system. Reinforcement learning, enhanced by deep learning, has gained substantial interest for its model-free approach and real-time optimization, effectively managing electric vehicle charging by maximizing cumulative rewards. This review synthesizes existing literature on reinforcement learning-based frameworks, objectives, and architectures for electric vehicle charging coordination strategies in power systems, classifying methods into centralized and decentralized categories. Additionally, the article offers suggestions for future research directions to further enhance reinforcement learning-based electric vehicle charging optimization.

S.M.H. Sithi Shameem Fathima, K.A. Jyotsna, Thiruveedula Srinivasulu et al.

In recent years biometrics play a vital role in recognizing the person and authentication. Recent studies prove that the gait cycle is unique for every individual. Gait refers to the walking pattern of an individual. Gait cycle is calculated by the right toe on the same right toe on period. Human Gait cycle and the angles calculated from head-to-toe portions of a person are important measures for both habitual and clinical analysis purposes. In most cases these parameters would furnish sufficient details for further deep learning and analysis serving as medical clues. These parametersprovide useful and validated results for medical rehabilitation. This paper proposes evaluation of Gait cycle for abnormal walking pattern of different diseases. The graphs were plotted for Gait cycle versus knee flexion angle and gait cycle versus hip to ankle angle variations which provide sufficient information for classifying normal and abnormal walk pattern. The main idea proposed here is to classify the different walk patterns without the involvement of medical tools. This proposed work will be helpful in obtaining the required and initial clinical details of persons by means of their gait without any direct medical investigation on them and facilitates in classifying different abnormal walk patterns.

Dexing Liu, Wanting Wang, Jiaona Zhang et al.

Abstract A single‐device method is reported for extracting gate‐ and/or drain‐voltage‐dependent contact resistance of thin‐film transistors (TFTs). An extended transition‐voltage method is proposed and verified by experiments of all‐carbon‐nanotube thin‐film transistors (ACNT‐TFTs), which can extract gate‐ and/or drain‐voltage‐dependent contact resistance at source and drain independently. By measuring the output and transfer characteristics of a single‐device and extracting the basic parameters with the aid of mature Y‐function method, the contact resistance can be calculated directly. The results show that although a slight Schottky contact behavior is exhibited at very small drain voltages, good electrical contact characteristics can still be obtained in ACNT‐TFTs, exhibiting quasi‐Ohmic contacts. Compared with the existing single‐device methods, this method is suitable for both Ohmic and Schottky contact scenarios without requiring a complex iteration process, which greatly improves the universality and efficiency of the contact resistance extraction. Besides, this method reveals the physical essence of the complex interface contacts and enables researchers to quantitatively analyze the contact performance, not only for network carbon nanotube TFTs but also for the other emerging transistors.

Ali Abed Salman, Mohammed Ali Abdulrehman, Ismail Ibrahim Marhoon et al.

This study was conducted in order to examine the production of Plaster of Paris (POP) in green and red colors. For this purpose, green and red pigments were prepared based on iron oxide and chromium oxide, respectively. To investigate the effect of pigments addition, the iron oxide and chromium oxide were added in four percentages by weight (0%, 2%, 4% and 6%). The colored POP mixes were investigated under mechanical and physical tests at the age of 28 days. These tests included plaster compressive strength, flexural strength and hardness of plaster. Also, the standard consistency test and the expansion test were conducted for the POP mixes. Based on the experimental results, it has been found that the 2% of pigments addition showed the best volumetric soundness over other ratios of additions. Moreover, The experimental program of the current study revealed that the coloring process has no any detrimental effect on the fresh and the hardened properties of the POP mixes.

Zhixuan Cheng, Xionghui Jia, Xing Cheng et al.

Abstract 2D MoTe2 is regarded as a favorable candidate for semiconductor nanoelectronics integration. Chemical‐vapor‐deposition‐grown MoTe2 usually presents p‐type characteristics. In order to realize basic electronic units like complementary metal‐oxide‐semiconductor (CMOS) inverter, controllable fabrication of p‐ and n‐type transistors at large scale is of vital importance. Here, large‐scale MoTe2 n‐channel field‐effect transistor (n‐FET) arrays are successfully fabricated with seamless coplanar metallic 1T′‐WTe2 contacts to reduce contact resistance. High‐k HfO2 serves as a gate dielectric and its atomic‐layer‐deposition (ALD) process causes an n‐doping effect on the 2H‐MoTe2 channel. The FETs perform typical n‐type characteristics with average electron density and on/off ratio of ≈1.7 × 1013 cm−2 and 2.1 × 104, respectively. Furthermore, large‐scale homogeneous CMOS inverter arrays are fabricated, showing clear logic swing with low power consumption (≈0.4 nW) and high device yield (≈92%). Notably, their voltage transfer characteristics exhibit small hysteresis, and they work well after being kept in air for 16 months, indicating high device stability. The statistical results show that both the n‐FETs and CMOS inverters have high uniformity and reliability in performance. Significantly, this fabrication method is free from transfer processes and compatible with traditional silicon technology. This work paves the way for the application of few‐layer MoTe2 in semiconductor nanoelectronics integration.

Yudhajit Ray, Shreyas Sen

Recent advancements in low power and low noise front-end amplifiers have made it possible to support high-speed data transmission within the deep roll-off regions of conventional wireline channels. Despite being primarily limited by inter-symbol-interference (ISI), these legacy channels also require power-consuming front-end amplifiers due to increased insertion-loss at high frequencies. Wireline-like broadband channels, such as proximity communication and human-body-communication (HBC), as well as multi-lane, densely-packed channels, are further constrained by their high loss and unique channel responses which cause the received signal to be noise-limited. To address these challenges, this paper proposes the use of a discrete-time integrating amplifier as a low power <1 pJ/b using 65nm CMOS up to 5-6 Gb/s) alternative to traditional continuous-time front-end amplifiers. Integrating amplifiers also reduce the effects of noise due to its inherent current integrating process. The paper provides a detailed mathematical analysis of gain of two conventional and three novel and improved integrating amplifiers, accurate input referred noise estimations, signal-to-noise ratio, and a comparison of the integrating amplifier’s performance with that of a low-noise amplifier. The analysis identifies the most optimum integrator architecture and provides comparison with simulated results. This paper also develops theoretical expressions and provides in-depth understanding of input referred noise, while supporting them by simulations using 65nm CMOS technology node. Finally, a comparative analysis between low-noise amplifier and discrete-time integrating amplifier is presented to demonstrate power and noise benefits for both legacy and wireline-like channels, while providing an easier design space as integrator provides two-dimensional controllability for gain.

Koji Shimizu, Ryuji Otsuka, Masahiro Hara et al.

Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion transport. In this study, we propose a neural network (NN) model to predict the Born effective charges of ions along an axis parallel to an applied electric field from atomic structures. The proposed NN model is applied to Li$_3$PO$_4$ as a prototype. The prediction error of the constructed NN model is 0.0376 $e$/atom. In combination with an NN interatomic potential, molecular dynamics (MD) simulations are performed under a uniform electric field of 0.1 V/angstrom, whereby an enhanced mean square displacement of Li along the electric field is obtained, which seems physically reasonable. In addition, the external forces along the direction perpendicular to the electric field, originating from the off-diagonal terms of the Born effective charges, are found to have a nonnegligible effect on Li migration. Finally, additional MD simulations are performed to examine the Li motion in an amorphous structure. The results reveal that Li migration occurs in various areas despite the absence of explicitly introduced defects, which may be attributed to the susceptibility of the Li ions in the local minima to the electric field. We expect that the proposed NN method can be applied to any ionic material, thereby leading to atomic-scale elucidation of ion behaviour under electric fields.

Teruo Matsushita