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

Sihan Sun, Chenlu Wang, Sami Alghamdi et al.

Abstract Gallium oxide (Ga2O3) is an emerging ultra‐wide bandgap (UWBG) semiconductor material that has gained significant attention in the field of high voltage and high frequency power electronics. Its noteworthy attributes include a large bandgap (Eg) of 4.8 eV, high theoretical critical breakdown field strength (EC) of 8 MV cm−1, and saturation velocity (νs) of 2 × 107 cm s−1, as well as high Baliga figures of merit (BFOM) of 3000. In addition, Ga2O3 has the advantages of large‐size substrates that can be achieved by low‐cost melt‐grown techniques. This review provides a partial overview of pivotal milestones and recent advancements in the Ga2O3 material growth and device performance. It begins with a discussion of the fundamental material properties of Ga2O3, followed by a description of substrate growth and epitaxial techniques for Ga2O3. Subsequently, the contact technologies between Ga2O3 and other materials are fully elucidated. Moreover, this article also culminates with a detailed analysis of Ga2O3‐based high voltage and high frequency power devices. Some challenges and solutions, such as the lack of p‐type doping, low thermal conductivity, and low mobility are also presented and investigated in this review.

Fuat Erden, Ilhan Danaci, Salih Ozbay

Abstract Composites of polyaniline (PANI) with carbon nanotubes (CNTs) are widely studied for thermoelectric applications. In this work, acrylonitrile (AN) is incorporated into the backbone of aniline (ANI) to form a poly(ANI‐co‐AN) copolymer, which is in situ wrapped around the single‐walled carbon nanotubes (SWNTs) to enhance the thermoelectric performance. The idea is to address the well‐known inverse relationship between the Seebeck coefficient and electrical conductivity through the carrier concentration, by using the insulating nature of AN to better control the charge transport properties. The results show that the carrier concentration is reduced without deteriorating the carrier mobility in the 70% SWNT/30% poly(90ANI‐co‐10AN) composites as compared to pristine SWNT/PANI. Consequently, the highest power factor (PF) reached in this work is 201 µWm−1K−2 for the 70% SWNT/30% poly(90ANI‐co‐10AN) composite, representing a ≈1.7‐fold improvement over SWNT/PANI composites prepared under identical conditions. Further, a flexible thermoelectric generator is fabricated using SWNT/poly(ANI‐co‐AN) composite films, demonstrating a promising output power and power density of 117 nW and 43.3 µWcm−2, respectively, at a temperature difference of 30 K. These findings suggest that wrapping CNTs with copolymers comprising monomers of both conducting and insulating polymers can be a promising strategy to enhance the thermoelectric properties.

Seong‐Hwan Ryu, Hye‐Mi Kim, Dong‐Gyu Kim et al.

Abstract A way to obtain highly ordered and thermally stable crystalline In–Ga–O (IGO) thin films is reported by atomic layer deposition with novel bulky dimethyl[N‐(tert‐butyl)−2‐methoxy‐2‐methylpropan‐1‐amine] gallium precursor. The optimal cation composition for IGO (In:Ga = 4:1 at%) shows a pronounced alignment along the high c‐axis with cubic (222) orientation at a relatively low annealing temperature of 400 °C. Moreover, the crystallinity and oxygen‐related defects persist even at elevated annealing temperatures of 700 °C. Owing to its well‐aligned crystallinity, the optimal IGO thin film transistor demonstrates extremely high field‐effect mobility (µFE) and remarkable thermal stability at high temperatures of 700 °C (µFE: 96.0 → 128.2 cm2 V−1s−1). Also, process‐wise, its excellent step coverage (side: 96%, bottom: 100%), compositional uniformity in a 40:1 aspect ratio structure, superior crystal growth in vertical structures, and excellent reproducibility make it a promising candidate for application as a channel in next‐generation 3D memory devices.

Xuebao Zhang, Qingbo Wang, Ying Gao et al.

Abstract Lead halide perovskites have shown high performance in radiation detection techniques owing to their excellent optoelectronic properties and stability. However, the high resistivity of the CsPbBr3 radiation detector is intensively dependent on the growth quality of the single crystal, which is closely related to temperature gradients or the introduction of additives. Herein, a CsPbBr3 single crystal with high radiation performance is grown based on the proposed temperature‐concentration balance (TCB) method. The prepared perfect single crystal remains high quality in repeated experiments, which belongs to the Pnma space group, benefiting from the effective growth method. Based on the CsPbBr3 single crystal, the fabricated detector with the asymmetrical Au‐In electrodes demonstrates outstanding linearity under reverse bias. It exhibits a lower dark current (2.66 × 10−2 nA) and high resistivity, which helps acquire a broader radiation measurement range. Moreover, the emission spectrum of the CsPbBr3 single crystals exhibits a sharp emission peak at 527 nm and narrower full width at half maximum, making crystals easily couple into radiation detectors. These findings provide insight into the growth and regulation of CsPbBr3 crystal for more extensive applications in radiation detection in the future.

Alfred Moore, Saqib Rafique, Ciaran Llewelyn et al.

Abstract In recent years, gallium oxide (Ga2O3) has drawn considerable research interest as an ultrawide‐bandgap semiconductor due to its promising applications in the power electronics, photodetection, and gas sensing. Moreover, Ga2O3 heterojunctions have emerged as a promising approach to address key limitations of Ga2O3 as a standalone material—most notably, its lack of p‐type doping capability. One of the key application areas for Ga2O3 and its heterojunctions is ultraviolet (UV) photodetection, which has gained significant attention yet remains a relatively nascent field with vast potential for further exploration and optimization. This review provides a detailed overview of the current state‐of‐the‐art in Ga2O3 technology, highlighting recent research advancements, key challenges, and emerging strategies aimed at overcoming these challenges. Specifically, it examines Ga2O3 heterojunctions for deep‐UV photodetection, analysing compatible electrode materials and assessing various substrates suitable for Ga2O3 growth to enhance device performance. This comprehensive review is designed to serve as an essential resource for researchers and engineers working with Ga2O3‐based heterojunctions, especially for applications in UV photodetection. Written with the needs of new entrants in mind, it aims to build a robust foundational understanding of Ga2O3 technology, supporting ongoing innovation and application expansion in this field.

Subham Dutta, Pralay Kumar Karmakar

The effective inductive (L), capacitive (C), and resistive (R) behavior of a plasma sheath in a conjoint coupled form is well familiar among plasma physics communities. A dynamic sheath instability in laboratory plasmas is systematically modelled herein as an electrical series-resonance LCR circuit of the above kind. It theoretically yields experimentally observed findings on coexistent plasma sheath oscillation, electric current perturbation, and subsequent plasma sheath waves (PSWs). The plasma current in the LCR circuit formalism is allowed to undergo linear (small-scale) spatiotemporal perturbations about its homogeneous equilibrium state. The oscillating sheath triggers ion-acoustic wave excitation in the bulk plasma through sheath-induced energy transfer processes. The obtained results could be applicable mainly in understanding electromagnetic communication antennas, ion energy modulation processes, diverse plasma probe diagnostics, etc.

Henry Jiang

Physical knot theory, where knots are treated like physical objects, is important to many fields. One natural problem is to give a knot a uniform charge, and analyze the resulting electric field and electric potential. There have been some results on the number of critical points of the electric potential from knots, such as by Lipton (2021) and Lipton, Townsend, and Strogatz (2022). However, little analysis has been done on the electric field and electric potential using calculations for specific knots. We focus on torus knots, specifically a parametrization that embeds it on a torus centered at the origin with rotational symmetry about the z-axis. Particularly, in this project, we analyze the electric field along the z-axis to take advantage of symmetry. We also analyze the length of the knot as a simpler integral. We show that the electric field is zero only at the origin, and investigate the extreme points of the electric field and electric potential using numerical methods and calculations. We also demonstrate a new way to apply methods for contour integration in complex analysis to calculate the length, electric potential, and electric field, and provide an explicit approximation for the length of a torus knot.

W. Zeng

The electric Hall effect (EHE) is a newly identified Hall effect characterized by a perpendicular electric field inducing a transverse charge current in two-dimensional (2D) systems. Here, we propose a spin and valley version of EHE. We demonstrate that the transverse spin and valley currents can be generated in an all-in-one tunnel junction based on a buckled 2D hexagonal material in response to a perpendicular electric field, referred to as the electric spin Hall effect and electric valley Hall effect, respectively. These effects arise from the perpendicular-electric-field-induced backreflection phase of electrons in the junction spacer, independent of Berry curvature. The valley Hall conductance exhibits an odd response to the perpendicular electric field, whereas the spin Hall conductance shows an even one. The predicted effects can further enable the transverse separation of a pair of pure spin-valley-locked states with full spin-valley polarization while preserving time-reversal symmetry, as manifested by equal spin and valley Hall angles. Our findings present a new mechanism for realizing the spin and valley Hall effects and provide a novel route to the full electric-field manipulation of spin and valley degrees of freedom, with significant potential for future applications in spintronics and valleytronics.

Daniel C. Cabra, Gerardo L. Rossini

Multiferroic materials exhibit the coexistence of magnetic and electric order. They are at the forefront of modern condensed matter physics due to their potential applications in next-generation technologies such as data storage, sensors, and actuators. Despite significant progress, understanding and optimizing the coupling mechanisms between electric polarization and magnetism remain active areas of research. We review here a series of papers presenting a comprehensive numerical and theoretical exploration of a pantograph mechanism modeling magneto-electric coupling through lattice distortions in low dimensional multiferroic systems. These works introduce and elaborate a microscopic model where elastic lattice distortions mediate interactions between spin 1/2 magnetic moments and electric dipoles, uncovering novel physics and functionalities. The model successfully describes ubiquitous phenomena in type II improper multiferroics, particularly when dominant Ising spin components are introduced through XXZ-type rotational symmetry breaking spin interactions. We also study more realistic extensions relevant for materials with higher spin magnetic ions and to materials where magnetic couplings draw higher dimensional lattices.

Liang Lai, Zhou Gaohua

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.

Sreedhar Burada, B.E. Manjunathswamy, M. Sunil Kumar

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.

Liping Wen

Recent innovations in determining power—including the Internet of Things, M2M, online services, computational AI, and ARM-based big little multicores for encased applications—have aided in developing industrial process automation. Advanced IP-enabled, the fifth edition of Industry guidelines, are being implemented via actuators, sensing devices, and processors in machinery automation. This allows for increased degrees of competence with less human participation. Because of these novel innovations, the SCPS (Smart et al.) will be pivotal in the next industrialization phase. Multiple integrated systems with varying software are interconnected in an SCPS to perform tasks such as calculation, interaction, supervision, and action. Based on our findings, we propose an inconsistent structure for SCPS that facilitates the execution of composite workflow dynamics via electrical, pneumatic, and hydrodynamic operations. By modeling system problems, instrument postponement, operator interruption, and translation interruption, the proposed design allows for the separation of all components of an operational change, including calculation, oversight, interactions, and activation. This allows us to disentangle these many components. Voltage frequency islands (VFI) with an elevated level of flexibility are used to allocate intellectually integrated components to the many different physical operations, with the amount of flexibility varying from procedure to procedure. All modeled process parameters are fine-tuned using the Variable Volt and Fundamental Shifting methods. In the coming era of manufacturing 5.0, where human involvement is also a part of the procedure in different industries like fuel, compost, paper goods, concrete, space travel, and motor vehicle manufacturing, the suggested layout would be best suited for implementation.

Toan T. Nguyen

In plasma physics, collisionless charged particles are transported following the dynamics of a meanfield Vlasov equation with a self-consistent electric field generated by the charge density. Due to the long range interaction between particles, the generating electric field oscillates and disperses like a Klein-Gordon dispersive wave, known in the physical literature as plasma oscillations or Langmuir's oscillatory waves. The oscillatory electric field then in turn drives particles. Despite its great physical importance, the question of whether such a nonlinear particle-wave interaction would remain regular globally and be damped in the large time has been an outstanding open problem. In this paper, we propose a new framework to resolve this exact nonlinear interaction. Specifically, we employ the framework to establish the large time behavior and scattering of solutions to the nonlinear Vlasov-Klein-Gordon system in the small initial data regime. The novelty of this work is to provide a detailed physical space description of particles moving in an oscillatory field and to resolve oscillations for the electric field generated by the collective interacting particles. This appears to be the first such a result analyzing oscillations in the physical phase space $\mathbb{R}^3_x\times \mathbb{R}_v^3$.

S. Cheung, B. Tossoun, Yuan Yuan et al.

Over the past few years, extensive work on optical neural networks has been investigated in hopes of achieving orders of magnitude improvement in energy efficiency and compute density via all-optical matrix-vector multiplication. However, these solutions are limited by a lack of high-speed power power-efficient phase tuners, on-chip non-volatile memory, and a proper material platform that can heterogeneously integrate all the necessary components needed onto a single chip. We address these issues by demonstrating embedded multi-layer HfO2/Al2O3 memristors with III-V/Si photonics which facilitate non-volatile optical functionality for a variety of devices such as Mach-Zehnder Interferometers, and (de-)interleaver filters. The Mach-Zehnder optical memristor exhibits non-volatile optical phase shifts > π with ~33 dB signal extinction while consuming 0 electrical power consumption. We demonstrate 6 non-volatile states each capable of 4 Gbps modulation. (De-) interleaver filters were demonstrated to exhibit memristive non-volatile passband transformation with full set/reset states. Time duration tests were performed on all devices and indicated non-volatility up to 24 hours and beyond. We demonstrate non-volatile III-V/Si optical memristors with large electric-field driven phase shifts and reconfigurable filters with true 0 static power consumption. As a result, co-integrated photonic memristors offer a pathway for in-memory optical computing and large-scale non-volatile photonic circuits. Stanley Cheung and co-authors introduce co-integrated III-V/Si memristors with fundamental photonic building blocks used in both communication and computing applications. This allows a path towards realizing low-loss, non-volatile optical elements with near-zero static power consumption.

Ł. Kolimas, S. Łapczyński, M. Szulborski et al.

FEM (finite element method) is an essential and powerful numerical method that can explicitly optimize the design process of electrical devices. In this paper, the employment of FEM tools such as SolidWorks, COMSOL and ANSYS is proposed in order to aid electrical apparatuses engineering and modeling – those are arc chambers of modular circuit breakers. Procured models of arc chambers have been undergoing simulations concerning heating, electric potential distribution, electric charge velocity and traverse paths. The data acquired has been juxta-positioned against experimental data procured in the Short-Circuit Laboratory, Warsaw University of Technology. The reflection of the theoretical approach was clearly noted in the experimental results. Mutual areas of the modeled element expressed the same physical properties and robustness errors when tested under specific conditions – faithfully reflecting those which were experimented with. Moreover, the physical phenomena essential for electrical engineering could be determined already at the model stage. This procedure proved highly valuable during designing/engineering work in terms of material economy.

Y. Hane, Kenji Nakamura

Quantitative analysis of the iron loss taking the dynamic hysteresis behavior into account is essential to the development of high-efficiency electric machines. In a previous paper, a novel magnetic circuit model incorporating a play model and a Cauer circuit was proposed. It was demonstrated that this magnetic circuit model can accurately and fast calculate the hysteresis loops and the iron loss considering carrier harmonics under PWM excitation for magnetic reactors made of several kinds of core materials. This paper describes that the iron loss of a permanent magnet synchronous motor (PMSM) driven by a PWM converter is calculated with high-accuracy and high-speed by combining a reluctance network analysis (RNA) model of an entire PMSM and a one-dimensional (1-D) magnetic circuit model representing the dynamic hysteresis characteristics in each element of an RNA model.

Y. Liu, C. Li, Zhe Guo et al.

To study sweep frequency impedance (SFI) features of short-circuit (SC) faults easily, this paper proposes a broadband electric circuit model of a transformer winding and solves its three key problems. The first problem is the calculation of lumped-circuit parameters considering frequency-dependent complex anisotropic permeabilities (FDCAPs), which are caused by the physical characteristics, such as skin, proximity, and geometrical effects and anisotropic properties, of the transformer core and winding materials. The other issue is the establishment of the electric circuit model based on the SFI measurement connection mode, the transformer winding parameters, and a double-ladder network (DLN). Another issue is the construction of the state-space model of the electric circuit toward different SFI values to obtain all network branch voltages and currents. The accuracy of the proposed model is assessed by comparing its SFI signatures with those of the simulation model, without considering FDCAPs under healthy winding, and the corresponding physical transformer model during healthy winding and SC faults. It is observed that the SFI results of the proposed model are closer to the experimental measurements, and the model can be effectively used to study the SFI features of SC faults. Moreover, the impacts of different types of SC faults on the SFI data are concluded in this paper.

Shameer Mohammed, S. Nanthini, N. Bala Krishna et al.

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture.

Puladasu Sudhakar, Nitin K. Kamble, Geetha K et al.

Researchers desperately ought to build an efficient turbine problem detection & simulation environment given the quick increase in wind energy capacity and the steadily rising operational cost duration of wind generators. The two aspects of fault identification & fault prediction are employed to describe the primary problem features of wind generators. We examine and synthesize the studies on fault detection techniques that rely on vibrating, electrical impulse evaluation, and pattern matching to address the challenging difficulties of defect detection. During the same period, we highlight the computationally efficient, constraints, and potential directions of distinct approaches. We review recent study developments & suggest a defect forecasting model that utilizes a physical breakdown prototype with data analysis theory fused depending on the mechanics and electronic subsystem degradation features of wind energy. In this study, we employ the Internet of Things (IoT) on Deep Learning (DL) architecture to forecast and identify wind energy generation issues. The testing findings demonstrate the method's ability to diagnose faults logically and forecast their types.

Khalid Lodhi, Jayant Chhillar, Sumit J. Darak et al.

5G Cell Search (CS) is the first step for user equipment (UE) to initiate communication with the 5G node B (gNB) every time it is powered ON. In cellular networks, CS is accomplished via synchronization signals (SS) broadcasted by gNB. 5G 3rd generation partnership project (3GPP) specifications offer a detailed discussion on the SS generation at gNB, but a limited understanding of their blind search and detection is available. Unlike 4G, 5G SS may not be transmitted at the center of carrier frequency, and their frequency location is unknown to UE. In this work, we demonstrate the 5G CS by designing 3GPP compatible hardware realization of the physical layer (PHY) of the gNB transmitter and UE receiver. The proposed SS detection explores a novel down-sampling approach resulting in a 60% reduction in on-chip memory and 50% lower search time. Via detailed performance analysis, we analyze the functional correctness, computational complexity, and latency of the proposed approach for different word lengths, signal-to-noise ratio (SNR), and down-sampling factors. We demonstrate end-to-end 5G CS using GNU Radio-based RFNoC framework on the USRP-FPGA platform and achieve 66% faster SS search compared to software. The 3GPP compatibility and demonstration on hardware strengthen the commercial significance of the proposed work.