Hasil untuk "Electronics"

D. Kuzum, Shimeng Yu, H. Philip Wong

Steve Park, Michael Vosguerichian, Z. Bao

Suk-Won Hwang, H. Tao, Dae-Hyeong Kim et al.

Zhihong Chen, Yu-Ming Lin, M. Rooks et al.

We have fabricated graphene nano-ribbon field-effect transistor devices and investigated their electrical properties as a function of ribbon width. Our experiments show that the resistivity of a ribbon increases as its width decreases, indicating the impact of edge states. Analysis of temperature-dependent measurements suggests a finite quantum confinement gap opening in narrow ribbons. The electrical current noise of the graphene ribbon devices at low frequency is found to be dominated by the 1/f noise.

M. Irimia‐Vladu

"Green" electronics represents not only a novel scientific term but also an emerging area of research aimed at identifying compounds of natural origin and establishing economically efficient routes for the production of synthetic materials that have applicability in environmentally safe (biodegradable) and/or biocompatible devices. The ultimate goal of this research is to create paths for the production of human- and environmentally friendly electronics in general and the integration of such electronic circuits with living tissue in particular. Researching into the emerging class of "green" electronics may help fulfill not only the original promise of organic electronics that is to deliver low-cost and energy efficient materials and devices but also achieve unimaginable functionalities for electronics, for example benign integration into life and environment. This Review will highlight recent research advancements in this emerging group of materials and their integration in unconventional organic electronic devices.

Xiongfei Wang, F. Blaabjerg, Weimin Wu

M. Bendikov, F. Wudl, D. Perepichka

A. Yariv, P. Yeh

P. Horowitz, Winfield Hill, R. J. Rollefson

Xingguo TAN, Chaomeng LI, Gaoming FENG et al.

To address the issue of low transmission efficiency in dual active bridge (DAB) converters during electric vehicle charging and discharging processes, a minimum current stress optimization control strategy combining the differential extremum method with segmented control is proposed. This strategy effectively optimizes current stress and suppresses backflow power under soft-switching constraints, thereby significantly improving transmission efficiency. Firstly, taking forward power transmission as an example, the conditions for achieving zero voltage soft-switching for all switches in two operation modes under extended phase shift (EPS) control are derived, and the mechanism of backflow power generation is analyzed, elucidating how reducing current stress contributes to its suppression. Subsequently, the optimization phase-shift combinations for minimum current stress are derived using the differential extremum method, and a segmented control scheme is implemented based on the soft-switching ranges of different modes. Finally, experimental results demonstrate that when the voltage conversion ratio is greater than 1, the proposed strategy achieves soft-switching for all switches across the full power range, while effectively reducing current stress and suppressing backflow power, leading to a significant improvement in transmission efficiency. However, when the voltage conversion ratio is less than 1, while current stress is still reduced, zero voltage switching cannot be achieved for all switches.

Mintao Xue, Bing Chen, Jinyang Li et al.

Ta-based refractory alloys offer excellent high-temperature strength, but high W content significantly reduces room-temperature tensile ductility. Here for Ta-14W alloy with fracture strain of ∼5%, we demonstrate that a minor Hf addition significantly improves its ductility to over 30%. Firstly, Hf induces an asymmetric core structure of screw dislocation and reduces generalized stacking fault energy (GSFE), thereby promoting dislocation mobility and dislocation multiplication during plastic deformation. Secondly, Hf can strengthen grain boundaries (GBs) by preferentially segregating near GBs. Thirdly, Hf binds with O atoms to form HfO2 particles at GBs, lowering the risk of oxygen embrittlement. All these mechanisms offer insightful guide for the development of advanced Ta-W-based refractory alloys.

Francisco Suarez, Amin Nozariasbmarz, D. Vashaee et al.

Kuiwu Wang, Qin Zhang, Zhenlu Jin et al.

Abstract Multi‐sensor networks often encounter challenges such as inconsistent sampling times among local sensors and data loss during transmission. To address these issues, this paper employs a data loss compensation strategy to reconstruct missing data information. It designs the state estimation of local sensors utilising iterative state equations, leveraging multistep prediction techniques to estimate sensor states at unsampled points, thereby transforming the asynchronous sensor network system into a synchronous one. Furthermore, the projection theorem is applied to determine the fusion weights of local sensors, grounded on the principle of square‐averaging significance. Ultimately, data information pertaining to the same target is fused through arithmetic averaging, guided by distance correlation. Simulation outcomes demonstrate that the proposed algorithm balances estimation accuracy with communication overhead, achieved by designing an optimal number of communication iterations.

Kun Fu, Zhengyang Wang, J. Dai et al.

Xianping Chen, H. Ye, Xuejun Fan et al.

W. Kang, Yangqi Huang, Xichao Zhang et al.

Yasser Khan, M. Garg, Qiong Gui et al.

Muhammad Hussain

This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural components. Subsequently, the review highlights key architectural innovations introduced in each variant, shedding light on the incremental refinements. The review includes benchmarked performance metrics, offering a quantitative measure of each variant’s capabilities. The paper further presents the performance of YOLO variants across a diverse range of domains, manifesting their real-world impact. This structured approach ensures a comprehensive examination of YOLOs journey, methodically communicating its internal advancements and benchmarked performance before delving into domain applications. It is envisioned, the incorporation of concepts such as federated learning can introduce a collaborative training paradigm, where YOLO models benefit from training across multiple edge devices, enhancing privacy, adaptability, and generalisation.

Wenbo Zhao, Xinghua Cheng, Dezhi Wang et al.

Abstract In the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

Nurezayana Zainal, Mohanavali Sithambranathan, Umar Farooq Khattak et al.

Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm.