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Ultra-flexible semitransparent organic photovoltaics

npj Flexible Electronics 2023年第1期7卷 274-282页

作者： Hanbee Lee Soyeong Jeong Jae-Hyun Kim Yong-Ryun Jo Hyeong Ju Eun Byoungwook Park Sung Cheol Yoon Jong H.Kim Seung-Hoon Lee Sungjun Park Department of Electrical and Computer Engineering Ajou UniversitySuwon 16499Republic of Korea Department of Chemistry and Centre for Processable Electronics Imperial College LondonWhite City W120BZUK Department of Intelligence Semiconductor Engineering Ajou UniversitySuwon 16499Republic of Korea Advanced Photonics Research Institute(APRI) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea Division of Molecular Science and Technology Ajou UniversitySuwon 16499Republic of Korea Division of Advanced Materials Korea Research Institute of Chemical Technology(KRICT)Daejeon 34114Republic of Korea Division of Advanced materials Engineering Kongju National University1223-24 Cheonan-daeroSeobuk-guCheonan-siChungcheongnam-doRepublic of Korea

Ultra-flexible organic photovoltaics(OPVs)are promising candidates for next-generation power sources owing to their low weight,transparency,and ***,obtaining ultra-flexibility under extreme repetitive mechanical stress while maintaining optical transparency remains challenging because of the intrinsic brittleness of transparent ***,we introduce straindurable ultra-flexible semitransparent OPVs with a thickness below 2μ*** conformal surface coverage of nanoscale thin metal electrodes(<10 nm)is achieved,resulting in extremely low flexural rigidity and high strain ***-depth optical and electrical analyses on ultrathin metal electrodes showed that the devices maintain over 73%of their initial efficiency after 1000 cycles of repetitive compression and release at 66%compressive strain,and the average visible light transmittances remain higher than 30%.To our knowledge,this is the first systematical study on mechanical behaviors of strain-durable ultra-flexible ST-OPVs through precise adjustment of each ultrathin electrode thickness toward the emergence of next-generation flexible power sources.

关键词： transparency durability rigidity

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Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment

Spiking Neural Network-Based Control of an Unmanned Aerial S...

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2024 American Control Conference, ACC 2024

作者： Garcia A, Omar A. Arana, Diego Chavez Espinoza, Eduardo S. Scola, Ignacio Rubio Garcia Carrillo, Luis Rodolfo Sornborger, Andrew T. Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Conahcyt Mexico Conicet and National University of Rosario Department of Industrial Products Engineering of the National Institute of Industrial Technology Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks. © 2024 AACC.

关键词： Flight simulators

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Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders 41

Learning Latent Structures in Network Games via Data-Depende...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Xue Li, Muchen Leng, Yan Liao, Renjie Center for Applied Statistics School of Statistics Renmin University of China Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China Department of Computer Science University of British Columbia VancouverBC Canada McCombs School of Business The University of Texas at Austin AustinTX United States Vector Institute for AI Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types. Copyright 2024 by the author(s)

关键词： Monte Carlo methods

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Sequential generation of self-starting diverse operations in all-fiber laser based on thulium-doped fiber saturable absorber

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Chinese Physics B 2022年第6期31卷 87-90页

作者： Pei Zhang Kaharudin Dimyati Bilal Nizamani Mustafa M.Najm S.W.Harun School of Electrical and Information Engineering Huaihua UniversityHuaihua 418008China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua 418008China Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Advanced Technology and Multidiscipline Airlangga UniversitySurabaya 60115Indonesia Institute of Computer Science and Digital Innovation UCSI UniversityKuala LumpurMalaysia

Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.

关键词： all-fiber laser Q-switched mode-locking dark soliton fiber saturable absorber

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A 2-Bit Phase Reconfigurable Aperture Coupled Pixel Antenna

A 2-Bit Phase Reconfigurable Aperture Coupled Pixel Antenna

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Zhaoyang Ming Shanpu Shen Ross Murch Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Electrical Engineering and Electronics University of Liverpool Liverpool U.K. Institute for Advanced Study The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A novel 2-bit phase reconfigurable pixel antenna is proposed. By implementing multi-objective genetic algorithm (GA) optimization, two layers of parasitic pixel surfaces mounted with three switches are optimized to achieve a 90° phase shift. Integrated with another 1-bit aperture coupled patch radiator, the proposed pixel antenna can realize 2-bit phase reconfigurability. From 2.37 to 2.42 GHz, the phase differences of four states remain relatively stable with 2-bit phase distribution and a maximum radiation gain of 7.41 dBi. These results preliminarily verify the potential of utilizing pixel antennas to implement phase reconfiguration for beam steering applications.

关键词： Beam steering Simulation Conferences Surface mount technology Aperture coupled antennas Optimization Genetic algorithms

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

Enhancing Stock Price Prediction Through Integration of Astr...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Vishal, S. Mahapatra, Sheila Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to Be University Nitte Institute of Professional Education Karnataka Mangalore India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R2, AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day's stock price would be higher or lower than the previous day's price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability. © 2025 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

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Energetic and Smart Design of Battery Management Scheme to Improve the Lifetime of Electric Vehicles

Energetic and Smart Design of Battery Management Scheme to I...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Varatharaju, V.M. Chidambaranathan, C.M. Karthik, Mamidala Vijay Sathi, G. Pandi, V. Samuthira Shobana, D. University of Technology and Applied Science Electrical Section Engineering Department Ibra Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Cmr Engineering College Department of Electrical & Electronics Engineering Telangana Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

In order to maximize efficiency, electric vehicles use Battery Management Systems (BMS) to manage their rechargeable batteries. The battery's safety and dependability are ensured by the battery management system, and the battery's age is extended without being damaged. Various monitoring methods are used to keep track of the battery pack's charging capacity, voltage-levels, current-ratings and ecological pressure ratio. A variety of analogue and digital sensors are employed with microcontrollers for this purpose. Several powerful batteries have been developed as a result of the tremendous advances in the field of batteries over the past ten years. High-power batteries with suitable battery management systems are essential for the safe and reliable operation of Electric Vehicles (EVs). This paper will address the power sources utilized by electric automobiles and the primary problems of battery control systems, as well as compare both the lithium ion battery as well as the metal based nickel-hydride batteries in regards to growing older along with the impact caused by climate through the use of the State-of-Charge (SOC) and closed device electricity. This study's goal is to compare and contrast different intelligence techniques along with the control methods used in electric vehicle battery management platforms. The study assesses the characteristics, structure, configuration, accuracy, benefits, and downsides of intelligent algorithms for estimating the condition of a battery. Focusing on parts, characteristics, aims, outcomes, advantages, and restrictions, this research analyses the various controllers used for warming, cooling, balancing, and protecting batteries. © 2023 IEEE.

关键词： Battery Management Systems BMS Electric Vehicles EVs SOC State-of-Charge

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Analysis of Self-Supervised Speech Models on Children’s Speech and Infant Vocalizations

Analysis of Self-Supervised Speech Models on Children’s Spe...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Jialu Li Mark Hasegawa-Johnson Nancy L. McElwain Department of Electrical and Computer Engineering University of Illinois Beckman Institute for Advanced Science and Technology University of Illinois Department of Human Development and Family Studies University of Illinois

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

To understand why self-supervised learning (SSL) models have empirically achieved strong performances on several speech-processing downstream tasks, numerous studies have focused on analyzing the encoded information of the SSL layer representations in adult speech. Limited work has investigated how pre-training and fine-tuning affect SSL models encoding children's speech and vocalizations. In this study, we aim to bridge this gap by probing SSL models on two relevant downstream tasks: (1) phoneme recognition (PR) on the speech of adults, older children (8-10 years old), and younger children (1-4 years old), and (2) vocalization classification (VC) distinguishing cry, fuss, and babble for infants under 14 months old. For younger children's PR, the superiority of fine-tuned SSL models is largely due to their ability to learn features that represent older children's speech and then adapt those features to the speech of younger children. For infant VC, SSL models pre-trained on large-scale home recordings learn to leverage phonetic representations at middle layers, and thereby enhance the performance of this task.

关键词： Adaptation models Analytical models Speech recognition Self-supervised learning Speech enhancement Phonetics Encoding

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YOLOv7-ship: An Efficient Method for Marine Ship Detection Based on Wave Glider 12

YOLOv7-ship: An Efficient Method for Marine Ship Detection B...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Sun, Xiujun Zhang, Xudong Wang, Yong Sang, Hongqiang Zhou, Ying Physical Oceangraphy Laboratory Ocean University of China Qingdao China Department of Computer Science and Technology Ocean University of China Qingdao China School of Mechanical Engineering Tianjin Key Laboratory of Advanced Mechatronic Equipment Technology Tiangong University Tianjin China Institute for Advanced Ocean Study Ocean University of China Qingdao China

ISBN: (纸本)9798400707988

Ship detection based on wave gliders plays an important role in tasks such as maritime patrols, target tracking, and maritime traffic control. However, the complex and diverse sea environment, as well as the large differences in the appearance and scale of ships on the sea, have led to traditional detection algorithms experiencing issues such as false detection, missed detection of small targets, and incomplete detection of ship mast areas in ship detection tasks. To alleviate this dilemma, we designed a ship detector named YOLOv7-ship based on YOLOv7-tiny. Firstly, we introduce a parameter-free attention module (SimAM) in the backbone network to search for attention regions in the scene, thereby helping to solve the problem of false detection under complex marine environment interference. Secondly, we designed a mixed upsampling feature fusion method and specifically added a small target detection layer to enhance the detection of small targets. Finally, we introduce Wise-IOU into the loss function, and carry out different gradient gain allocation strategies in stages to solve the problem of incomplete detection of ship mast. The experimental results show that the YOLOv7-ship detector we designed achieves mAP@0.5 99.4% and 81.1% on the public dataset SeaShips and our wave glider dataset WG-Ships, respectively, while maintaining real-time inference speed and outperforming most mainstream models in performance. © 2023 ACM.

关键词： Ships

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Quantum-dot and organic hybrid light-emitting diodes employing a blue common layer for simple fabrication of full-color displays

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Nano Research 2022年第7期15卷 6477-6482页

作者： Suhyeon Lee Donghyo Hahm Suk-Young Yoon Heesun Yang Wan Ki Bae Jeonghun Kwak Department of Electrical and Computer Engineering Seoul National UniversitySeoul 08826Republic of Korea Inter-university Semiconductor Research Center(ISRC) Seoul National UniversitySeoul 08826Republic of Korea SKKU Advanced Institute of Nanotechnology(SAINT) School of Nano Science&TechnologySungkyunkwan University(SKKU)Suwon 16419Republic of Korea Department of Materials Science and Engineering Hongik UniversitySeoul 04066Republic of Korea

Colloidal quantum-dot(QD)light-emitting diodes(QLEDs)have been in the forefront of future display devices due to their outstanding optoelectronic ***,a complicated solution-process for patterning the red,green,and blue QDs deteriorates the QLED performance and limits the resolution of full-color ***,we report a novel concept of QD–organic hybrid light-emitting diodes by introducing an organic blue common layer(BCL)which is deposited through a common mask over the entire *** from the optimized device structure,red and green QLEDs retained their color coordinates despite the presence of the ***,adopting the BCL improved the external quantum efficiency of green and red QLEDs by 38.4%and 11.7%,respectively,due to the Förster resonance energy transfer from the BCL to the adjacent QD *** the BCL structure,we could simply demonstrate a full-color QD-organic hybrid device in a single *** believe that this device architecture is practically applicable for easier fabrication of solution-processed,highresolution,and full-color displays with reduced process steps.

关键词： quantum-dot light-emitting diode(QLEDs) organic light-emitting diode blue common layer full-color display reduced process step

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