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Ferroelectrically gated two-dimensional bismuth oxyselenides for strain-invariant flexible synaptic thin-film transistors

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science China(information sciences) 2025年第5期68卷 396-403页

作者： Jie WEN Fei XIAO Zheng-Dong LUO Dongxin TAN Xuetao GAN Dawei ZHANG Zhufei CHU Yinshui XIA Yan LIU Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University School of Materials Science and Engineering The University of New South Wales ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) The University of New South Wales Faculty of Electrical Engineering Computer Science Ningbo University

Two-dimensional van der Waals（2D vdW） semiconductors have proven to be of great importance for flexible thinfilm transistors（TFTs） owing to their intrinsic mechanical flexibility and superior electronic *** particular,bismuth oxyselenide（Bi2O2Se）,featuring ultrahigh electron mobility along with facile scalable thin-film growth methods,could offer a new option to deliver massively enhanced potential for flexible ***,it has remained a challenge to achieve nonvolatile flexible memory devices based on Bi2O2Se TFTs,thereby hindering the extension of Bi2O2Se TFTs to storage and emerging neuromorphic computing ***,a flexible synaptic TFT is demonstrated through the creation of a Bi2O2Se-based ferroelectric field-effect transistor（FeFET） structure on the flexible mica *** proposed device exhibits excellent nonvolatile memory characteristics,including a large memory window,excellent current modulation ratio,great retention,and strong ***,the Bi2O2Se-based FeFET can be operated as a synaptic device with analog conductance-modulating *** to the superior mechanical flexibility of the component materials and the mica substrate,the Bi2O2Se-based FeFETs can retain their performance against various bending states,showing a straininvariant electrical *** study marks the advancement of Bi2O2Se-based TFTs toward flexible nonvolatile memories and synaptic devices.

关键词： Bi$_{2}$O$_{2}$Se FeFET nonvolatile memory flexible device synaptic transistor

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Lithium battery SOH and RUL prediction based on time-frequency multi-feature extraction and fusion networks 5

Lithium battery SOH and RUL prediction based on time-frequen...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Xiaohua Dai, Ke Hu, Min Ni, Nanbing Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373820

Accurate prediction of battery state of health (SOH) and remaining useful life (RUL) is crucial for reducing the risk of lithium battery failure and intelligent management of energy storage power plants. Currently, most of the existing research methods only take the features between charge/discharge cycles as model inputs, ignoring the interconnectivity of feature data within the battery cycle. A model based on time-frequency multi-feature extraction and fusion (TFMEFNet) is proposed to improve the accuracy of lithium-ion battery SOH and RUL prediction. The time-frequency multi-feature extraction module is used to extract features of time and frequency domains respectively from the data within the discharge cycle, and then the global, time-domain and frequency-domain features are fused by the feature fusion module. Long-term dependencies in continuous data then captured by a TCN-GRU (Temporal Convolutional Network-Gated Recurrent Unit) module. A Temporal Attention module is used to better represent the decaying trend of the capacity series, weighted by attention, resulting in more accurate predictions. Extensive experiments are conducted on NASA datasets and compared with existing methods. The experimental results show that the proposed model can predict the SOH and RUL of Li-ion batteries more accurately. The root mean square error (RMSE) on the NASA dataset is less than 0.786%. © 2024 IEEE.

关键词： Feature extraction

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Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

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computer Systems science & engineering 2023年第2期44卷 1531-1549页

作者： N.Krishnammal C.Kalaiarasan A.Bharathi Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia School of Engineering Presidency UniversityBengaluruIndia Department of Information Technology Bannari Amman Institute of TechnologySathyamangalamIndia

Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as signiﬁcant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identiﬁed that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efﬁciency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.

关键词： Wireless mesh access networks mobile ad-hoc network reinforcement learning multi-objective constraint asymmetric link

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DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation 24

DAT: Dialogue-Aware Transformer with Modality-Group Fusion f...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Jia Yu, Yangchen Chen, Yin Zhang, Yu Jia, Peng Xu, Yunbo Li, Ziqiang Wang, Meng Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction. In this paper, we propose a Dialogue-Aware Transformer framework (DAT) with Modality-Group Fusion (MGF), which relies solely on audio-visual input and is language-independent, for estimating human engagement in conversations. Specifically, our method employs a modality-group fusion strategy that independently fuses audio and visual features within each modality for each person before inferring the entire audio-visual content. This strategy significantly enhances the model's performance and robustness. Additionally, to better estimate the target participant's engagement levels, the introduced Dialogue-Aware Transformer considers both the participant's behavior and cues from their conversational partners. Our method was rigorously tested in the Multi-Domain Engagement Estimation Challenge held by MultiMediate'24, demonstrating notable improvements in engagement-level regression precision over the baseline model. Notably, our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets. The source code will be available at https://***/MSA-LMC/DAT. © 2024 ACM.

关键词： engagement estimation

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Lane-changing decision-making for autonomous driving based on the Deep Deterministic Policy Gradient algorithm 9

Lane-changing decision-making for autonomous driving based o...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Yang, Du Xu, Ao Yang, Wenhua Shao, Yao Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

During vehicle operation, obstacles or other vehicles may require lane changes, necessitating the coordination of both lateral and longitudinal control. In predictable environments, rule-based lane-changing methods are relatively simple to implement. However, such methods fail to respond effectively to sudden, unforeseen obstacles. To address this issue, an automatic lane-change approach based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. In the reinforcement learning simulation process, the state space is defined by the distance between the vehicle and the lane boundaries, as well as the surrounding vehicles, while the action space consists of the vehicle's steering angle and longitudinal acceleration. The reward function incorporates several factors: collision avoidance, with different lane-changing distances considered for safety;steering rate and jerk for comfort;and overall driving stability. For a one-way, two-lane road, the highway-env framework is used to construct a visual simulation environment. By comparing lane-changing trajectories in simplified traffic flow scenarios, the study investigates the impact of driving style on the lane-change process and the time required for lane changes. Experimental results demonstrate that the proposed method enables the autonomous vehicle to learn lane-changing strategies that balance safety and comfort. © 2024 IEEE.

关键词： Steering

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Research on Key Technologies for Analyzing Public Opinion Propagation in Social Networks 9

Research on Key Technologies for Analyzing Public Opinion Pr...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Xiao, Lizhong Zhong, Tianhao Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

With the rapid development of the internet, technology, and social media platforms, an increasing number of people are choosing to express their opinions on their social media accounts. This paper aims to address public safety-related public opinion events by analyzing user social networks and node features to predict the extent and paths of opinion dissemination. The study employs a multi-head attention model to extract sentiment features from user tweets, categorizes topics into communities, and integrates updated tweet keywords into node features. It incorporates potential influencing factors from the actual propagation network into the weight matrix of the Graph Attention Network (GAT) to more accurately analyze the impact of neighboring nodes on users and predict the coverage and paths of public opinion dissemination. The research indicates that focusing on the latent factors of public opinion dissemination and node features can provide valuable insights for predicting public opinion spread and designing intervention strategies. © 2024 IEEE.

关键词： Tweets

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Instance-Level Neural Feature Selection Based on Disentanglement Enhancement 15

Instance-Level Neural Feature Selection Based on Disentangle...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Liu, Zihao Pan, Jun Wang, Hao Yu, Kui Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798331508821

Feature selection aims to select the optimal subset from the original features to reduce data dimensionality, eliminate irrelevant features, and improve the interpretability and performance of the model. In recent years, feature selection methods using neural networks have received much attention, but they often lead to suboptimal results due to feature entanglement problems. To this end, we propose a novel instance-level neural feature selection method based on disentanglement enhancement, including a feature selection network, a label prediction network, and a disentanglement bootstrap network. The feature selection network divides the features into strongly and weakly relevant ones, the label prediction network uses the strongly relevant features for supervised learning, and the disentanglement bootstrap network promotes feature disentanglement by minimizing the mutual information between strong and weak features. The designed gradient-based solution algorithm effectively solves the joint optimization problem. Experimental results validate the effectiveness of the method on tabular and image datasets. © 2024 IEEE.

关键词： Feature Selection

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Transmission of Electromagnetic Waves Through a Clay Material 1

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Bilandžija, Domagoj Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper deals with the simulation calculation of transmission (S21) and reflection (S22) parameters in a material parametrically based on clay (brick). The electromagnetic parameters of the clay that are the subject of the study are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ, while the only structural parameter is the thickness d of the sample. The values of all considered parameters are within the range of data from available literature and values that could occur due to external causes (e. g. an increase in clay moisture leads to an increase in electrical conductivity). The simulation model was carried out with the software tool ANSYS HFSS and validated with a laboratory measurement model. The laboratory model consists of a circular waveguide with an inner diameter of 150 mm and a total length of 700 mm, which is excited by 52 mm long probes placed 50 mm from the last wall of the waveguide. The lower cut-off frequency of this measurement system is 1.173 GHz, so all results (simulation and measurement) are limited by this lower cut-off frequency of the waveguide. All simulation calculations were performed for three clay sample thicknesses: 18 mm, 36 mm and 54 mm. These calculations were made with the aim of determining brick composites that have high attenuation of electromagnetic EM waves (low transmission parameter and high reflection parameter) in order to reduce the strength of EM fields inside the object made of such bricks. The measured results were used to verify the simulation model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Circular waveguides

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Prediction of Time Series Data based on PCA-HMM-BN 1

Prediction of Time Series Data based on PCA-HMM-BN

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1st International Conference on Software, Systems and information technology, SSITCON 2024

作者： Liu, Yutong Heilongiang University of Science and Technology School of Computer and Information Engineering Harbin China

ISBN: (纸本)9798350352931

This present work describes a framework for the analysis of score dynamics and energy fluctuations in competitive environments, aimed at forecasting match outcomes and providing strategic decisions useful to coaching and players' performance. Potential energy was defined, seven key measures were identified related to Kendall's tau, shown as heat maps to outline the underlying patterns. Using the CV method and the AHP composite weighting, overall scores can determine the overall possible energy level of players. The line graph should be drawn based on the latter in order to assess volatility throughout the course of the match. Then, it carries out a derivation method for potential energy series, followed by extracting the most critical turning points in energy dynamics through the PCA-HMM-BN algorithm. Feature engineering with lag features and effect coding improves predictability. This graph presents a comparison among different machine learning models, namely BP Neural Network, XGBoost, and SVM, by showing the high performance obtained by an SVM model tuned with the ASAA algorithm, with an R-squared score of 0.99967. © 2024 IEEE.

关键词： Graph neural networks

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SWM-YOLO: A New PCB Surface Defect Detection Algorithm Based on YOLOv8 9

SWM-YOLO: A New PCB Surface Defect Detection Algorithm Based...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Yang, Ruijun Zhao, Wen Shanghai Institute of Technology College of computer science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

Printed circuit boards (PCBs) present significant challenges for defect detection due to their complex structures, small size, and high false positive/negative rates associated with traditional methods. To address these issues, this paper proposes SWM-YOLO, a novel network model based on YOLOv8n. By incorporating Swin Transformer backbone, the model enhances feature extraction capabilities, effectively capturing global contextual information. Furthermore, Mixed Local Channel Attention is integrated into the C2f structure to accurately extract detailed features while preserving overall feature representation. Finally, the Wise-IoU loss function is adopted to improve the model's ability to detect subtle defects. Experimental results on a PCB surface defect dataset demonstrate a 3.2% precision improvement over the baseline YOLOv8n, significantly reducing false positives and negatives in PCB surface defect detection. © 2024 IEEE.

关键词： Image coding

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