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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

引用

2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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A New Adaptive Image Sharpening Scheme 13

A New Adaptive Image Sharpening Scheme

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Chen, Tzong-Jer Lu, Rong-Hui School of Mathematics & Computer Science Wuyi University Fujian Wuyishan354300 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry China Wuyi University Information Technology and Laboratory Management Center Fujian Wuyishan354300 China

ISBN: (纸本)9798350319156

The sharpening is a scheme applied to highlight the intensity transitions in an image. This enhancement increases edge acutance and significantly improves the overall sharpness of processed images. Image sharpening is also of enormous importance in medicine for diagnosis. Noise overshoot and over-sharpening effects occur in traditional sharpening algorithms. The adaptive approach skips the noise producing a better sharpening result. Abrupt pixel value changes, if adequately emphasized, can be utilized effectively in the sharpening process. Since a residual image contains noise with edges, the bigger the filter size the greater the content of edges left in the residual image. Two residual images are produced first with different filter sizes and assumed a relationship between two residuals. A simple linear regression is measured using these residuals. This linear equation is an expectation and the deviation is the difference between two residuals. The window may contain edges if the residual value measured by larger size filter to some degree higher than that by smaller size filter. One standard deviation (±σ) is proposed as the threshold, in this work. A sensitive sharpening filter is adaptively then applied to those locations in image. It has been demonstrated that the proposed approach yields better results than the global sharpening filter on the measurements of Pratt's P indices. A sharpening scheme without latent image noise amplification is useful for pattern recognition and machine-learning. In the future this scheme will be used to preprocess image segmentation and medical image. © 2023 IEEE.

关键词： Image segmentation

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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification 11

Residual and Attention Mechanism-Based Network for Aluminum-...

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11th International Conference on Information Systems and Computing technology, ISCTech 2023

作者： Yang, Xiao Shen, Tao Yin, Jiancheng Liu, Yingli Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Materials Science and Engineering Kunming650093 China

ISBN: (纸本)9798350342406

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys. © 2023 IEEE.

关键词： Image classification

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TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction 5th

TGNN: A GNN-Based Method with Multi-entity Node for Person...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Mo, Zhikang Shao, Qixiang Wu, Likang Yu, Runlong Liu, Qi Xu, Jiexin Song, Hongmei Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Computer Science and Techonology University of Science and Technology of China Hefei China Department of Information Technology China Merchant Bank Shenzhen China

ISBN: (纸本)9789811989148

The offline business of banks is deeply involved in bank clients’ financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients’ banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

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2023 IEEE International Conference on Memristive Computing and applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video Datasets 9

Temporal Transformation Network Based On Scale Sequences for...

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9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Zhao, Bo Dong, Zhiwei Dong, Lingyun Li, Danyang School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9798350317251

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline. © 2023 IEEE.

关键词： Video recording

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A Novel Wavelet Based Generative Model for Time Series Prediction

A Novel Wavelet Based Generative Model for Time Series Predi...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Chaofan Dai Xiaoguang Yuan Zongkai Tian Xinyue Hu Zhen Luan Youchen Wang National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Generative models have become an exciting area of research in recent years. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) have been utilized in various data augmentation applications. However, generative models can learn high-dimensional features of data through adversarial learning, making them suitable for nonlinear and nonstationary time series analysis, such as stock market prediction, high-frequency trading, and ocean current forecasting. In this paper, the researchers focus on using a wavelet-based GAN to predict stock market prices by generating synthetic stock market price trends. Historical stock price data from 2014 to 2024 is used for our experiments, and the results show that the wavelet-based GAN outperforms deep learning baseline models..

关键词： Wavelet transforms Analytical models Oceans Time series analysis Noise reduction Predictive models Generative adversarial networks Wavelet analysis Data models Stock markets

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning 7

Tensor Graph Convolutional Network for Dynamic Graph Represe...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application For Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

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Dangerous Goods Detection in X-ray Security Inspection Images Based on Improved YOLOv7 23

Dangerous Goods Detection in X-ray Security Inspection Image...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Enyan Zhang Yunhong Yang Yanxiang Feng Zeyu Hu Aili Wang Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology China Department of Computer Science Chubu University Japan

ISBN: (纸本)9798400709272

According to multi-scale targets and overlapping targets in X-ray security inspection images, this paper proposes a dangerous goods detection algorithm called as YOLOv7-MPCN(MaxPooling with CA and BiFPN). As the convolutional network layers overlap, the resolution of the feature map decreases, resulting in the loss of some position information. Firstly, the coordinate attention mechanism is combined with the MPConv structure in the downsampling stage to enrich semantic information while preserving position perception information, improving the ability to extract dangerous goods feature information in complex overlapping backgrounds. Secondly, using the BiFPN(Bidirectional Feature Pyramid Network) feature fusion module with smaller parameter values as the feature pyramid structure of the model effectively reduces the computational complexity of the model; Finally, referring to the residual network structure in RepVGG, adding skip connections in the lower branch of MPConv enables the network to reach deeper depths without the gradient vanishing. The average detection accuracy of YOLOv7-MPCN on the public dataset SIXray reached 92.5%, which is 0.9% higher than YOLOV7. The experimental results show that the proposed algorithm in this paper has an improvement in detection accuracy compared to the original YOLOv7, proving the effectiveness of YOLOv7-MPCN.

关键词： X-ray images attention mechanism dangerous goods detection feature fusion

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NOMA in Flexible Optical Access Network: Principle, Advantage and Challenges

NOMA in Flexible Optical Access Network: Principle, Advantag...

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International Conference on Optical Communications and Networks (ICOCN)

作者： Jia Chai Nan Feng Mingyi Ma Zhe Li Shaobo Li Beijing Institute of Computer Technology and Application The 54th research Institute of CETC Shijiazhuang Hebei Key Laboratory of Photonic Information Technology and Application Shijiazhuang

We summarize the current principle, advantage, and challenges of power domain non-orthogonal multiple access technology which exploited the benefits of different channel gains of passive optical network to improve the...

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