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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Xia, Zhaosheng Ren, Xingang Wei, Shengyang Huang, Zhixiang Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Hefei230039 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9798350375909

The investigation of the optoelectronic characteristics of all-perovskite tandem solar cells holds pivotal significance in surpassing the Shockley-Queisser limit of single-junction perovskite solar cells. Initially, we established two distinct four-terminal tandem solar cells (4T TSCs) composed of different perovskite materials: FA0.8Cs0.2Pb(I0.7Br0.3)3 (Eg = 1.75 eV) and MASnI3 (Eg = 1.3 eV). Through numerical simulations of the optoelectronic characteristics of 4T TSCs, we evaluated the performance of the TSCs at varying perovskite layer thicknesses. Following optimization, the power conversion efficiency (PCE) of the 4T TSCs increased from the initial 30.34% to 33.54%. Through an in-depth analysis of the cell and energy distribution in TSCs, it was observed that the light-coupling layer significantly influenced the PCE of the 4T TSCs. Upon optimizing the light-coupling layer, the PCE of the TSC increased from the initial 30.34% to 34.7%. This study provides crucial theoretical and experimental foundations for optimizing the performance of all-perovskite TSCs. © 2024 IEEE.

关键词： Perovskite solar cells

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Power Minimization for the IRS-Aided SWIPT Secure Communication System with Imperfect Cascaded Channel

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Journal of Communications and information Networks 2023年第2期8卷 133-140页

作者： Chunlong He Shuqiong Xu Gongbin Qian Xingquan Li Chiya Zhang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China School of Microelectronics Shenzhen Institute of Information TechnologyShenzhen 518000China School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhen 518055China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Peng Cheng Laboratory(PCL) Shenzhen 5180523China

intelligent reflecting surface(IRS)-assisted simultaneous wireless information and power transfer(SWIPT)is a promising technology for prolonging the lifetime of the users and improving system ***,it is challenging to obtain the perfect channel state information(CSI)at the base station(BS)due to the lack of radio frequency(RF)chains at the IRS,which becomes even harder when eavesdroppers(Eves)*** this paper,we study the power issues in the IRS-aided SWIPT system under the imperfect CSI of BS-IRS-Eves *** aim to minimize the transmitting power at the BS by jointly designing transmit beamforming and reflective beamforming with subject to the rate outage probability constraints of the Eves,the energy harvesting constraints of the energy receivers(ERs),and the minimum rate constraints of the information receivers(IRs).We use Bernstein-type inequality to solve the rate outage probability ***,the formulated nonconvex problem can be efficiently tackled by employing alternating optimization(AO)combined with semidefinite relaxation(SDR)and penalty convex-concave procedure(CCP).Numerical results demonstrate the effectiveness of the proposed scheme.

关键词： IRS SWIPT secure communication AO

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Representation of Semantic Word Embeddings Based on SLDA and Word2vec Model

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Chinese Journal of Electronics 2023年第3期32卷 647-654页

作者： TANG Huanling ZHU Hui WEI Hongmin ZHENG Han MAO Xueli LU Mingyu GUO Jin School of Computer Science and Technology Shandong Technology and Business University Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing Key Laboratory of Intelligent Information Processing in Universities of Shandong Shandong Technology and Business University School of Information and Electronic Engineering Shandong Technology and Business University Information Science and Technology College Dalian Maritime University School of Computer and Information Technology Liaoning Normal University

To solve the problem of semantic loss in text representation, this paper proposes a new embedding method of word representation in semantic space called wt2svec based on supervised latent Dirichlet allocation(SLDA) and Word2vec. It generates the global topic embedding word vector utilizing SLDA which can discover the global semantic information through the latent topics on the whole document set. It gets the local semantic embedding word vector based on the Word2vec. The new semantic word vector is obtained by combining the global semantic information with the local semantic information. Additionally, the document semantic vector named doc2svec is generated. The experimental results on different datasets show that wt2svec model can obviously promote the accuracy of the semantic similarity of words,and improve the performance of text categorization compared with Word2vec.

关键词： Semantics Text categorization Resource management Task analysis

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SAR Tomography with Improved Non-Local Means Filtering Based on Adaptive Window

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Journal of Beijing Institute of technology 2023年第6期32卷 670-684页

作者： Shenglei Wang Zhiyang Chen Yuanhao Li Cheng Hu Radar Research Laboratory School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of TechnologyBeijing 100081China

In order to mitigate speckle noise in synthetic aperture radar(SAR)images and enhance the accuracy of SAR tomography,non-local means(NL-means)filtering has been proven to be an effective method for improving the quality of SAR *** from considerations like noise type and the definition of similarity,the size and shape of filtering windows are critical factors influencing the efficacy of NL-means filtering,yet there has been limited research on this *** paper introduces an enhanced NL-means filtering method based on adaptive windows,allowing for the automatic adjustment of filtering window size according to the amplitude information of the SAR ***,a directional window is incorporated to align SAR interferograms,achieving the dual objective of preserving filtering standards and retaining detailed *** results on interferogram filtering and tomography,based on TerraSAR-X data,demonstrate that the proposed method effectively reduces phase noise while maintaining texture accuracy,thereby improving tomography quality.

关键词： NL-means filter adaptive window SAR interferogram filtering SAR tomography

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Application of intelligent Schedule Management in Volunteer Service System 1

Application of Intelligent Schedule Management in Volunteer ...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Li, Yongxin Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

As the demand for volunteer services grows, enhancing the efficiency and management of these services has become a key issue. This paper explores methods of integrating intelligent scheduling management functions with volunteer service systems to optimize volunteer time management and task allocation. By analyzing existing volunteer service systems, we propose a novel system framework that integrates intelligent scheduling management. This framework updates volunteer schedules in real- time, automatically matches tasks with volunteers' time windows, and optimizes task distribution using smart algorithms. Experimental results show that volunteer service systems with integrated intelligent scheduling management significantly improve task allocation accuracy and volunteer satisfaction. This research provides new ideas and practical basis for the intelligent development of volunteer services. © 2024 IEEE.

关键词： Digital family digital platform volunteer service

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Simulation extractable SNARKs based on target linearly collision-resistant oracle

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Science China(Technological Sciences) 2024年第9期67卷 2853-2866页

作者： WANG LiGuan LI Yuan ZHANG ShuangJun CAI DongLiang KAN HaiBin Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Shanghai Engineering Research Center of Blockchain Shanghai 200433China Yiwu Research Institute of Fudan University Yiwu 322000China

The famous zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARK) was proposed by Groth in ***, the construction is based on quadratic arithmetic programs which are highly efficient concerning the proof length and the verification complexity. Since then, there has been much progress in designing zk-SNARKs, achieving stronger security,and simulated extractability, which is analogous to non-malleability and has broad applications. In this study, following Groth's pairing-based zk-SNARK, a simulation extractability zk-SNARK under the random oracle model is constructed. Our construction relies on a newly proposed property named target linearly collision-resistant, which is satisfied by random oracles under discrete logarithm assumptions. Compared to the original Groth16 zk-SNARK, in our construction, both parties are allowed to use such a random oracle, aiming to get the same random number. The resulting proof consists of 3 group elements and only 1 pairing equation needs to be verified. Compared to other related works, our construction is shorter in proof length and simpler in verification while preserving simulation extractability. The results also extend to achieve subversion zero-knowledge SNARKs.

关键词： quadratic arithmetic program simulation extractability subversion zero-knowledge succinct non-interactive arguments of knowledge target linearly collision-resistant

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Research on YOLOv8-Based Household Item Detection for Indoor Scenes 1

Research on YOLOv8-Based Household Item Detection for Indoor...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Wu, Jingwen Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

This research is driven by the growing need for efficient, scalable object detection in smart home applications with real-time performance and resource constraints. In this study, we utilize the YOLOv8 deep learning model for household item detection, focusing on its performance in real-time scenarios. We compare different versions of YOLOv8 - specifically, YOLOv8n, YOLOv8s, and YOLOv8m - using a preprocessed custom dataset. The results indicate that YOLOv8n stands out due to its lower parameter count and faster inference speed, making it ideal for real-time applications, even though it has slightly lower precision and recall. In contrast, while YOLOv8s and YOLOv8m show improvements in certain metrics, their higher computational demands limit their effectiveness in resource-constrained settings. Overall, YOLOv8n effectively balances speed and accuracy, offering a viable solution for household item detection. © 2024 IEEE.

关键词： Smart homes

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Facial Recognition-Based Classroom Attendance System Utilizing Deep Learning 1

Facial Recognition-Based Classroom Attendance System Utilizi...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Zhou, Cong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

In recent years, artificial intelligence technologies, especially facial recognition, have made significant strides. With its high precision and efficiency, facial recognition has shown great potential and advantages in areas such as identity verification and behavior analysis. Classroom learning is the main channel through which students acquire knowledge, develop skills, and improve their competencies. A well-organized classroom attendance system is essential for ensuring the effective implementation and smooth execution of teaching plans. This system is based on an OpenCV and PyQt5 facial recognition client, utilizing the LBPH algorithm to achieve efficient facial recognition. In addition to providing a simple check-in function, the system offers a complete registration process and data management features. In practical use, students simply need to check in by facing the camera, allowing the system to automatically recognize the student's face and record the attendance data. This significantly simplifies the check-in process for both teachers and students, thereby improving classroom management efficiency. © 2024 IEEE.

关键词： information management

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems

Exploring the Compositional Deficiency of Large Language Mod...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhao, Jun Tong, Jingqi Mou, Yurong Zhang, Ming Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9798891761643

Human cognition exhibits systematic compositionality, the algebraic ability to generate infinite novel combinations from finite learned components, which is the key to understanding and reasoning about complex logic. In this work, we investigate the compositionality of large language models (LLMs) in mathematical reasoning. Specifically, we construct a new dataset MATHTRAP‡ by introducing carefully designed logical traps into the problem descriptions of MATH and GSM8K. Since problems with logical flaws are quite rare in the real world, these represent "unseen" cases to LLMs. Solving these requires the models to systematically compose (1) the mathematical knowledge involved in the original problems with (2) knowledge related to the introduced traps. Our experiments show that while LLMs possess both components of requisite knowledge, they do not spontaneously combine them to handle these novel cases. We explore several methods to mitigate this deficiency, such as natural language prompts, few-shot demonstrations, and fine-tuning. Additionally, we test the recently released OpenAI o1 model and find that human-like 'slow thinking' helps improve the compositionality of LLMs. Overall, systematic compositionality remains an open challenge for large language models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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