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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Zhao, Longyang Zhang, Shuhao Xu, Zhi School of Computer Science and Information Security Guilin University of Electronics Technology No.1 Jinji road Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9781510677685

Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection Network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance. © 2024 SPIE.

关键词： Network architecture

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Spectral Demodulation of Tilted Fiber Bragg Grating Based Sensor for Temperature, Salinity and Pressure Measurement Using Convolutional Neural Network 28

Spectral Demodulation of Tilted Fiber Bragg Grating Based Se...

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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Liu, Ziqi Zhang, Shengqi Mei, Yongchang Hu, Pengfei Liu, Zhengyong Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China Zhuhai519000 China

ISBN: (纸本)9781957171302

This paper proposes a temperature-salinity-pressure sensor based on titled fiber Bragg grating, using convolutional neural network for spectral demodulation instead of conventional wavelength detection. Prediction results exhibit an error of ~0.0547℃, ~0.6506‰, ~0.006MPa, respectively. © Optica Publishing Group 2023, © 2023 The Author(s)

关键词： Demodulation

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Pressure Sensing Based on Sagnac Interferometers with Vernier Effect Assisted by Deep Learning Method 28

Pressure Sensing Based on Sagnac Interferometers with Vernie...

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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Mei, Yongchang Xia, Titi Cai, Haoen Liu, Zhengyong Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China Zhuhai519000 China

This paper presents a new approach to characterize pressure sensing employing cascaded Sagnac interferometers with Vernier effect via deep learning methods. Results show it could obtain a prediction error of 2.35X10-2... 详细信息

ISBN: (纸本)9781957171302

关键词： Fiber optic sensors

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A PREA and OODA Computing Framework of Unmanned systems to Perform Complex Tasks

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-18页

作者： Yang, Dongsheng Yu, Lei Zhang, Yi Zhu, Fenghua Li, Qiang Cui, Huachao Ma, Qiongmin Fan, Lili Tian, Yonglin Jinan University Guangzhou China Intelligent Science & Technology Academy Limited of CASIC Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 28th Research Institute of China Electronics Technology Group Corporation Nanjing China National Key Laboratory for Complex Systems Simulation National University of Defense Technology Changsha China School of Information and Electronics Beijing Institute of Technology Beijing China

A C2 computing framework for unmanned systems to perform complex tasks is proposed using methods of system of systems engineering, which is formed by combining the macro-scale command and control (C2) process mechanism model (PREA loop) and micro-scale C2 process mechanism model (OODA loop). Guided by PREA loop and OODA loop, the computing framework is divided into four steps and three kinds of transformations. The four steps are design, construction, operation monitoring, and assessment. The three transformations are tactical feedback, campaign feedback, and strategic feedback. A C2 organization model of joint landing combat force is established against the background of a typical complex task of joint landing operation, and the C2 computing framework based on the PREA &OODA is used to implement the C2 activities of the joint landing combat force. The influence of the computing framework on the performance of unmanned systems under different task conditions is verified, including task coordination load, task efficiency and sensitivity of unmanned systems response. Authors

关键词： Feedback

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Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

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Computers, Materials & Continua 2024年第10期81卷 1529-1563页

作者： Shijie Yang Jiateng Yuan Zhipeng Zhang Zhibo Chen Hanchao Zhang Xiaohui Cui School of Information Science and Technology Beijing Forestry UniversityBeijing100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083China State Key Laboratory of Efficient Production of Forest Resources Beijing100083China Division of Biostatistics Department of Population HeathGrossman School of MedicineNew York UniversityNew YorkNY 10016USA

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered *** sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant *** current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data *** regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme *** improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation *** combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline *** order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and

关键词： Unmanned aerial vehicle sensor self-organization path planning multi-UAV task assignment

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Visual Instruction Tuning for Holistic and Regional Remote Sensing Imagery Comprehension 2

Visual Instruction Tuning for Holistic and Regional Remote S...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Zhang, Wei Cai, Miaoxin Zhang, Tong Yin, Zhuang Mao, Xuerui National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing China Advanced Research Institute of Multidisciplinary Sciences China School of Mechatronical Engineering Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

Recently, the visual instruction multimodal large language models (MLLMs) have been extensively studied in the nature scenario. However, current remote sensing (RS) MLLMs mainly focus on image-level understanding and typically allow interaction only through text instructions, resulting in limited accuracy and efficiency. To address those limitations, a visual instruction model is proposed in this article to extend MLLM's fine-grained perception ability by incorporating bounding boxes with language instruction, aiming at achieving region-level visual understanding. To achieve this goal, a visual instruction dataset featuring multi-modal box-based region-text pairs is constructed. Furthermore, the visual instructions and images are encoded by the different encoders and subsequently fed into a large language model (LLM) along with text instruction tokens for tuning. Finally, experimental results demonstrate the proposed model's promising performance in region-level image comprehension. © 2024 IEEE.

关键词： Adversarial machine learning

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Fog Images Generation About Unmanned Surface Vessels with Improved Generative Adversarial Network 42

Fog Images Generation About Unmanned Surface Vessels with Im...

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42nd Chinese Control Conference, CCC 2023

作者： Tang, Yifeng Huang, Zhihui Tang, Chuancong Luo, Chuanshang Xu, Yifan Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

This paper proposes a fog weather data augmentation method for the unmanned surface vessels (USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Generative adversarial networks

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A Survey on Text-to-SQL Parsing: From Rule-Based Foundations to Large Language Models 2nd

A Survey on Text-to-SQL Parsing: From Rule-Based Foundations...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： El Boujddaini, Farida Laguidi, Ahmed Mejdoub, Youssef CED Engineering Sciences Higher School of Technology Hassan II University Casablanca Morocco Laboratory of Systems Analysis Information Processing and Industrial Management ESTS Mohammed 5 University Rabat Morocco

ISBN: (纸本)9783031704109

Transforming natural language into SQL queries represents a significant challenge in both natural language processing (NLP) and database research fields. The main target of this transformation is to allow users to interact with databases using their natural language. This extends the scope of data analysis to include persons who do not have expertise in computer science or data engineering. In recent years, the implementation of deep learning models has yielded significant results. Following this, the introduction of large language models (LLMs) such as GPT-4 and BERT has lifted the standard for this task, promoting them to new heights of performance and capacity. Our survey provides a detailed review of the evolutionary phases of Text-to-SQL parsing. Initially, we investigate the primary approaches in this field that use rule-based models. Following this, we present the several deep learning models proposed for this task. After that, we introduce several datasets commonly used in training and evaluating the proposed models. In the last section, we examine the role of large language models (LLMs) in revolutionizing Text-to-SQL tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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Nanoscale phase management of the 2D/3D heterostructure toward efficient perovskite solar cells

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Science Bulletin 2024年第18期69卷 2853-2861页

作者： Hao Gu Annan Zhu Junmin Xia Wang Li Jiahao Zheng Tao Yang Shengwen Li Nan Zhang Shiliang Mei Yongqing Cai Shi Chen Chao Liang Guichuan Xing Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Organic Electronics and Information Displays&Institute of Advanced Materials(IAM) Jiangsu National Synergetic Innovation Center for Advanced Materials(SICAM)Nanjing University of Posts&Telecommunications(NUPT)Nanjing 210023China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of PhysicsNational Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologyXi’an Jiaotong UniversityXi’an 710049China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsHarbin Institute of TechnologyShenzhen 518055China Institute for Electric Light Sources School of Information Science and TechnologyFudan UniversityShanghai 200433China

The stabilization of the formamidinium lead iodide(FAPbI_(3))structure is pivotal for the development of efficient photovoltaic *** two-dimensional(2D)layers to passivate the threedimensional(3D)perovskite is essential for maintaining the a-phase of FAPbI_(3) and enhancing the power conversion efficiency(PCE)of perovskite solar cells(PSCs).However,the role of bulky ligands in the phase management of 2D perovskites,crucial for the stabilization of FAPbI_(3),has not yet been *** this study,we synthesized nanoscale 2D perovskite capping crusts with=1 and 2 Ruddlesden-Popper(RP)perovskite layers,respectively,which form a type-Ⅱ 2D/3D *** heterostructure stabilizes the a-phase of FAPbI_(3),and facilitates ultrafast carrier extraction from the 3D perovskite network to transport contact *** introduced tri-fluorinated ligands to mitigate defects caused by the halide vacancies and uncoordinated Pb^(2+)ions,thereby reducing nonradiative carrier recombination and extending carrier *** films produced were incorporated into PSCs that not only achieved a PCE of 25.39%but also maintained 95%of their initial efficiency after 2000 h of continuous light exposure without *** findings underscore the effectiveness of a phase-pure 2D/3D heterostructure-terminated film in inhibiting phase transitions passivating the iodide anion vacancy defects,facilitating the charge carrier extraction,and boosting the performance of optoelectronic devices.

关键词： Phase-pure 2D perovskite 2D/3D heterostructure Type-II energy alignment Carrier dynamic FAPbI_(3)

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