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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Li, Jiaming Ren, Cheng Xu, Jiaxin Wang, Yao Xu, Hanlin Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9798331507886

Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and time-consuming. Due to the unique structural characteristics of aviation connectors, single-modality sensors often struggle to simultaneously detect both pins and holes. To address this issue, we propose a 3D skewed pins detection (3D-SPD) algorithm that integrates both 2D and 3D modals features. Due to the low quality of the raw connector data, we propose a connector point cloud enhancement (CPCE) algorithm that combines multi-view point clouds for point cloud fusion enhancement. Based on enhanced data, we perform skewed pin detection. Experimental results indicate that CPCE and 3D-SPD are effective in detecting skewed pins, achieving an accuracy rate exceeding 99%. Our algorithm can be deployed as an AI assistant on the edge device for the skewed pin detection. © 2024 IEEE.

关键词： defect detection machine vision multi-modal cooperative perception skewed pin

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A lightweight object detection network based on YOLOv5 for SAR image 2

A lightweight object detection network based on YOLOv5 for S...

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

作者： Shen, Aijia Zhao, Liangbo Xu, Fanyun Wang, Guoqing Liu, Wenchao Shen, Zimeng Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (纸本)9798331515669

Remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military defense. When performing remote sensing image object detection on airborne and spaceborne platforms, the vast amount of remote sensing data processing and limited computational resources impose high real-time requirements on the object detection algorithms. This paper designs a lightweight object detection network model named YOLOv5-tiny, based on the existing deep learning network detection model YOLOv5s, and deploys it on the Jetson TX2 development board for training and testing. Experimental results show that the proposed YOLOv5-tiny model, when tested on the SAR-AIRcraft-1.0 with an input image size of 640x640, is 10 times smaller than YOLOv5s;it has a computational cost of 5.2 GFLOPs, which is 1/5 of YOLOv5s, and the processing time for a single image is reduced to half that of YOLOv5s, with only a 0.1% decrease in accuracy. © 2024 IEEE.

关键词： Object tracking

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Code Generation Based on Quality-Aware Reordering 9

Code Generation Based on Quality-Aware Reordering

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9th International Conference on intelligent computing and Signal processing, ICSP 2024

作者： Song, Yuan Yu, Luo College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan430065 China

ISBN: (纸本)9798350376548

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. This method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. The experimental results show that the approach proposed in this paper obtains a significant improvement in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improvement in solving the problem of selecting the correct output from natural language to code generation, effectively improves the accuracy of evaluation by automating the generation of test cases, and provides an effective solution to improving the correctness of code generation. © 2024 IEEE.

关键词： code generation natural language processing (NLP) pre-trained large models quality estimation reranking test case generation

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A SIR dual-band microstrip filter loaded with shaped branches

A SIR dual-band microstrip filter loaded with shaped branche...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zhiyan Lv Xiaodi Song Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, a SIR-coupled dual-band bandpass filter is proposed, which has lower insertion loss, better out-of-band rejection. The bandpass characteristics of the dual-band are obtained by bending the SIR and coupling it, and loading the shaped branches. By changing the shape of the shaped branches, a transmission pole is added in the first passband, which reduces the insertion loss of the filter and improves the rejection effect of the stopband. The simulation results show that the dual-frequency bandpass filter has a center frequency of 6.26GHz, an insertion loss of 0.17dB, a return loss of less than -27.8dB, and a relative bandwidth of 15.5% for the low band, and an insertion loss of 0.67dB, a return loss of less than -15.8dB, and a relative bandwidth of 11.6% for the high band, at a center frequency of 9.26GHz.

关键词： Band-pass filters Matched filters Microstrip filters Shape Simulation Dual band Insertion loss Bandwidth Propagation losses Passband

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Ship Target Detection Method Based on Local Saliency Enhancement 5

Ship Target Detection Method Based on Local Saliency Enhance...

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5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Li, Nana Pan, Xueli Yang, Lixia Huang, Zhixiang Wu, Zhenhua Zhang, Xianqi Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China East China Institute of Optoelectronic Integrated Devices Suzhou China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Due to the non-uniformity of ship target scattering, speckle noises and side lobes, synthetic aperture radar (SAR) ship target detection is facing great challenges. Therefore, to improve the detection performance in this scenario, the ship detection method based on the local saliency enhancement is proposed in this paper. Firstly, the proposed method improves the target-to-clutter ratio (TCR) by combining the relative differences of multi-features between ship target and the clutter background, and the threshold segmentation is performed based on the final local enhancement results. The experimental results show that the proposed method can suppress clutter background and speckle noises, improve the TCR and the algorithm has a high detection rate and a low false alarm rate in the complex background and multi-target marine environment. © 2022 IEEE.

关键词： Synthetic aperture radar

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Prototype-Guided Representation Distribution Optimization for Change Detection in High-Resolution Remote Sensing Imagery 2

Prototype-Guided Representation Distribution Optimization fo...

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

作者： Wang, Guoqing Chen, He Wei, Xin Shi, Junqing Chen, Liang Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

Change detection is the process of automatically extracting change areas from bi-temporal remote sensing images. The key of deep learning-based change detection methods is to extract features that represent changes/non-changes. Most of the existing methods only enhance the representation ability of the changed regions through the model architecture design, but lack further constraints in the representation space. This paper introduces a prototype-guided representation distribution optimization method. By comparing pixel features with prototypes, confusing pixels can be determined, and these pixels are adaptively weighted during learning, so that similar representations are closer and different representations are farther apart. In addition, the representation distribution is also applied as transferred knowledge to knowledge distillation for lightweight CD model. Experimental results on two public datasets show the effectiveness of the proposed method. © 2024 IEEE.

关键词： Change detection

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Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

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Nano-Micro Letters 2024年第3期16卷 303-312页

作者： Xueqiu You Dongao Zhang Xia‑Guang Zhang Xiangyu Li Jing‑Hua Tian Yao‑Hui Wang Jian‑Feng Li School of Ocean Information Engineering Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent ProcessingJimei UniversityXiamen 361021People’s Republic of China State Key Laboratory of Physical Chemistry of Solid Surfaces MOE Key Laboratory of Spectrochemical Analysis and InstrumentationiChEMCollege of Chemistry and Chemical EngineeringCollege of MaterialsCollege of EnergyXiamen UniversityXiamen 361005People’s Republic of China Key Laboratory of Green Chemical Media and Reactions Ministry of EducationCollaborative Innovation Center of Henan Province for Green Manufacturing of Fine ChemicalsCollege of Chemistry and Chemical EngineeringHenan Normal UniversityXinxiang 453007People’s Republic of China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005People’s Republic of China

Interfacial water molecules are the most important participants in the hydrogen evolution reaction(HER).Hence,understanding the behavior and role that interfacial water plays will ultimately reveal the HER ***,investigating interfacial water is extremely challenging owing to the interference caused by bulk water molecules and complexity of the interfacial ***,the behaviors of interfacial water in different cationic electrolytes on Pd surfaces were investigated by the electrochemistry,in situ core-shell nanostructure enhanced Raman spectroscopy and theoretical simulation *** spectral evidence reveals a red shift in the frequency and a decrease in the intensity of interfacial water as the potential is shifted in the positively *** comparing the different cation electrolyte systems at a given potential,the frequency of the interfacial water peak increases in the specified order:Li+

关键词： In situ Raman Interfacial water Hydrogen evolution reaction Cations

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Research on Pedestrian Gender Detection Based on A Lightweight Model 36

Research on Pedestrian Gender Detection Based on A Lightweig...

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36th Chinese Control and Decision Conference, CCDC 2024

作者： Yuling, Hu Xinyi, Wang Weiguang, Zou Beijing University Of Civil Engineering And Architecture Institute Of Electrical And Information Engineering China Beijing University Of Civil Engineering And Architecture Beijing Key Laboratory Of Intelligent Processing For Building Big Data Beijing China

ISBN: (纸本)9798350387780

In recent years, a variety of emergency events have been occurring in buildings in countries. For there are some differences between male and female in movement ability and evacuation behavior, gender has a non-negligible impact on evacuation and decision-making of emergency events. Thus, it is of great practical significance to detect the gender of pedestrians at entrances or exits of buildings in real time to grasp the proportion of different gender people in buildings and to formulate emergency warning or evacuation strategies scientifically. In response to the requirement of rapid emergency response, this paper proposes a pedestrian gender detection method based on a lightweight object detection model YOLOX-Tiny. In addition, considering the inaccurate pedestrian gender detection caused by the change of pedestrian posture and camera angle, the attention mechanism CBAM is introduced to enhance the ability of filtering a large amount of redundant information, obtaining key information and acquiring pedestrian gender characteristic information. Through the experiments of self-made pedestrian gender datasets, the results show that the detection accuracy of the improved model is increased by 1.8%, and the detection speed reaches 251FPS, which can meet both speed and accuracy of pedestrian gender detection in emergencies. © 2024 IEEE.

关键词： Object detection

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Global information Edge Augmentation Network for Remote Sensing Image Continuous Super-Resolution 2

Global Information Edge Augmentation Network for Remote Sens...

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

作者： Gao, Shize Lei, Yong Zhang, Jingya Wang, Guoqing Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques. © 2024 IEEE.

关键词： Image enhancement

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Building Segmentation from Remote Sensing Image via DWT Attention Networks 23

Building Segmentation from Remote Sensing Image via DWT Atte...

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5th International Conference on Pattern Recognition and intelligent Systems, PRIS 2023

作者： Wu, Yingbin Zhou, Mingquan Geng, Shengling Zhang, Dan College of Computer Qinghai Normal University Xining China College of Math and Information Yuncheng University Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9781450399968

The attention mechanism has been widely used and achieved good results in many visual tasks. But the calculations of attention mechanism in vision tasks consume huge spaces and times, which is the obvious disadvantage of this method. In order to alleviate this problem, we use the DWT(Discrete Wavelet Transform) method to reduce the complexity of attention calculation. DWT can transform an N-dimensional vector into two vectors, one is the low-frequency component of N/2 dimension and the other is high-frequency component of N/2 dimension too. We only use the low-frequency to calculate the attention matrixes, which can reduce the complexity of matrix multiplication, then the time and space consumption of the network is reduced significantly. We also find that the building segmentation in the remote sensing image is different from the other scene segmentation, that the sizes and numbers of different classes of the targets in the general scene images are obvious. Despite all this, our method is still applicable for the targets with large numbers and sizes in general scene images, but not for the targets with small sizes and numbers, and this view is also verified by the subsequent experiments on different datasets. We apply our method on three typical networks (Danet, Swin and Segmenter), and carry out comprehensive experiments on the Cityscape dataset and three building segmentation datasets (Inria Aerial Dataset, Massachusetts Buildings Dataset and Chinese Style Architecture Dataset). The experiments show that, our method is more suitable for building segmentation and can reduce the complexity of the model calculation in building segmentation, and the Mean IoU of segmentation results is not reduced clearly, some even improved. © 2023 ACM.

关键词： Remote sensing

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