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3rd International Conference on Electronic Information engineering and Computer, EIECT 2023

作者： Guo, Yongwen Chen, Hongming Huang, Juntao Lin, Yilei Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350357707

Water contamination is a major concern worldwide due to its impact on the global ecology. Accurate forecasting of water quality parameters is crucial for effective water contamination control. However, existing models face challenges in accurately predicting water quality series due to their nonlinearity and non-stationarity. Additionally, further investigation is needed to find out the performance of the Bidirectional Long Short-Term Memory network (BiLSTM) when combined with optimization of signal processing and adjustment of hyperparameters. In this article, we introduce a novel hybrid approach named CEEMDAN-QPSO-BiLSTM, which combines three techniques-Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Quantum behaved Particle Swarm optimization (QPSO), and BiLSTM-for predicting water quality parameters. To evaluate the effectiveness of this hybrid model, we developed four benchmark models, namely LSTM, BiLSTM, PSO-BiLSTM, and QPSO-BiLSTM, and compared their predictive accuracy with that of our hybrid approach. The training and testing of these models utilized actual water quality data from the Chinese National Marine Environmental Monitoring Center's official system, specifically focusing on Dissolved Oxygen (DO), pH value, and Chemical Oxygen Demand (COD) parameters. The experimental results show that the CEEMDAN-QPSO-BiLSTM surpasses the baseline models, providing better error performance and prediction accuracy. © 2023 IEEE.

关键词： Empirical mode decomposition

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation 8

Cross-Modality Comprehensive Feature Aggregation Network for...

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8th International Symposium on Computer Science and Intelligent Control, ISCSIC 2024

作者： Yang, Yale Zhang, Zihao Hou, Huifang Zhang, Wenwen Zhuang, Chunsheng Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control School of Artificial Intelligence and Big Data Zhengzhou China Data Henan University of Technology School of Artificial Intelligence and Big Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (纸本)9798350380286

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences;then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Stereo Matching Algorithm for Improved Acquisition of Disparity Of Tiny Objects 14

Stereo Matching Algorithm for Improved Acquisition of Dispar...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Meng, Lingjiang Wei, Hui Wang, Haoming Zhang, Qing Li, Guangrong Nanning Vocational and Technical University School of Artifical Intellgence Nanning530000 China Shanghai Key Laboratory of Data Science Fudan University Laboratory of Algorithms for Cognitive Models Shanghai200000 China Harbin Institute of Technology School of Mechatronics Engineering Harbin150000 China

ISBN: (纸本)9798350356670

Deep-learning algorithms have an excellent nonlinear fitting ability. However, tiny objects in images do not approach the general properties of the fit, which causes the algorithms to have problems acquiring disparity for tiny objects. In addition, the results suffer from problems such as disparity inflation and warping variation. To address the shortcomings of deep learning algorithms, we propose an improved deep learning algorithm based on traditional algorithms. First, the algorithm divides the image into segments based on edges. Second, the edge error disparity and disparity expansion is corrected. Third, it finds the wrong disparity in a small segment, and removes it. The adjacent pixels with disparity removed are grouped into clusters. The matching relationship is established by combining the cluster's gray, gradient, and feature-point information into matching elements. Finally, removal of disparity warping variation and post-processing is performed. The algorithm can effectively correct error disparity, detect the disparity of tiny objects, and can be applied to practical scenarios where tiny objects are detected. © 2024 IEEE.

关键词： Adversarial machine learning

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Image denoising based on residual network 3

Image denoising based on residual network

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3rd International Conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Ma, Lihe Wang, Yan Sha, Ning Ma, Lin School of Information Engineering Harbin University Harbin China Heilongjiang Government Affairs Big Data Center3 Harbin China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin China

ISBN: (纸本)9781510680449

Image denoising has always been a hot research topic in the field of computer vision, playing an important role in improving image quality and accuracy. In this paper, a method for image denoising based on residual network is proposed, which uses deep learning technology to improve the effect of image processing. This method can automatically learn useful features in the image and effectively remove noise in the image, achieving automatic removal of image noise. Finally, experiments were conducted on standard image datasets for evaluation. The experimental results show that the image denoising method based on residual network has better performance compared to traditional methods. It can effectively remove various types of noise, improve image quality and clarity, and has broad application prospects in image processing. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image denoising

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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Recognition of field roads based on improved U-Net++Network

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International Journal of Agricultural and Biological engineering 2023年第2期16卷 171-178页

作者： Lili Yang Yuanbo Li Mengshuai Chang Yuanyuan Xu Bingbing Hu Xinxin Wang Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co.Ltd. Beijing 102200China

Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor *** achieve high performance and low cost,perception tasks are of great *** this study,a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural *** approach of this study utilized point clouds,with low-resolution Lidar point clouds as inputs,generating high-resolution point clouds and Bird's Eye View(BEV)images that were encoded with several basic *** a BEV representation,road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++neural *** enhancements were proposed for U-Net++:1)replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block(ACBlock);2)adding a multi-branch Asymmetric Dilated Convolutional Block(MADC)in the highest semantic information layer;3)adding an Attention Gate(AG)model to the long-skip-connection in the decoding *** results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds,which was 7.35 percentage points higher than U-Net++.Furthermore,the average processing time of the model was about 70 ms,meeting the time requirements of unmanned driving in agricultural *** proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.

关键词： image segmentation unmanned agricultural machinery field roads point cloud super-resolution point cloud bird’s eye view

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Low-cost irrigation system based on multi-tenant and WSN technologies(SCA) 36

Low-cost irrigation system based on multi-tenant and WSN tec...

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36th International Conference on Software engineering and knowledge engineering, SEKE 2024

作者： Ma, Jinwei Li, Meian Chao, Lumeng Computer and Information Engineering College Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Agriculture University Hohhot China

ISBN: (纸本)1891706594

The increase in irrigated crop areas lead to the requirements for freshwater will in fact rise significantly. Considering this kind of need, this study aims to develop a low cost irrigation system that provide the monitoring and control of irrigated agriculture using Cloud Computing, Internet of Things(IoT) to improve the efficiency of water use in irrigated agriculture and reduce the cost of the solution. In this paper, an irrigation system driven by Internet of Things and cloud based web application is developed. The web application uses a multi-tenant web framework (MWF) that can provide cost-effective services for scenarios where a large number of small tenants, only some of which are active at the same time. The system uses wireless sensor network(WSN) communication technology to integrate various sensors, solenoid valves and edge devices scattered throughout the crop area for monitoring and control. The solution significantly improves water use efficiency while reducing costs, making it attractive to users. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Solenoid valves

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Semantic Segmentation of Remote Sensing Images Based on Swin Transformer 3

Semantic Segmentation of Remote Sensing Images Based on Swin...

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3rd International Conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2023

作者： Lin, Yinghao Zhao, Shihao Wang, Yuye Xie, Yi Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University Shenzhen China

ISBN: (纸本)9781643684444

With the evolution of Earth observation technology and remote sensing technologies, the amount of data available for high-resolution remote sensing images has exploded, and high-precision image segmentation has become a current research hotspot. Semantic segmentation technology is becoming increasingly important in fields such as urban planning, land use management, and autonomous driving. Large disparities within intraclass and modest differences intraclass are hallmarks of high resolution remote sensing pictures. Traditional image semantic segmentation methods rely on human-computer interaction and have poor generalization ability. When facing remote sensing images with rich types of ground objects and significant differences in target scales, the segmentation accuracy is not high. In this paper, we suggest an UperSwin decoder structure. The decoder includes several Swin transformer blocks and a fusion upsampling module, where the multi head contextual attention module in the Swin transformer block simultaneously uses multi-scale features and upsampling output features from the backbone network. In addition, the fusion upsampling module concatenates the backbone network features with the output features of the Swin transformer block, and then performs upsampling operations, preserving more detailed information. This article evaluates the accuracy and intersection ratio indicators on the Potsdam and Vaihingen datasets, verifying the feasibility and effectiveness of the model. © 2023 The Authors.

关键词： Land use

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Fusion Attention-ASPP and Context Semantic for Complex Scene Semantic Segmentation

Fusion Attention-ASPP and Context Semantic for Complex Scene...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Lin, YingHao Zhao, ShiHao Cai, Kun Qiao, BaoJun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

Semantic segmentation is widely used in remote sensing data extraction and classification. Existing semantic segmentation networks focus on capturing contextual information in many different ways, simply fusing features at different levels, and ultimately improving the accuracy of semantic segmentation. However, low-level semantic features lack spatial context guidance, and high-level semantic features tend to encode large objects with coarse spatial details, making segmentation results prone to losing fine details. In this paper, we analyze the advantages and disadvantages of different levels of feature maps, and enhance the feature representation from two aspects to solve this problem. On the one hand, inspired by the architectural idea of atrous spatial pyramic pooling (ASPP), we adjust the structure of ASPP module and add the attention module to ASPP, and a new Attention-ASPP(AASPP) module is constructed in this paper. On the other hand, feature information such as boundary contours is enhanced by channel attention modeling, thereby improving local detail representation. Comprehensive experimental results show that our model framework achieves excellent segmentation performance on two public datasets, WHU building dataset and ISPRS Potsdam dataset. © 2023 SPIE.

关键词： Semantic Segmentation

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CHSTS: Chameleon Hash based on Schnorr threshold signature on Editable Blockchain

CHSTS: Chameleon Hash based on Schnorr threshold signature o...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Yuan, Linlin Yang, Yuxiang Tan, Chaoyue Dou, Hui Luo, Yun Chen, Yuling College of Information Engineering Guizhou Open University Guiyang China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350304602

Blockchain's immutability, while a core feature, can pose challenges in cases involving sensitive information or compliance with legal regulations, hindering its development. Many subsequent works designed editable blockchains based on the chameleon hash function proposed by H. Krawczyk et al. to solve the above problems. However, existing editable blockchain solutions lack security and are inefficient. To address the security and efficiency issues, we introduce the novel concept of Chameleon hashing based on Schnorr threshold signatures (CHSTS). The CHSTS scheme has double trapdoors, the publisher of the message owns the long-term trapdoor, and the editorial committee owns the ephemeral trapdoor. Message publishers can collaborate with the editorial committee to modify their published information, preventing the exposure of private data, facilitating oversight during the editing process, and enabling the tracking of malicious users. We have designed an updatable ephemeral trapdoor based on Schnorr signatures, effectively avoiding the inefficiencies associated with bilinear pairing. Experimental results show that our CHSTS scheme has a significant advantage in computational cost. © 2023 IEEE.

关键词： Blockchain

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