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MMLN: Multi-directional and Multi-constraint Learning Network for Remote Sensing Imagery Semantic Segmentation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 1-16页

作者： Sun, Hang Xie, Yongchang Ren, Dong Wen, Fangqing Tong, Lyuyang Chang, Laibin Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive Technology Shiyan China School of Computer Science Wuhan University Wuhan China

Recent advances in satellite remote sensing technology and computer technology have significantly impacted practical applications in remote sensing image segmentation. However, the prevalent hybrid segmentation models that combine Convolutional Neural Networks (CNNs) and Transformers, often overlook the critical exploration of local and global feature correlations across various scales. This exploration is essential for learning more representative features and strengthening context modeling capabilities. Additionally, the decoding layers of these models do not effectively exploit the pixel-level semantic relationships within cross-layer feature maps, thereby limiting the models' ability to discern small object features. To address these challenges, this paper introduces a Multi-directional and Multi-constraint Learning Network (MMLN) designed for semantic segmentation of remote sensing imagery. This network features a Multi-directional Dynamic Complement Decoder (MDCD), which enhances the interaction between local and global features in the feature space, and subsequently improves the feature discrimination within the segmentation network. Moreover, a Multi-constraint Saliency Boundary-adaptive Module (MSBM) is implemented to reinforce the spatial constraints on saliency at the edge regions and ensure semantic consistency along the mask boundaries. This, in turn, augments the segmentation model's capability to detect small objects. The evaluation on four datasets reveals that the MMLN outperforms the existing state-of-the-art methods in remote sensing imagery segmentation. The code is available at https://***/zhongyas/MMLN. Authors

关键词： Semantics

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Fault prediction of electric vehicle charging stations based on cooperative game strategy and DBO-BiLSTM 6

Fault prediction of electric vehicle charging stations based...

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6th IEEE Advanced information Management, Communicates, Electronic and Automation control Conference, IMCEC 2024

作者： Tian, Liguo Shang, Chunhu Li, Meng Chen, Qingbin Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350316520

Aiming at the problem of high failure rate of electric vehicle charging pile, an electric vehicle charging pile failure prediction method based on cooperative game strategy and dung beetle optimisation algorithm-bidirectional long and short-term memory network (DBO-BiLSTM) is proposed. Firstly, the outliers are handled by parameter statistical distribution, the missing values are handled by mean padding, and the normalisation operation is performed on the processed data;secondly, multiple single-assignment methods are selected to calculate the feature weights, the combined weights are calculated by the cooperative game strategy, and the parameter feature matrix is enlarged;and then, the DBO-BiLSTM model is constructed, and under the simulation experiments, the accuracy rate of the training set and test set, the F1 coefficients are 0.89, 0.89, 0.90 and 0.89, ***, relevant comparison experiments are constructed, and the results show that the proposed model has better performance, which verifies the effectiveness and reasonableness of the proposed model. © 2024 IEEE.

关键词： Brain

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Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model 38

Denoising Diffusion Path: Attribution Noise Reduction with A...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Lei, Yiming Li, Zilong Zhang, Junping Shan, Hongming Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Institute of Science and Technology for Brain-Inspired Intelligence MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University China

The explainability of deep neural networks (DNNs) is critical for trust and reliability in AI systems. Path-based attribution methods, such as integrated gradients (IG), aim to explain predictions by accumulating gradients along a path from a baseline to the target image. However, noise accumulated during this process can significantly distort the explanation. While existing methods primarily concentrate on finding alternative paths to circumvent noise, they overlook a critical issue: intermediate-step images frequently diverge from the distribution of training data, further intensifying the impact of noise. This work presents a novel Denoising Diffusion Path (DDPath) to tackle this challenge by harnessing the power of diffusion models for denoising. By exploiting the inherent ability of diffusion models to progressively remove noise from an image, DDPath constructs a piece-wise linear path. Each segment of this path ensures that samples drawn from a Gaussian distribution are centered around the target image. This approach facilitates a gradual reduction of noise along the path. We further demonstrate that DDPath adheres to essential axiomatic properties for attribution methods and can be seamlessly integrated with existing methods such as IG. Extensive experimental results demonstrate that DDPath can significantly reduce noise in the attributions-resulting in clearer explanations-and achieves better quantitative results than traditional path-based methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Cloud Edge Collaboration-based Architecture for Offshore Floating Photovoltaic Power Plants 25

Cloud Edge Collaboration-based Architecture for Offshore Flo...

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Zheng, Meiling Kong, Xiangyu Wang, Chunjie Yu, Zilong Fu, Qiang Jin, Hui Tianjin University of Technology Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin China Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9798350366600

Affected by offshore wind and wave fluctuations, offshore photovoltaic field station data centers face problems such as low operational energy efficiency, poor interactivity, and duplicated construction of resources. It is difficult to meet the demand for control precision and accuracy of the new power system with prominent digital features. Due to the large-scale centralized access of offshore floating PV to participate in regulating offshore floating PV power station systems, the information layer must process the collected information and then form a regulation strategy to guide the optimal scheduling of the energy layer. To realize the adaptive multi-mode real-time optimization and regulation of offshore floating PV power plants, it is necessary to construct the information-energy coupling architecture of offshore floating PV power plants, which deeply integrates the optimization results of the information layer with the scheduling instructions of the energy layer. Therefore, by constructing a layered and partitioned controller architecture for offshore floating PV power plants, a cloud-side synergistic integrated control system is formed to support implementing offshore floating PV power plant technology programs. The proposed architecture's performance in data transmission, storage, and energy consumption is analyzed through simulation. The results show that compared with the traditional centralized architecture, the construction of a layered and partitioned controller architecture for offshore floating PV power plants can save 330% of the storage capacity of the cloud servers in the big data center and reduce the energy consumption by about 22%, which verifies the validity and reasonableness of the application of the proposed architecture in the actual project. © 2024 IEEE.

关键词： Offshore power plants

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Bearing Remaining Useful Life Prediction Combining Trajectory Similarity and Relevance Vector Machine 41

Bearing Remaining Useful Life Prediction Combining Trajector...

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第41届中国控制会议

作者： Zhuyi Li Kesen Fan Cong Zhang Sihang Zhang Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education Chinaand School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Control Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bearing remaining useful life(RUL) prediction is critical for safe operation of rotating *** this paper,we propose a combined RUL prediction approach that leverages both trajectory similarity and relevance vector machine(RVM).The similarity based prediction relies on historical degradation trajectories that are highly similar to the online data,hence would perform poorly if all historical trajectories have low similarity with the online *** RVM based prediction relies solely on a regression model learned from the available online data,thus gives an inaccurate prediction when insufficient data are available in the early stage of degradation.A weighted sum of these above two predictions is proposed to address the limitation of each single prediction method,whose weights are determined by solving a non-negative least squares fitting *** further improve RUL prediction accuracy,we distinguish between fast and slow degradation modes,so that each mode uses a different set of historical degradation trajectories and kernel *** doing so,we predict RUL under the identified *** case study using the PHM2012 dataset demonstrates the effectiveness of the proposed RUL prediction approach.

关键词： Bearing degeneration remaining useful life prediction relevance vector machine trajectory similarity

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Unequal Error Protection Rateless Codes for Spatial Audio Transmission 24

Unequal Error Protection Rateless Codes for Spatial Audio Tr...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Yin, Junpeng Fu, Yusun Huang, Haobo Tang, Jinhui Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China School of Electronic Information and Electrical Engineering The Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

Spatial audio is an important component of the metaverse. With the rapid increase of the spatial resolution and the number of objects, the data volume of the spatial side information has grown larger. However, the low bit rate transmission of spatial side information in the object-based audio coding methods leads to the low-quality reconstruction of spatial side information with limited bandwidth. To address this issue, this paper constructs a cascade design of spatial audio coding and unequal error protection (UEP) rateless codes. Furthermore, the further protection of specific spatial side information is proposed for different scenarios, which effectively addresses the limitation of traditional spatial audio coding in side information protection. For the situation where the code length K of different importance levels is different, we propose an adaptive degree distribution (ADD), adding K to the selection probability as an influencing factor, which has better performance than other degree distribution for short code length. ADD is used to replace the multiple degree distribution strategy of the traditional unequal error protection scheme, and the block error rate (BER) is reduced nearly 10 times compared with the traditional scheme. © 2024 IEEE.

关键词： Audio signal processing

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Interpretable multi-view attention network for drug-drug interaction prediction

Interpretable multi-view attention network for drug-drug int...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lin, Xuan Wen, Qi Yang, Sijie Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education China Singapore Hunan University College of Information Science and Engineering China

ISBN: (纸本)9798350337488

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI1 model achieved superior performance to state-of-the-art models on DDI prediction. © 2023 IEEE.

关键词： Contrastive learning Drug-drug interaction prediction Interpretable attention network Multi-view learning

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Scientific Reading Comprehension with Sentences Selection and Ranking 12th

Scientific Reading Comprehension with Sentences Selection a...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Chen, Jialei Wang, Weihua Shao, Shuai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446986

Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. However, in the scientific domain, the original text is longer than the general domain. In this paper, we proposed a model that consists of a content retrieval module and a pre-trained model module. The content retrieval module finds the most semantically relevant sentences from the text and re-rank them. The seleted sentences and question will be input into the pre-trained model to get the answers. This model could overcome the length limitation of Transformer model length while achieving impressive results. Our model achieved 0.45 score of RougeL, resulting in the second place in the NLPCC2023 Shared Task2. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： information retrieval

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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A Policy-controlled Signature Scheme for end-to-end Secure Communication 12

A Policy-Controlled Signature Scheme for end-to-end Secure C...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Yu, Feng Meng, Linghui Xi, Jing Li, Xianxian Lu, Qianhui Zhu, Weidong Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology GuiLin China University of SouthEast Jiangsu Provincial Key Laboratory of Netwrok and Information Security Nanjing China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security GuiLin China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University GuiLin China

ISBN: (纸本)9798331511074

In the digitalized society, end-to-end security communication is receiving increasing attention. Issues such as ma-licious sender impersonation and over-privilege communication continue to prevail in end-to-end communication. This paper pro-poses a policy-controlled signature scheme aimed at addressing these concerns through identity verification and permission man-agement, thus establishing a secure mode of communication. The scheme designs policy-controlled signatures with partial policy hiding functionality, employing expressive access structures and garbled Bloom Filter (GBF) to protect the sender's attributes. In comparison to related approaches, this scheme provides benefits in terms of signature computation cost, thereby enhancing the resilience and efficiency of the communication system against attacks. © 2024 IEEE.

关键词： Authentication Protocol

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