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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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Ontology-driven Data Management Design in Healthcare Domain: The ADCATER Experience

Ontology-driven Data Management Design in Healthcare Domain:...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Cocco, Leonardo Fantozzi, Paolo Lembo, Domenico Nanni, Umberto Scafoglieri, Federico Maria Department of Computer Control and Management Engineering University of Rome la Sapienza Rome00185 Italy Department of Law Economics Politics and Modern Languages LUMSA University Rome00192 Italy

In this paper, we outline our experience in implementing the data management component of a data-intensive healthcare application within the ADCATER project (Advanced Digital Solutions for Professional Food and Nutrition Catering Service), where an ad hoc ontology, tailored to the project’s domain, plays a crucial role in driving the system design. We will discuss the creation process of this ontology, its underlying building principles, and how it aids the development of a reconciled database useful to integrate and consolidate heterogeneous sources of information, vital for the proper running of solution at hand. Here, the ontology is essential for harmonizing vocabularies and ensuring the establishment of a schema devoid of inconsistencies. Finally, we will explore how Business Intelligence services, operating on the foundation of the Data Warehouse built upon the reconciled database, are seamlessly aligned with the crafted ontology. © 2024 Copyright for this paper by its authors.

关键词： Data warehouses

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GMA: Gap Imputing Algorithm for time series missing values

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Journal of Electrical Systems and Information Technology 2023年第1期10卷 1-20页

作者： Khattab, Abd Alhamid Rabia Elshennawy, Nada Mohamed Fahmy, Mahmoud Computer and Automatic Control Departmaent Faculty of Engineering Tanta University Tanta Egypt

Data collected from the environment in computer engineering may include missing values due to various factors, such as lost readings from sensors caused by communication errors or power outages. Missing data can result in inaccurate analysis or even false alarms. It is therefore essential to identify missing values and correct them as accurately as possible to ensure the integrity of the analysis and the effectiveness of any decision-making based on the data. This paper presents a new approach, the Gap Imputing Algorithm (GMA), for imputing missing values in time series data. The Gap Imputing Algorithm (GMA) identifies sequences of missing values and determines the periodic time of the time series. Then, it searches for the most similar subsequence from historical data. Unlike previous work, GMA supports any type of time series and is resilient to consecutively missing values with different gaps distances. The experimental findings, which were based on both real-world and benchmark datasets, demonstrate that the GMA framework proposed in this study outperforms other methods in terms of accuracy. Specifically, our proposed method achieves an accuracy score that is 5 to 20% higher than that of other methods. Furthermore, the GMA framework is well suited to handling missing gaps with larger distances, and it produces more accurate imputations, particularly for datasets with strong periodic patterns.

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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14674-14687页

作者： Xu, Zekun Zhou, Yipeng Wen, Shiting Jing, Weipeng Northeast Forestry University College of Computer and Control Engineering Harbin150040 China Macquarie University School of Computing Sydney Australia NingboTech University School of Computer and Data Engineering Ningbo315100 China

Unmanned aerial vehicle (UAV) remote sensing technology has been widely applied in pine wilt disease (PWD) detection. UAV-collected PWD images are characterized by complex backgrounds, small target sizes, and uneven spatial distribution. State-of-the-art generic detection methods typically use Transformers or Mamba-based backbones to mitigate background interference by enhancing the global receptive field. However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. To overcome these challenges, a novel PWD detection method called feature interaction and dependency capturing-YOLO (FIDCYOLO) is proposed in this article. Firstly, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network (SELAN) is proposed to promote information interaction between features, improving the model's learning capability. Then, to address the interference of complex backgrounds, the bidirectional extension of Mamba (Hydra) is improved by employing the partial dependency capture (PDC) module, which captures both global and local dependencies for suppressing background information. Additionally, the feature aggregation-spatial pyramid pooling fast (FA-SPPF) module is introduced in front of PDC, which aggregates cross-level features to enhance detection performance. Finally, we use ADown as the downsampling module to avoid the loss of target information. Extensive experiments on our self-constructed PWD dataset and the public DIOR dataset demonstrate the excellent performance of FIDC-YOLO. © 2008-2012 IEEE.

关键词： Drones

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Intelligent Decision Algorithm for Photovoltaic Power Generation control Based on Feature Neural Computing Modeling

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Tang, Mingze School of Control and Computer Engineering North China Electric Power University Beijing100096 China

The integration of renewable energy sources into the power grid, especially photovoltaic (PV) systems, has seen a significant upsurge due to the global push for sustainable energy. However, the variable nature of solar energy poses unique challenges in the effective management and control of PV power generation. This study introduces an innovative approach to intelligent decision-making in PV power generation control, leveraging deep learning techniques for temporal data feature modeling. By collecting and analyzing time-series data from PV systems, the proposed method utilizes feature neural computing to model the inherent characteristics of the data, facilitating effective temporal feature classification. The classification results are then employed to inform intelligent control strategies, optimizing the efficiency and reliability of PV power generation. This article proposes an end-to-end neural network structure that can simultaneously mine multi-scale local information and global temporal correlation information in sequences. This structure is primarily achieved through a module. The module consists of two parallel branches. One branch improves the InceptionTime module with depthwise separable convolution to extract multi-scale subsequence information. The other branch employs multi-head self-attention technology with added layer positional encoding to extract global temporal correlation information in sequences. © 2024 Mingze Tang, published by Sciendo.

关键词： Solar energy

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine Learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Automation Detection, Segmentation, and Classification for Ocean Reinforced Concrete Corrosion Based on Artificial Intelligence

SSRN

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SSRN 2024年

作者： Cui, Zhendong Jia, Zhitong Zhao, Yang Fu, Mingjiao School of Computer and Control Engineering Yantai University Yantai264000 China

Based on artificial intelligence technology, it is of great significance to automatically identify and determine the degree of corrosion damage for Ocean reinforced ***, an enhanced and comprehensive non-destructive testing framework was proposed for the automatic detection of corrosion in ocean concrete and the evaluation of its corrosion ***, a dataset containing 3,700 images of offshore ocean concrete corrosion has been established, which captured in various geographical locations, weather conditions, lighting situations, and engineering ***, a Faster-DeepLabV3+ model for damage segmentation and grade classification was present. An Efficient Channel Attention structure was introduced at the front of the inverted residual module of MobileNetV2 to reduce the model parameters and computation while maintaining high-accuracy segmentation results;a stick pooling layer was introduced in Atrous Spatial Pyramid Pooling to improve the model's ability to perceive damage at different scales;a combination of loss functions with Dice loss function and focal loss function was used to enhance the model's capacity to detect outlines and finer ***, different corrosion levels were identified to categorize cracks, spalling, and rebar corrosion in marine concrete into three conditions, and accurate delineation of corroded areas and rebar rusting was successfully ***, experiments are carried out on the constructed data set, and by comparing the intersection merging ratio, average pixel accuracy, pixel accuracy and frames per second, the proposed model is proved to be accurate and efficient. © 2024, The Authors. All rights reserved.

关键词： Nondestructive examination

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SSIG: Single-Sample Information Gain Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

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Chinese Journal of Electronics 2021年第2期30卷 303-312页

作者： ZHANG Yuanyuan WANG Ziqi WANG Shudong KOU Chuanhua School of Information and Control Engineering Qingdao University of Technology College of Computer Science and Technology China University of Petroleum (East China)

Different living environments of cancer samples lead to different molecular mechanisms of cancer development, which in turn leads to different cancer subtypes. How to identify cancer subtypes is a key issue for the realization of precision medicine. With the development of high-throughput technologies, multi-omics data which can better understand different causes of cancer have emerged. However, the current methods of analyzing cancer subtypes using multi-omics data is mostly derived from population cancer sample data and ignores the differences between different cancer ***, the joint analysis of multi-omics based on a single sample may reveal more information about the differences between individual cancers. A strategy for identifying cancer subtypes is proposed based on Single-sample information gain(SSIG) which construct sample feature matrix by considering the heterogeneity of sample. Applying this strategy to current popular subtype identification methods, cancer subtypes can be identified more accurately and the mechanism of cancer can be found from the perspective of a single sample. By comparing different methods in different clustering measure, and using survival analysis, it is shown that SSIG is more suitable for cancer subtype identification than the original multi-omics data, and it is easier to mine the cancer subtype classification mechanism hidden behind the data.

关键词： identify cancer subtypes SSIG medical computing cancer subtype classification mechanism Single-sample information gain model different cancer subtypes original multiomics data genetics cancer subtype identification current popular subtype identification methods population cancer sample data different cancer samples data analysis cancer development genomics cancer sample feature matrix different molecular mechanisms different living environments bioinformatics individual cancers different clustering measure

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Recognition of Residual Current A+6 Waveform Based on Random Forest Algorithm 6

Recognition of Residual Current A+6 Waveform Based on Random...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Zhao, Pei Wang, Xiaohui Liu, Fanyu Ou, Ziye Jia, Yunshuo State Grid Hebei Electric Power Marketing Service Center Hebei Shijiazhuang China North China Electric Power University School of Control and Computer Engineering Hebei Baoding China

ISBN: (纸本)9798331541750

In the field of power system safety protection, residual current detection technology plays a vital role, which is essential to ensure the safe and stable operation of the system. Accurate identification of the waveform of the residual current is necessary for the residual current protection device to perform the disconnect operation correctly. At present, the recognition of specific waveforms such as F, A0, A90, A135, and SDC in the A+6 waveform is not very sufficient, especially in terms of compatibility and recognition efficiency at 50Hz and 60Hz frequencies. In this study, we propose a new residual current A+6 waveform recognition technology based on a random forest algorithm, which improves recognition accuracy by selecting 13 time-domain statistical features. The experimental results show that the accuracy of the proposed method for the recognition of alternating current, F, A0, A90, A135, and SDC waveforms and their amplitudes at 50Hz and 60Hz on the experimental bench reaches 98.6% and 99.3%, respectively. In addition, the generated decision trees can be easily integrated into embedded systems, showing high practical value. © 2024 IEEE.

关键词： Random forests

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers 63

Unpredictable Switching for Cyber-Physical Security Against ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kanellopoulos, Aris Fotiadis, Filippos Vamvoudakis, Kyriakos G. Sandberg, Henrik Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden Georgia Institute of Technology Daniel Guggenheim School of Aerospace Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350316339

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method. © 2024 IEEE.

关键词： Cyber attacks

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