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Automated Extraction of Active Offshore Oil/Gas Platforms Based on High-Spatial Resolution SDGSAT-1/GLI data: A Case Study in the Northern Gulf of Mexico 2

Automated Extraction of Active Offshore Oil/Gas Platforms Ba...

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2023 2nd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2023

作者： Chen, Zhongzhong Yan, Lin Qiu, Yujia Liang, Yueji Li, Xiao-Ming College of Geomatics and Geoinformation Guilin University of Technology Guilin541004 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510672949

The acquisition of spatial distribution information on active offshore oil/gas platforms (AOOP) is of importance for effective ocean management and the comprehensive study on marine carbon emissions. In recent years, the methodological framework for AOOP extraction using low-light remote sensing imagery has emerged as a novel and promising approach. Consequently, in this study, we utilized high-resolution low-light images obtained from the Glimmer Imager (GLI) payload aboard the Sustainable Development Goals science Satellite 1 (SDGSAT-1) to facilitate the automatic extraction of AOOP. We applied a time-series low-light remote sensing (TLLRS) method for automatic extraction of AOOP in the northern Gulf of Mexico for the year 2022. The results indicate that the TLLRS method achieves a remarkable precision rate of 95.31% in the automatic extraction of AOOP, with an overall accuracy of 84.92%. Our findings demonstrate the feasibility and efficacy of applying SDGSAT-1 GLI data to monitor AOOP at large-scale. © 2024 SPIE.

关键词： Remote sensing

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Almost sure stability for a class of dual switching linear discrete-time systems

Almost sure stability for a class of dual switching linear d...

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作者： Long, Fei Liu, Cai Ou, Weihua School of Electrical Engineering Guizhou Institute of Technology Guiyang China College of Big Data and Information Engineering Guizhou University Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China

In this paper, a dual switching discrete-time linear system, simultaneously subject to deterministic switching and Markov chain, is considered. This study does not consider the transition probability of the Markov chain as fixed but determined by the current position of deterministic switching. Namely, such dual switching discrete-time linear system is composed of a family of discrete-time Markov jump systems and follows a rule that directs the switching sequences between them. The exponentially almost sure stability problem, for dual switching discrete-time linear system with exponential uncertainty, is addressed by using persistent dwell time and stochastic multi-Lyapunov function. The sufficient conditions for the exponentially almost sure stability of dual switching discrete-time linear system are expressed as linear matrix inequalities. Finally, a simulation example demonstrates the validity of the derived results. © 2020 John Wiley & Sons, Ltd.

关键词： Switching

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Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

SSRN

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SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Reversible data Hiding in Encrypted Images Based on Multi-Predictor and Quad-tree Block Encoding 2022

Reversible Data Hiding in Encrypted Images Based on Multi-Pr...

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7th International Conference on Intelligent Information Technology, ICIIT 2022

作者： Zhang, Huiqi Li, Lin Li, Qingyan School Of Science Jimei University Xiamen361021 China Computer Engineering College Jimei University Xiamen361021 China Digital Fujian Big Data Modeling And Intelligent Computing Institute Jimei University Xiamen361021 China

ISBN: (纸本)9781450396172

With the rapid development of the Internet, people's awareness of privacy protection is also gradually improved. Reversible data hiding of encrypted image is a technique that encrypts the original image and allows the embedding of secret data without knowing the original image information. This method can accurately extract the secret data and reconstruct the original image completely. In this paper, a reversible data hiding algorithm of encrypted image based on multi-predictor and quad-tree block encoding is proposed. First, the original image is divided into 8×8 non-overlapping blocks. Then, multi-predictor is used to predict each block to get the prediction error image, then block scrambling is used to encrypt it. Since the pixel values of the encrypted image are small integer closes to zero, the quad-tree block encoding method can be used to embed a large amount of data. Experimental results show that the embedding rate of this method is better than that of some new algorithms. © 2022 ACM.

关键词： Embeddings

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Forecasting of credit card default based on incremental random forest

Forecasting of credit card default based on incremental rand...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Gao, Xiang Wen, Junhao Zhang, Cheng College of Computer Science Chongqing University Chongqing China College of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Branch of China Minsheng Bank Chongqing China

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Random forests

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Fed2PKD: Bridging Model Diversity in Federated Learning via Two-Pronged Knowledge Distillation

Fed2PKD: Bridging Model Diversity in Federated Learning via ...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Zaipeng Xie Han Xu Xing Gao Junchen Jiang Ruiqian Han Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Heterogeneous federated learning (HFL) enables collaborative learning across clients with diverse model architectures and data distributions while preserving privacy. However, existing HFL approaches often struggle to effectively address the challenges posed by model diversity, leading to suboptimal performance and limited generalization ability. This paper pro-poses Fed2PKD, a novel HFL framework that tackles these challenges through a two-pronged knowledge distillation approach. Fed2PKD combines prototypical contrastive knowledge distillation to align client embeddings with global class prototypes and semi-supervised global knowledge distillation to capture global data characteristics. Experimental results on three benchmarks (MNIST, CIFAR10, and CIFAR100) demonstrate that Fed2PKD significantly outperforms existing state-of-the-art HFL methods, achieving average improvements of up to 30.53%, 13.89%, and 5.80 % in global model accuracy, respectively. Furthermore, Fed2PKD enables personalized models for each client, adapting to their specific data distributions and model architectures while benefiting from global knowledge sharing. Theoretical analysis provides convergence guarantees for Fed2PKD under realistic assumptions. Fed2PKD represents a significant step forward in HFL, unlocking the potential for privacy-preserving collaborative learning in real-world scenarios with model and data diversity.

关键词： Knowledge engineering Adaptation models Cloud computing data privacy Federated learning Computational modeling Prototypes

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Improving QoS of Workloads with CPU Pinning: A Deep Reinforcement Learning Approach

Improving QoS of Workloads with CPU Pinning: A Deep Reinforc...

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International Workshop on Quality of Service

作者： Xi Tao Weipeng Cao Yinghui Pan Ye Liu Zhong Ming Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In cloud computing, tenants tend to select predefined flavors with substantial resources that go beyond their needs, in order to avoid resource starvation. As a result, cloud service providers experience a high allocation ratio with a low utilization ratio of resources. To improve the resource utilization, cloud service providers offer lower-priced resources by building an overcommitted environment to reuse resources. However, the colocation interference of workloads in the overcommitted environment may cause performance anomalies and degrade quality of service (QoS). In this paper, we propose a real-time mechanism to pin a group of workloads to a certain subset of CPUs to increase resource utilization and minimize interference. To be specific, we leverage deep reinforcement learning (DRL) to automatically adjust the CPU pinning configuration in the overcommitted environment according to the performance of workloads.

关键词： Cloud computing Computational modeling Buildings Quality of service Interference Deep reinforcement learning Real-time systems

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MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interaction Sites Prediction Using Squeeze-and-Excitation Capsule Networks

MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interact...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weipeng Lv Changkun Jiang Jianqiang Li College of Computer Science and Software Engineering and the National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task.

关键词： Proteins Deep learning Protein engineering Neurons Predictive models Network architecture Feature extraction

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Explicit Analysis of Age of Synchronization in IoT Networks

Explicit Analysis of Age of Synchronization in IoT Networks

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Zhiwei Jin Siyuan Zhou Yunquan Dong Bin Tang Guoping Tan College of Information Science and Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The Internet of Things (IoT) has the capability to support the synchronous transmission of dynamically sensed environmental status information to base stations. To improve transmission timeliness, previous studies have commonly utilized Age of Information (AoI) as a metric for network performance. However, AoI falls short in capturing status synchronicity, as synchronization requires knowledge of updates from both the transmitter and the receiver, which AoI cannot reflect due to its inability to track changes at the transmitter. In this work, we adopt the Age of Synchronization (AoS) as a metric to quantify the time elapsed since the freshest information at the receiver becomes desynchronized. Specifically, we develop a model for an IoT communication network to derive closed-form expressions for the average peak AoS (PAoS) and average AoS, considering discrete geometric and uniformly distributed packet arrivals. Ad-ditionally, the analysis has been carried out for both first-come-first-served (FCFS) and last-come-first-served with preemptive resume (LCFS-PR) service disciplines. Within these results, we prove that network's synchronization performance worsens with longer packet arrival intervals but improves with higher arrival rates. LCFS-PR strategy makes synchronization independent of packet arrival, solely influenced by service rate. Additionally, AoS consistently remains lower than AoI in the same system, with both tending to converge as packet arrival rate rises.

关键词： Measurement Closed-form solutions Transmitters Processor scheduling Wireless networks Receivers Signal processing Synchronization Internet of Things Queueing analysis

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