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Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

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Human-Centric Intelligent Systems 2023年第1期3卷 37-46页

作者： Munawar, Hafiz Suliman Hammad, Ahmed W. A. Waller, S. Travis Shahzad, Danish Islam, Md. Rafiqul School of the Built Environment University of New South Wales Sydney Australia Lighthouse Professor and Chair of Transport Modelling and Simulation “Friedrich List” Faculty of Transport and Traffic Sciences Technische Universität Dresden Dresden Germany Department of Visual Computing University of Saarland Saarbrücken Germany Data Science Institute (DSI) University of Technology Sydney (UTS) Sydney Australia

Road network detection is critical to enhance disaster response and detecting a safe evacuation route. Due to expanding computational capacity, road extraction from aerial imagery has been investigated extensively in the literature, specifically in the last decade. Previous studies have mainly proposed methods based on pixel classification or image segmentation as road/non-road images, such as thresholding, edge-based segmentation, k-means clustering, histogram-based segmentation, etc. However, these methods have limitations of over-segmentation, sensitivity to noise, and distortion in images. This study considers the case study of Hawkesbury Nepean valley, NSW, Australia, which is prone to flood and has been selected for road network extraction. For road area extraction, the application of semantic segmentation along with residual learning and U-Net is suggested. Public road datasets were used for training and testing purposes. The study suggested a framework to train and test datasets with the application of the deep ResUnet architecture. Based on maximal similarity, the regions were merged, and the road network was extracted with the B-snake algorithm application. The proposed framework (baseline + region merging + B-snake) improved performance when evaluated on the synthetically modified dataset. It was evident that in comparison with the baseline, region merging and addition of the B-snake algorithm improved significantly, achieving a value of 0.92% for precision and 0.89% for recall.

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Multi-Graph Fusion Networks for Urban Region Embedding

arXiv

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arXiv 2022年

作者： Wu, Shangbin Yan, Xu Fan, Xiaoliang Pan, Shirui Zhu, Shichao Zheng, Chuanpan Cheng, Ming Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Department of Computer Science and Technology Xiamen University China Department of Data Science and AI Faculty of Information Technology Monash University Australia University of Chinese Academy of Sciences China

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 13.11% improvement. https://***/wushangbin/MGFN. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Incremental Feature Selection for Dynamic Incomplete data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Neural Networks for Fault diagnosis in Electrical Machine

Neural Networks for Fault diagnosis in Electrical Machine

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Intelligent Technologies (ACOIT), Asian Conference on

作者： R. Vinston Raja J. Raja Sreenivasulu Gogula Atul Katiyar T. Thilagam V. S. Bhagavan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) Vardhaman College of Engineering Hyderabad Telangana India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Science and Engineering S.A Engineering College Chennai Tamil Nadu India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Finding electrical equipment problems quickly is essential for minimising downtime and ensuring ongoing functioning. When it comes to early defect identification, traditional methods like human inspection or sensor-based surveillance might not cut it. Another alternative to employing machine learning algorithms to analyse complex patterns in sensor data is neural networks. With an emphasis on motor drives, this article lays forth a method for identifying electrical equipment issues using neural networks. The network has been trained to identify various issues, such as imbalanced rotors, broken stator windings, and failing bearings, by examining historical data. The trial findings prove that the suggested method may effectively find issues, make things more reliable, and cut down on maintenance expenses.

关键词： Fault diagnosis Motor drives Machine learning algorithms Surveillance Neural networks Stator windings Rotors Machine learning Maintenance Predictive maintenance

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Guided Image-to-Image Translation by Discriminator-Generator Communication

arXiv

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arXiv 2023年

作者： Cao, Yuanjiang Yao, Lina Pan, Le Sheng, Quan Z. Chang, Xiaojun The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia CSIRO’s Data61 Australia Department of Computing Mac-quarie University SydneyNSW Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia

The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention. One major branch of this research is to formulate I2I translation based on Generative Adversarial Network (GAN). As a zero-sum game, GAN can be reformulated as a Partially-observed Markov Decision Process (POMDP) for generators, where generators cannot access full state information of their environments. This formulation illustrates the information insufficiency in the GAN training. To mitigate this problem, we propose to add a communication channel between discriminators and generators. We explore multiple architecture designs to integrate the communication mechanism into the I2I translation framework. To validate the performance of the proposed approach, we have conducted extensive experiments on various benchmark datasets. The experimental results confirm the superiority of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Extended Kalman Filter Algorithm to Detect False data Injection in Wireless Sensor Network

Extended Kalman Filter Algorithm to Detect False Data Inject...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： R Babuji J. Jeya Ganesan D. Lekha M. Karthikeyan D. M. Kalai Selvi Department of Electronics and Communication Engineering RMK Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science And Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio.

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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HFM++: An Enhanced Holographic Factorization Machine for Recommendation 19th

HFM++: An Enhanced Holographic Factorization Machine for Rec...

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19th Australasian Conference on data Mining, AusDM 2021

作者： Fang, Zhengxin Qu, Mingcheng Zhang, Shuai Zhang, Jingyu Yuan, Yi Yao, Lina Chen, Shiping Faculty of Computing Harbin Institute of Technology Harbin China ETH Zurich Zürich Switzerland School of Computer Science and Engineering University of New South Wales Kensington Australia School of Engineering University of Sydney Sydney Australia CSIRO Data61 Sydney Australia

ISBN: (纸本)9789811685309

Recommender systems are essential and are playing a more and more important role in our daily life, ranging from entertainment to online shopping. They have great commercial value, not only can improve the user experience by saving users time to locate related items, but also increase the exposure rate of long-tail items. Factorization machines (FMs) have been serving as an effective go-to recommender algorithm for years. Yet, it is not without disadvantages, such as mediocre performances. In this paper, we propose an enhanced factorization model, namely, enhanced holographic factorization machines (or HFM++). Our model integrates a Restricted Boltzmann Machine (RBM) and a generalized matrix factorization (GMF) into holograph FM, resulting in a more powerful recommendation model that inherits the benefits of all these three models. To evaluate the effectiveness and accuracy of HFM++, we conduct experiments with several well-known public benchmarking datasets and compare them against a number of baseline models. The experimental results show that HFM++ obtains the best performances across all metrics, outperforming recently published baselines. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Usharani V. M. Sivagami K. Saravanan S. Pushparani K. Sashi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations.

关键词： Cloud computing Technological innovation Costs Biological system modeling Machine learning Predictive models Maintenance

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Classification-based self-learning for weakly supervised bilingual lexicon induction 58

Classification-based self-learning for weakly supervised bil...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Karan, Mladen Vulić, Ivan Korhonen, Anna Glavaš, Goran TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Language Technology Lab TAL University of Cambridge Data and Web Science Group University of Mannheim

ISBN: (纸本)9781952148255

Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present CLASSYMAP, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of CLASSYMAP for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs. © 2020 Association for Computational Linguistics

关键词： Iterative methods

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