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Deep learning based water leakage detection for shield tunnel lining

Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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A Novel Deep Learning Based Approach for Detecting and Localization of Pulmonary Cancer 14

A Novel Deep Learning Based Approach for Detecting and Local...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Manoj, Diwakar Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

In the ever-evolving field of scientific diagnostics, the early diagnosis of pulmonary most cancers continues a key undertaking. This observation proposed a unique deep learning-primarily based approach, in particular using Generative Adversarial Networks (GANs), intending to modernise the identification and localization of pulmonary malignancies through scientific imaging. Our models, trained using a varied dataset, demonstrated a promising accuracy fee of 70% within the sample set, suggesting its ability to adeptly differentiate between malignant and non-malignant instances in scientific images. While the conclusions suggest a significant growth in lung cancer detection, also they highlight locations demanding in addition refinement. The balance of technological prowess and scientific significance, as reflected through criteria like sensitivity and specificity, remains a focus topic for future projects. The outcomes of this research are substantial. Beyond the on the spot discoveries, the take a look at emphasizes the transformational possibility of incorporating sophisticated AI approaches into healthcare. As the scientific network grapples with the difficulties of early cancer identification, gear like the one displayed in this study ought to usher in a new age in diagnostics-marked by accuracy, efficiency, and patient-centricity. In conclusion, this have a look at now not simplest adds a fresh diagnostic tool to the sector but moreover sets the way for future innovations within the confluence of AI and healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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Machine Learning Methods for the Timely Identification of Autism Spectrum Disorder in Toddlers 4

Machine Learning Methods for the Timely Identification of Au...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jain, Sweta Tripathy, Hrudaya Kumar Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

Autism Spectrum Disorder (ASD) is an evolving ailment that upsets the nervous system, by means of a widespread range of symptoms and severity levels. Early ASD identification is challenging and there is no treatment, early detection and intervention are vital for improving outcomes in individuals with ASD. This research, explored the application of machine learning (ML) techniques, specifically Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting (GB), for detection of ASD using a dataset focused on screening toddlers. The dataset, obtained from Kaggle in the 2018 release, encompasses demographic information, medical history, and screening test results for toddlers. The presented system employs leading edge algorithms to analyze the dataset and build predictive models for ASD detection. SVM is preferred due to its capability to lever multifaceted associations in high-dimensional data, while RF excels in handling diverse feature sets and capturing ***, known for its ensemble learning capabilities, is also employed to boost predictive performance. The assessment of these models is accomplished via bench mark estimation metrics in ML. This approach aims at an effective model for ASD detection in children based on the characteristics of the dataset. Additionally, feature importance analysis is conducted to gain insights into the factors contributing to ASD prediction. The implementation results reveals that SVM algorithm outperforms in the proposed ASD diagnosis. Early identification using ML techniques could assist healthcare professionals and caretakers in timely intervention, ultimately improving the prolonged effects for persons with ASD. © 2024 IEEE.

关键词： Support vector machines

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Performance Enhancement of IoT LoRa Network for Mobile Devices based on Improved AMILCC Technique 1

Performance Enhancement of IoT LoRa Network for Mobile Devic...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Venkatesan, C. Thamaraimanalan, T. Mohammed Ibrahim, M. Vinoly, G. Ramkumar, M. Saranya, K. HKBK College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Tamilnadu Thiruchengode India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

In Internet of Things (IoT) networks, long-range (LoRa) technology is well-known for its wide communication range, but using it with mobile devices presents unique difficulties. Frequently occurring channel attenuation, which causes packet loss, increased energy consumption, and longer transmission times, is one of these difficulties. An improved Adaptive Mobility-based IoT LoRa Clustering Communication (AMILCC) strategy addressed to solve these problems. AMILCC partitions the network into optimal Spreading Factor (SF) areas by using a 2D random waypoint mobility model. The Hybrid Adaptive Data Rate (HADR) technique, which combines intra-and inter-SF region modifications to improve resource allocation for mobile IoT LoRa devices, is a key component of AMILCC. The simulation findings demonstrate the advantages of AMILCC over conventional stationary techniques, even under low mobility conditions (up to 5 m/s). In particular, AMILCC decreases energy usage by 56.5%, improves packet success ratio (PSR) by 72 %, and decreases end-to-end delay by 45.32%. These outcomes demonstrate how well AMILCC works to enhance efficiency and performance for mobile LoRa applications. © 2024 IEEE.

关键词： Resource allocation

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QR Code with Block Chain Technology for Medical Device Ownership 9

QR Code with Block Chain Technology for Medical Device Owner...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Makanyadevi, K. Rithika, S. Biratheep, S. Subanki, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur India

ISBN: (纸本)9798350397376

Medical device quality and safety are important because they affect people's lives and health, and society has placed a high value on these factors. Healthcare supply chain has been majorly intruded for fake medical devices and technologies. Products that are counterfeited are frequently created to reach profit from the original product's lower price. Because of its high risk, a traceability system must be set up. Most businesses are making greater efforts to prevent counterfeiting. The QR code system presented in this project is application-, security-and privacy-focused. The existing system uses non-fungible token (NFT) based solution that enhances the tokenization protocols. In our paper we propose the usage of QR technology. It is easy and affordable to create a QR code for a medical device's identification. Because QR codes are simple to use. This system stores the information in decentralized storage of the Blockchain technology system to verify the legitimacy of medical equipment, ensuring that users are not dependent on outside apps. Blockchain which is defined as the distributed ledger system that is based upon apowerful cryptographic technique and is used to capture and preserve information about medical devices, making it difficult for anybody to tamper with them. Along with Blockchain, the usage of decentralized system makes the privacy of the data more effective. The paper also allows the customer end to view and verify the medical device ownership. © 2023 IEEE.

关键词： Blockchain

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A Survey Analysis on Decentralized Land Registry System Using Blockchain and Smart Contracts 1

A Survey Analysis on Decentralized Land Registry System Usin...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Narayanaprakash, K. Malarchelvi, P. D. Sheba Kezia Ranjithkumar, S. Sridevi, R. Anitha, P. Kumar, N.Suresh Alliance University Anekal Department of Computer Science and Engineering Bengaluru562 106 India Saranathan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Trichy India Oasys Institute of Technology Department of Computer Science and Engineering Pulivalam Trichy India Bangalore India

ISBN: (纸本)9798350341737

This study investigates the potential use of blockchain technology and smart contracts to construct a decentralised land registry system. It looks at the problems with traditional land registry systems and provides a technical breakdown of how to create a blockchain-based land registry system. The research also evaluates the countries that have already adopted such systems and discusses the possible adoption of a blockchain-based land register system in India. The implementation of a blockchain-based land registration system, per the research, can increase the security and efficacy of land transactions, reduce fraud and mistakes, and promote economic growth. © 2023 IEEE.

关键词： Smart contract

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Security and Privacy Preservation Method Based on Data Disturbance for Collaborative Edge Computing for Social-Internet-of-Things (S-IoT)

Security and Privacy Preservation Method Based on Data Distu...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Rahul Venkatesh Venugopal, K.R. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bangaluru India

ISBN: (纸本)9798350315455

Socia1 Internet of Things (SIoT) is new paradigm that integrate human social with physical Internet of Things (IoT) systems, social IoT is ray of hope in providing ubiquitous connectivity among users. The collaborative edge computing for SIoT is significant because it ensures human social features and connections among IoT devices and users. But it is formidable to perform CEC for social IoT due to user privacy, data security and network heterogeneity and so on. The problem of computing and storage in IoT devices is solved by Collaborative edge computing (CEC) with deep learning (DL). To prevent privacy breach and security threats at edge network and edge devices in SIoT system. This research work discusses the data protection method based on data disturbance approach. To ensure correctness of model adversarial training viewpoint is introduced. The proposed method is capable of predicting contextual filtering words in adversarial text examples, and adaptively determine word replacement order. The proposed method BESAW, a BERT Based Simulated Annealing Word algorithm is used. BESAW compare with five existing methods includes TextFooler, BAE, BERT-Attack, PWWS, and PSO in experimental analysis. © 2023 IEEE.

关键词： Collaborative edge computing (CEC) Internet-of-Things (IoT) Sentence similarity analysis Simulated Annealing (SA)

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A Modular-based Strategy for Mitigating Gradient Conflicts in Simultaneous Speech Translation

A Modular-based Strategy for Mitigating Gradient Conflicts i...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Xiaoqian Du, Yangfan Wang, Jianjin Ge, Yuan Xu, Chen Xiao, Tong Chen, Guocheng Zhu, Jingbo School of Computer Science and Engineering Northeastern University Shenyang China College of Computer Science and Technology Harbin Engineering University Harbin China NiuTrans Research Shenyang China

ISBN: (纸本)9798350368741

Simultaneous Speech Translation (SimulST) involves generating target language text while continuously processing streaming speech input, presenting significant real-time challenges. Multi-task learning is often employed to enhance SimulST performance but introduces optimization conflicts between primary and auxiliary tasks, potentially compromising overall efficiency. The existing model-level conflict resolution methods are not well-suited for this task which exacerbates inefficiencies and leads to high GPU memory consumption. To address these challenges, we propose a Modular Gradient Conflict Mitigation (MGCM) strategy that detects conflicts at a finer-grained modular level and resolves them utilizing gradient projection. Experimental results demonstrate that MGCM significantly improves SimulST performance, particularly under medium and high latency conditions, achieving a 0.68 BLEU score gain in offline tasks. Additionally, MGCM reduces GPU memory consumption by over 95% compared to other conflict mitigation methods, establishing it as a robust solution for SimulST tasks. © 2025 IEEE.

关键词： GPU Memory Efficiency Gradient Conflict Mitigation Multi-task Learning Simultaneous Speech Translation

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Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE 3

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Selvam, Prabu Nevgi, Akshaj Gunasundari, C. Sowrish, V.K. Natarajan, B. Jessika, S. Sharon School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Chennai Vellore India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India VIT University Department of Computer Science and Engineering Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

ISBN: (纸本)9798350317022

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-Processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results. © 2023 IEEE.

关键词： connected component edge detector image processing MSER Text detection

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