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2nd International Conference on River Corridor Research and Management, RCRM 2022

作者： Uma Narayan, M. Bharti, Rishikesh Nair, Archana M. Earth System Science and Engineering Department of Civil Engineering Indian Institute of Technology Guwahati Assam India

ISBN: (纸本)9789819944224

Siang River, the upstream subbasin of the mighty Brahmaputra in northeast India, enters the Indian territory from the Tibetan plateau through the Namche Barwa massif carrying the footprints of tectonic perturbations. This study uses morphometric, morphotectonic and profile analyses of the Siang basin to quantify the landscape responses to tectonics and spatiotemporal variability in the responses. The tectonic imprints on the river basin's morphological characteristics were studied using SRTM DEM-based morphometric analysis and geomorphic indices. The hypsometric integral (‘H.I'), asymmetry ratio (‘Af') and stream length gradient index (‘S.L') are to mention a few morphotectonic parameters analysed in this study. The higher asymmetry value with the sigmoidal to slightly convex up hypsometric curve of the Siang River indicates an unstable tectonic setting of the river basin. An increasing trend for the ‘S.L' index is visible along the upstream Siang River. The gradient changes with sharp elevation differences were evident from the river's long profile. The ‘chi' plot displays disequilibrium from the steady state, as diversions from linearity, indicating an imbalance between the rate of erosion and upliftment at the upstream reach of the river. These drainage responses of the Siang basin can be attributed as typical landscape responses to the ongoing upliftment and the active tectonics of the area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tectonics

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Underwater Frequency Selection Algorithm Based on the Received Signal

Underwater Frequency Selection Algorithm Based on the Receiv...

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2025 IEEE Underwater technology, UT 2025

作者： Alahmad, Raji Albaroudi, Mohammad Alraee, Abdullah Ishii, Kazuo Kyushu Institute of Technology Department of Human Intelligence Systems Fukuoka Kitakyushu Japan Kyushu Institute of Technology Department of Life Science and System Engineering Fukuoka Kitakyushu Japan

ISBN: (纸本)9798331534189

Underwater communication is widely regarded as one of the most significant challenges due to the unique physical properties of water. Among the available communication methods, radiofrequency communication offers higher data transmission rates than acoustic communication. However, higher frequencies suffer from substantial attenuation in water, making it difficult to achieve long-distance transmission. Addressing the tradeoff between transmission rate and communication distance is essential for achieving optimal performance. Unlike conventional systems that rely on fixed operating frequencies, this study introduces a dynamic frequency selection algorithm. This algorithm dynamically adjusts the operating frequency in real-time based on channel conditions and transmission rate feedback, ensuring a more adaptive and efficient communication system. To validate this approach, a dipole antenna with multiple impedance matching was designed and tested in a water tank environment. The experimental results revealed that bandwidth performance varied across different frequencies. Transmission rate analysis showed that higher frequencies provided higher data transmission over short distances. Conversely, lower frequencies exhibited better signal stability and reliability for long-distance communication due to their reduced attenuation. These findings highlight the importance of adaptive frequency selection in underwater radiofrequency communication, where a balance between transmission rate and communication distance must be achieved to support various operational requirements. © 2025 IEEE.

关键词： Adaptive algorithms

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Curve Monitoring and Alert system for Smart Transportation 1

Curve Monitoring and Alert System for Smart Transportation

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa K, R. Viswanathan, Shrinithi Jayarathinam, Sandhiya Balasubramaniam, Vikas Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu Korea Republic of

ISBN: (纸本)9798350372748

The proposed IoT-based Smart Curve Monitoring and Alert system is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE.

关键词： Convolution

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Fusion of Traditional and Deep Learning Models for Underwater Image Quality Assessment 8

Fusion of Traditional and Deep Learning Models for Underwate...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Dhivya, R.M. Nagasai, V. Masilamani, V. Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai600127 India

ISBN: (纸本)9798331531539

Underwater image quality is significantly affected by the unique challenges of the underwater environment, including light attenuation, color distortion, backscatter, and the presence of particulate matter. These factors reduce visibility, alter color perception, and introduce noise and artifacts that degrade the overall quality of captured images. Ensuring high-quality underwater images is essential for various applications, including marine biology, environmental monitoring, underwater navigation, and industrial operations. This research proposes a novel approach to evaluating underwater image quality by integrating conventional machine learning methods with advanced deep learning architectures. The fusion process, employing Support Vector Regression (SVR), known as FTDUIQA (Fusion of Traditional and Deep Learning Models for Underwater Image Quality Assessment), combines well-established deep learning-based models - CNNIQA (Convolutional Neural Network for No-Reference Image Quality Assessment), DBCNN (Deep Bilinear Convolutional Neural Network), and HyperIQA (HyperNet for Image Quality Assessment) - with the classical IQA model, MSAEQA (Multi-scale and Antagonistic Energy Distribution). Additionally, we incorporate a modified deep learning model, MAUIQA (Multi-scale AttentionNet for Underwater Image Quality Assessment), which has been adapted from its image super-resolution application. This hybrid approach leverages the strengths of both deep learning and traditional techniques to provide a robust and comprehensive assessment of underwater image quality. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Q-Learning Approach to Intrusion Detection and Prevention systems: Enhancing Cybersecurity through Intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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Machine learning–based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects

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Environmental science and Pollution Research 2025年第13期32卷 8160-8182页

作者： Mastoi, Aamir Khan Hameed, Saifal Ali, Mutahar Traore, Abdoul Fatah Institute of Geotechnical and Underground Engineering School of Civil Engineering & Hydraulics Huazhong University of Science and Technology 318 6th Building of the West Wuhan430074 China Civil Engineering Department Aror University of Art Architecture Design and Heritage Sindh Sukkur Pakistan Department of Civil Engineering Quaid-E-Awam University of Engineering Sciences and Technology Sindh Nawabshah67450 Pakistan

This research investigates the application of machine learning techniques for predicting unconfined compressive strength (UCS) and contaminant leachability in dredged contaminated sediments (DCS) with implications for land reclamation projects. Traditionally, determining these parameters has been challenging, costly, and time-consuming, hindering efficient project planning and execution. Therefore, this study evaluated the efficacy of two machine learning models, namely extreme gradient boosting (XGBoost) and decision tree (DT), in improving prediction accuracy and reducing the need for resource-intensive testing procedures. The models were constructed using 165 data samples and 6 input parameters. The models’ generalizability and predictive performance were evaluated using Monte Carlo and K-fold cross-validation approaches. Results indicate that the XGBoost model outperforms DT, exhibiting superior prediction accuracy and consistency with best typical metrics such as higher adjusted R-squared (Adj. R2) and lower root mean square error (RMSE) and mean absolute error (MAE) values. The sensitivity analysis of models shows that the ground granulated blast furnace slag (GGBS) content has a significant impact on the prediction of UCS, whereas the zinc concentration level (Z) has a significant effect on the leachability of zinc. These findings demonstrate the ability of machine learning to refine prediction algorithms for DCS performance, allowing for more efficient and cost-effective land reclamation initiatives. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Blast furnaces

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Product platform design for minimum cost in mass customization manufacturing environment 21

Product platform design for minimum cost in mass customizati...

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21st International Conference on Applied Mechanics and Mechanical engineering, AMME 2024

作者： Ismail, T. Saeed, Ahmed Elkharbotly, A. Zamzam, N. Basic and Applied Sciences Department Faculty of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Egypt Design and Production Engineering Department Ain Shams University Egypt

Nowadays manufacturers are facing several challenges to compete in the marketplace. One of the main challenges can be described by the need to offer a range of products and categories in addition to shortening the time during which the product is brought to the customer while maintaining low costs to achieve maximum profitability. Several approaches emerged during the past couple of decades that enabled designers and manufacturers to offer the product variety within a product family on one hand and allowing flexibility and responsiveness at the manufacturer's side on the other hand. This study addressed the problem of product platform design with the objective of minimizing the manufacturing costs incurred in the platform manufacturing phase and the mass customization phase. A genetic algorithm model that minimizes the total manufacturing cost is developed where optimum product platform design is determined. The developed model is used to study the effect of demand proportion of different product variants within a product family on the platform design and the total manufacturing cost. © Published under licence by IOP Publishing Ltd.

关键词： Smart manufacturing

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Consensus Control of Multi-Agent systems Using Fault-Estimation-in-the-Loop:Dynamic Event-Triggered Case

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1440-1451页

作者： Yamei Ju Derui Ding Xiao He Qing-Long Han Guoliang Wei Shanghai Key Lab of Modern Optical System the Department of Control Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Automation BNRistTsinghua UniversityBeijing 100084China College of Science University of Shanghai for Science and TechnologyShanghai 200093China

The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variable is utilized to improve the utilization of communication ***,a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation(FC)is adopted to realize the demand of reliability and safety of addressed ***,a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the l_(2)-l_(∞)constraint by employing the variance analysis and the Lyapunov stability ***,the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way,***,a simulation result is employed to verify the usefulness of the proposed design framework.

关键词： Consensus control dynamic event-triggered protocol(DETP) fault compensation(FC) fault estimation multi-agent systems(MASs)

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Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 4071-4083页

作者： Bui, Trong-An Lee, Pei-Jun Liang, Chun-Sheng Hsu, Pei-Hsiang Shiu, Shiuan-Hal Tsai, Chen-Kai National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Liscotech System Company Ltd. Department of Research and Development Taipei100213 Taiwan

Edge computing enables rapid data processing and decision-making on satellite payloads. Deploying deep learning-based techniques for low-light image enhancement improves early detection and tracking accuracy on satellite platforms, but it faces challenges due to limited computational resources. This article proposes an edge-computing-enabled inference model specifically designed onboard satellites. The proposed model follows an encoder-decoder architecture to generate the illumination map with low multiplication matrix complexity, 25.52 GMac of $1920 \times 1200$ image size. To reduce nanosatellite hardware consumption with a single-precision floating-point format, the edge-computing-enabled inference model proposes a quantized convolution that computes signed values. The proposed inference model is deployed on Arm Cortex-M3 microcontrollers onboard satellite payload (86.74 times faster than normal convolution model) but also has a similar quality with the low-light enhanced in full-precision computing of lightweight training model by using the peak signal-to-noise ratio (average of 28.94) and structural similarity index (average of 0.85) metrics. © 2008-2012 IEEE.

关键词： Image enhancement

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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