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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Chen, Ye Bian, Qi Yao, Wang Gao, Dongzhi Wang, Yanlong Chen, Xu Liu, Song Gao, Yonghai CNOOC Offshore Oil Engineering Co. Ltd. Tianjin China Shandong Qingdao China CNOOC Research Institute Ltd. Beijing China Co. Ltd. Qingdao China Shanghai Engineering Technology Branch CNOOC Energy Development Co. Ltd. Shanghai China

ISBN: (纸本)9783031425141

Solid fluidization is a new method to extract natural gas hydrate (NGH) out of theburied sediments, which is characterized by collecting hydrate resources in solid state first and decomposing into fluid later. Combining both the merits of traditional mineral and petroleum developments, solid fluidization can obtain the new supply fuel without a series of security risks caused by hydrate disorder phase transitions. In this paper, the strength, weakness, opportunity and threaten of solid fluidization are discussed respectively by SWOT analysis, then the related application prospect for NGH development in South China Sea is obtained. It is indicated that solid fluidization is one of the most suitable development method for the geological characteristics like loose, weak cementation of NGH-bearing sedimentary reservoir in South China Sea. However, there are also some application limits such as weak theoretical foundation, immature technical system and urgent improvement need of mining capacity. Grasping the global energy structure transformation tide and responding to the technology challenges actively, solid fluidization is believed to become the one of the core optimization methods for commercial marine NGH development in the future, especially in South China Sea. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hydration

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Unveiling the Mysteries and Truths Hidden Within Mod-ern Transistors and Terabyte Architectures 4

Unveiling the Mysteries and Truths Hidden Within Mod-ern Tra...

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

作者： Deshpande, Vivek Kannimuthu, S. Reddy, B. Velusudha, N.T. Das, Sidhant Debnath, Baishakhi Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam College of Engineering Department of Information Technology Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Management Karnataka Bangalore India

ISBN: (纸本)9798331518981

Finding the depth underlying current transistors and terabyte structures is crucial in the digital age. Using mathematical tests, computer simulations and models, and hands-on experimentation, the research seeks to understand these technologies. Terabyte and tera Centre architectures are among the most revolutionary. Because technology improves constantly. The digital age's seemingly little elements and processes have impacted our reality in complex and unnoticed ways. The secrets and facts of these developments have led our civilization into new inventions, networking, and computing fields. The basic building blocks of our phones and the massive data centers that power the internet have changed. The recommended technique evaluates computer performance characteristics such as speed, efficiency, data storage, reliability, redundancy, cost, safety, privacy, and environmental friendliness. It is compared to six typical approaches to demonstrate its superiority in various presentation methods. © 2024 IEEE.

关键词： Economic and social effects

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Enhancing combustion performance and emission profiles using Citrullus colocynthis biodiesel blends with hydrogen

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International Journal of Hydrogen Energy 2025年 143卷 627-634页

作者： Wang, Yuan Devanesan, Sandhanasamy Aljawdah, Hossam M. M, Rithika Brindhadevi, Kathirvel College of Electrical Engineering Yellow River Conservancy Technical University Kaifeng475004 China Bioproducts Research Chair Department of Zoology College of Sciences King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences India Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam

This study was performed to explore the potential use of a combination of two alternative fuels such as hydrogen and biodiesel. A Citrullus colocynthis biodiesel blend with a proportion of 10 % or 20 % biodiesel was chosen. The hydrogen gas flow rate was controlled using a rotameter to maintain a constant supply of hydrogen at 3 L per minute (LPM) for all test fuels throughout the run. The fuels used for conducting the experimental tests were D100, B10 (Diesel 90 % + 10 % Citrullus colocynthis), B20 (Diesel 80 % + 20 % Citrullus colocynthis), D100 + H3L (Diesel 70 % + 30 % Citrullus colocynthis), B10 + H3L (Diesel 90 % + 10 % Citrullus colocynthis with 3 LPM) and B20 + H3L (Diesel 90 % + 10 % Citrullus colocynthis with 3 LPM). Test blends were allowed to run in the direct ignition diesel engine and the performance, combustion, and emission characteristics were determined and the results were compared. All tests were conducted at full load condition with varying engine speed from 1500 rpm to 3000 rpm with 500 rpm intervals. Use of the 10 % and 20 % biodiesel blends reduced the brake thermal efficiency of the engine due to lower cetane number and calorific value. Due to the higher viscosity, fuel consumption was higher for both blends. The results obtained from the fuel blends without and with hydrogen showed that the addition of hydrogen significantly improved performance and combustion efficiency. Further, the addition of hydrogen and use of Citrullus biodiesel reduced emission of the greenhouse gases CO and CO2. Presence of excess oxygen content in the biodiesel ensured superior combustion, which eventually led to lower emission of exhaust emissions. However, there is no evidence of reduction in the NOx emission for all the blends compared to diesel. © 2025 Hydrogen Energy Publications LLC

关键词： Biodiesel

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Enhancing Sensitivity and Specificity in Diabetic Retinopathy Detection Using CNN-LSTM Models 3

Enhancing Sensitivity and Specificity in Diabetic Retinopath...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mehta, Shiva Bhardwaj, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Center for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331542375

Early and accurate detection of diabetic retinopathy (DR), as the leading cause of blindness worldwide, is important to prevent blindness. We propose a hybrid Convolutional Neural Network and Long Short Term Memory (CNN LSTM) model to increase DR classification through a combination of spatial feature extractions and sequential learning. We illustrate the training and evaluation of the proposed model on the APTOS 2019 and EyePACS datasets of retinal fundus images, each annotated into five severity stages of DR. Experimental results show that the proposed CNN-LSTM model achieves 93.1% accuracy, which is better than ResNet-50 (89.2%), and DenseNet-121 (90.3%) baseline models. Overall, the model had sensitivity of 92.0% vs. 87.5% and 88.9% for ResNet-50 and DenseNet-121 respectively, which proves (that the model) was better at identifying DR cases. Furthermore, the CNN-LSTM model achieved specificity of 92.8%, which leads to a drop in the false positive rate, fairly superior to ResNet-50 (88.6%), DenseNet-121 (89.5%). Using the proposed model displayed superior discriminative power (Area Under the Curve (AUC) score 0.96) compared to ResNet-50 (0.91) and DenseNet-121 (0.93). Finally, we analyzed the performance of the model though training-validations curves, confusion matrix analysis and achieved final training accuracy of 94.5 % and validation accuracy of 93.1% with stable learning but very minimal overfitting. The results presented show the potential of the proposed CNN-LSTM framework in improving DR detection and thus, as a viable tool for use in automated screening. Towards future work, we will study methods to improve generalization with attention mechanisms as well as with larger scale datasets. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhanced ECG Signal Analysis Using Hybrid CNN-LSTM and Augmentation Techniques 3

Enhanced ECG Signal Analysis Using Hybrid CNN-LSTM and Augme...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mehta, Shiva Aneja, Aseem Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798331542375

The nature and timely nature of the detection of the arrhythmias are critical in preventing severe cardiac repercussions like stroke or sudden cardiac death. First, this research proposes a deep learning model that integrates convolutional neural network (CNN) as well as recurrent neural network with long short-term memory (LSTM) for the classification of arrhythmias from ECG data. CNN component is responsible for extracting geographical features whereas LSTM identifies temporal dependency, creating a good base for understanding consecutive ECG signals. MIT-BIH Arrhythmia Database was used for training and validation, showing near perfect performance of the model with an accuracy of 98.2%, precision of 97.8%, recall of 98.5%, and F1-score of 98.1%. Preprocessing measures such as normalization, R-peak detection for signal segmentation and augmentation ensured data accuracy and generality. The comparative study with standalone models revealed that the proposed hybrid architecture outperformed CNN (accuracy: 92. Traditional classifiers, like SVM, SVR, attained 85.3% accuracy, while the universal LSTM model achieved an impressive 94.2%, RNN 80.5% and GRU 7%. This was further supported by ROC curves where AUC of the model was high on five different classes of arrhythmia. For extensibility and compliance with clinical decision-making processes, forecaster was incorporated into the framework with SHAP which ensure that forecasts are explicable and believable to many health care professionals. © 2025 IEEE.

关键词： Long short-term memory

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Dynamic Spectrum Access in Cognitive Radio Networks: A Reinforcement Learning Approach 4

Dynamic Spectrum Access in Cognitive Radio Networks: A Reinf...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Kumar, Gaurav Kumar, Sandeep Shrivastava, Anurag Srivastava, Arun Pratap Badhoutiya, Arti Pant, Rajesh Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Research Impact and Outreach Punjab Rajpura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Lloyd Institute of Engineering & Technology Greater Noida India GLA University Department of Electrical Engineering Mathura India Uttaranchal University Uttaranchal Institute of Management Uttarakhand India

ISBN: (纸本)9798350307757

This study also evaluates the role of RL algorithms like Q-learning, DQN and Policy Gradient Methods including DDPG in relation to implementation within DRSA for CRNs. By using metrics such as average reward, convergence time and spectrum efficiency a realistic evaluation has been done in the simulated environment. The findings show that DDPG outperformed other algorithms in terms of average rewards, fast convergence and high spectrum utilization efficiency proving its adaptability under dynamic settings. It is in line with the wider trend of using sophisticated approaches that emphasize high-level learning, collaborative optimization models and efficient distributed methods for the studies related to spectrum. This study gives helpful information on RL algorithms' pros and cons in the field of DSM, highlighting their ability to redefine wireless communication frameworks. This provides a basis for creating intelligent and self-optimization communication networks, that are of immense importance in addressing spectrum scarcity, creation innovative wireless technologies. © 2024 IEEE.

关键词： Reinforcement learning

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Lifted Bilinear Model-based Linear Model Predictive Control with Scalability

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IFAC-PapersOnLine 2023年第2期56卷 9405-9410页

作者： Masaki Kanai Masaki Yamakita Controls and Robotics Innovation Center Research & Development Group Ltd. Hitachi Ibaraki Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

We propose a novel linear model predictive control (MPC) using a lifted bilinear model based on Koopman theory, which is computationally scalable against the dimension of the target system and the prediction horizon length. In MPC, the accuracy of the prediction model determines control performance, but it is a challenge to reduce the computational cost especially when considering nonlinearity of the model. To address this, a method has been proposed which represents the nonlinear input affine system as a lifted bilinear model and utilizes linear approximation and prediction error correction regarding the lifted state to achieve a low-computational-cost linear MPC with equivalent performance to nonlinear MPC. However, although the previous studies have shown its effectiveness for relatively low-order systems, it has not been applied to practical systems with higher dimensions. In this study, we extend the conventional method and propose a scalable linear MPC using a lifted bilinear model and apply it to higher-order nonlinear systems. In this paper, a quadrotor system operating in three-dimensional space is considered and its analytical lifted bilinear model is derived. In the formulation of linear MPC using the lifted bilinear model, an error correction method is newly introduced to feedback error for adjustment of the numerical relationships among the elements in the lifted state. The effectiveness of the proposed method is demonstrated through numerical simulations.

关键词： Model predictive control Koopman theory Lifting linearization Bilinear system

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An Improved Canny Algorithm for Edge Detection in the Thickness Measurement of Reconstituted Tobacco by Modulated Line Laser 4

An Improved Canny Algorithm for Edge Detection in the Thickn...

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4th International Conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Tong, Yuxing Wang, Ziwei Liu, Ben Liu, Zhichang Gao, Song Shu, Hao Liu, Xu Kong, Dejin China Tobacco Hubei Industrial Co. Ltd Wuhan China Hubei Xinye Reconstituted Tobacco Development Co. Ltd Wuhan China Applied Technology Research of Reconstituted Tobacco Hubei Province Key Laboratory Wuhan China School of Electronics and Electrical Engineering Wuhan Textile University Wuhan China

ISBN: (纸本)9798331531409

In the thickness measurement of reconstituted tobacco by modulated line laser, it is necessary to extract the edge of the modulated line laser to perform the straight line fitting. However, in the edge detection, the conventional Canny edge detection algorithm suffers from the problem of edge blur and manual double threshold setting caused by filtering. In this paper, a dynamic adaptive weighted median filtering scheme is proposed. Instead of the scheme of single Gaussian filtering in the conventional Canny algorithm, the proposed scheme uses an adaptive dual threshold selection algorithm based on the maximum entropy of the image to cope with the problem of dual threshold setting. With the above two improvement schemes, the filtering parameters can be dynamically adjusted to reduce the degree of smoothing to the edges while reducing the noise. In addition, the edge detection quality of Canny algorithm can be effectively improved by the adaptive threshold selection method. The results show that the improved Canny algorithm can effectively filter out the noise and has the advantages of high adaptability and edge clarity. © 2024 IEEE.

关键词： Wiener filtering

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Towards an Automated Process for Adaptive Modelling of Orthoses and Shoe Insoles in Additive Manufacturing 12

Towards an Automated Process for Adaptive Modelling of Ortho...

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12th International Workshop on Innovative Simulation for Health Care, IWISH 2023

作者： Zwettler, Gerald A. Trixner, Martin Schartmüller, Clemens Bauernfeind, Sophie Stockinger, Thomas Praschl, Christoph Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Granitweg 1 Kirchschlag bei Linz4202 Austria Pulvermühlstrasse 3 Linz4040 Austria

ISBN: (纸本)9788885741966

Although orthopedics is becoming increasingly important as a medical domain, especially in emerging countries, the level of automation is still marginal and hardly any Industry 4.0 paradigms have been implemented. In this scientific work, solution concepts for holistic process automation in orthopedics are introduced so that prosthetic covers and orthoses for different body regions can be automated by using AI and evaluated with sensor networks. In this process, body scan models are adapted to the conditions of the anatomy or prosthesis models, so that stability as well as fitting accuracy are given in comparison with the other half of the body. Automation in the field of orthopedics leads not only to a significant reduction in costs but can also help to close the research gap regarding objectifiability of results. The first partial aspects have already been successfully implemented for leg prostheses, arm prostheses and shoe insoles with the aid of machine learning processes and physical models for elastic form fitting. As soon as the overall process has been realized, the applicability will be validated in the following year of the project by means of clinical studies and evaluated by utilizing sensor networks for pressure and temperature measurements. © 2023 The Authors.

关键词： Sensor networks

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Detection of SYN Flood Attack Based on Bays Estimator (DsFaBe) in MANET 4

Detection of SYN Flood Attack Based on Bays Estimator (DsFaB...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Rishkan, Basheer Usmani, Muhammad Hamza Sheikh, Md Amin Ullah Jazri, Husin Bin Khan, Navid Ali Sharma, Komal School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Andhra Pradesh Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

SYN flood attacks pose a significant threat to the normal traffic flow in Mobile Ad Hoc Networks (MANETs). These attacks flood the network with unnecessary traffic, leading to congestion on specific routes. In this paper, we introduce an Adaptive Detection Mechanism that employs an Artificial Intelligence Technique. This method, known as SYN Flood Attack Detection Based on Bay Estimator (DsFaBe), is designed specifically for MANETs. Each node collects real-time information about the available channels. The DsFaBe then selects the most secure and congestion-free (Best Path) channel for traffic. Persistent congestion can lead to SYN Flood attacks. However, our use of AI techniques has resulted in a higher probability of detecting SYN Flood attacks compared to other methods. We also found that our proposed algorithm is cost-effective and more robust compared to existing approaches. The results of our simulations support our theory. © 2024 IEEE.

关键词： Mobile ad hoc networks

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