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Microstructure Changes and Shrinkage Characteristics of Cement Paste dried Under different relative Humidity: A Comparison with Cement Paste Containing Water-dispersible Polyurethane

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SSrN 2024年

作者： Asamoto, Shingo Takahashi, Keisuke Sakamoto, Naoki Sampei, Tokio Matsui, Kunio Saitama University Japan Department of Civil and Environmental Engineering Saitama Japan Innovation Design Institute Kagawa University Japan Research and Development Center Mitsubishi UBE Cement Corporation Japan Platform Laboratory for Science and Technology Asahi Kasei Corporation Japan R&D Laboratories Asahi Kasei Homes Corporation Japan

This study investigated the microstructure and shrinkage characteristics of cement pastes (HPC) dried under varying relative humidity (rH) levels and compared them with those of cement paste containing water-dispersible polyurethane (PUC). Our analysis employed mercury intrusion porosimetry (MIP), water vapour sorption isotherms (WVSI), and small-angle X-ray scattering (SAXS) to explore the pore structures, surface characteristics, and microstructural changes of the C-S-H units. The MIP results showed that the PUC paste consistently had a larger total pore volume than the HPC paste regardless of the drying rH. HPC paste exhibited a significant shift in capillary pore size distribution at drying rH less than 60%, which was not pronounced in PUC paste. The WVSI results indicated higher water vapour adsorption in the HPC paste, with notable changes in the adsorption behaviour under different drying conditions. The PUC paste demonstrated reduced sensitivity of the WVSI to the drying rH, maintaining more stable microstructural properties. SAXS analysis revealed that the presence of polyurethane mitigated changes in the C-S-H microstructure during drying, with less variation in the disc thickness of the fractal disc-shaped particle model compared to that in the HPC paste. The weight and length change isotherms further confirm that the PUC paste experienced less shrinkage and hysteresis in both isotherms under varying rH. Overall, this study reveals that the shrinkage of cementitious materials is significantly influenced by C-S-H microstructural changes during initial drying, and mitigating these changes effectively reduces shrinkage. © 2024, The Authors. All rights reserved.

关键词： drying

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Integrating machine learning models for real-time pollution monitoring in smart cities

Integrating machine learning models for real-time pollution ...

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8th IET Smart Cities Symposium (SCS 2024)

作者： r. ravi Matheswaran Saravanan Veni Jayakumar V. Sivaraman V. Amirthalingam Prabaharan Sengodan Department of CSE J.J College of Engineering and Technology India R&D Institute of Science and Technology India India Department of Management Studies J.J College of Engineering and Technology India Department of Computer Science & Engineering Cyber Security Madanapalle Institute of Technology & Science Andhra Pradesh India Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Salem India School of Computing Galgotias University Noida India

ISBN: (数字)9781837243105

The integration of machine learning (ML) models into smart city infrastructures offers a robust solution for real-time pollution monitoring. This paper presents innovative approaches, including data collection, preprocessing, model deployment, and adaptive learning techniques. Through comprehensive experiments, our study demonstrates the efficacy of various ML models. The Gradient Boosting model achieved the highest accuracy with an r-squared value of 0.93, MAE of 2.9 µg/m³, and rMSE of 4.2 µg/m³. The dBSCAN algorithm effectively identified pollution hotspots with a silhouette score of 0.71 and a davies-Bouldin index of 0.35. The deep Q-Network (dQN) optimized pollution control strategies, achieving a cumulative reward of 1400 and a convergence rate of 90%. An ensemble model combining random Forest and Gradient Boosting achieved an r-squared value of 0.94. These results underscore the potential of ML models to enhance pollution monitoring and management in smart cities, improving urban air quality and public health.

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A Powerful Load Balancing Algorithm for Cloud Performance Evaluation

A Powerful Load Balancing Algorithm for Cloud Performance Ev...

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International Conference on Inventive research in Computing Applications (ICIrCA)

作者： J. Praveen Chandar Chidambaranathan C M Anbarasa Kumar Anbarasan A. Karthikram Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Cyber Security School of Computers Madanapalle Institute of Technology & Science Madanapalle India

The energy consumption of data centers plays a significant role in the cost estimation of maintenance, performance evaluation, and environmental pollution in cloud computing. Carbon emissions are one of the major drawback of data centers in this scenario. To manage the data center temperature as well as the necessity to reduce power consumption while satisfying customer demands. This is extremely beneficial to both the data center and the environment. The data center is a collection of servers from which consumers can benefit by using their services. In this context, cloud service providers are working hard to reduce energy usage and avoid environmental contamination. This research work describes the novel algorithm for scheduling virtual machines in servers in order to consume a minimal amount of energy. The weight approximation algorithm arranges servers and virtual machines in decreasing order depending on their configuration to allocate VMs with jobs to optimize power usage. The power consumption of the server is calculated before and after VM allocation in this algorithm. To avoid CrAC modifications, the critical threshold of each server is maintained in this instance. If it exceeds the server's critical threshold, the VM is moved to another ideal server to avoid migration. In this method of VM allocation, the ordered tasks are allocated to the server using round-robin fashion. The model is simulated using cloudsim tools, and the server's performance and energy consumption are better than the random selection algorithm.

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Machine Learning in Smart Cities: Survey of Wireless Sensor Network Applications

Machine Learning in Smart Cities: Survey of Wireless Sensor ...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Basavaraj G N J. V. rama Kumar K. S. Jayareka T.B Sivakumar raja rajeswari Balaji A. Backia Abinaya BMS Institute of Technology Bengaluru Department of Computer Science and Engineering Sagi Rama Krishnnam Raju Engineering College (A) Bhimavaram Andhra Pradesh Department of Computer Science and Engineering Sona College of Technology Salem School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of CSE (Cyber Security) Nandha Engineering College Erode Department of ECE St. Joseph's College of Engineering and Technology Thanjavur

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

AI and ML methods can assist in processing automated tasks over the Internet on Things terminals deployed across smart communities. The key application areas of the Internet of Things in smart cities are smart traffic tracking, intelligent waste management, smart building optimisation and patient healthcare monitoring. The gateways then use WSN (Wireless Sensor and Bluetooth standards to push the data from small-sized IoT nodes over long distances. Network life maximization, connectivity issues regarding bandwidth and network coverage power consumption efficient data sharing deployment methodologies newer communication methods new infrastructure Available applications Major design challenges in the WSN-enabled smart cities. In this extensive literature survey, it is proposed to Machine Learning techniques as optimization methods for WSN-IoT nodes (Network of sensors) that are used in Smart City initiatives. This is, to the best of our knowledge, the first review study which covers all machine learning methods in low-power WSN-IoT for urban smart cities. Here the algorithms used varied in context of type as shown by results suggesting supervised learning ( 61%) for smart city applications, unsupervised learning(12%),and reinforcement learning(27%). This research will make a valuable contribution to the growing number of works on AI and ML usage with an impact on how these technologies can boost efficacy, efficiency as well as performance in smart city context for IoT deployments. removal of the number references also maintains a level with version information voided of plagiarism.

关键词： Surveys Wireless communication Waste management Wireless sensor networks Smart cities Supervised learning Energy efficiency Telecommunication computing Internet of Things Unsupervised learning

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Smartcharge: reducing Power Usage in Intelligent Homes

Smartcharge: Reducing Power Usage in Intelligent Homes

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： A. Sonya Neha Chhabra Abhishek Kumar Gupta P. Lakshmi Harika B. Aishwarya r. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Smart homes oriented towards reminders and monitoring for healthcare must be reasoned, definitely centered on matured home or disability in a house. Accurately predicting the movements of people would be essential to offer such level support for residents in this situation. Our study helps to cater this requirement by proposing an improved forecasting model which is both accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can be constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. So, finally this recurrent Extreme Learning Machine model validates that for smart home technology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should be enough during the learning phase though; but it also allows machine to learn more about human behavior making them generally intelligent because now you have a pattern how humans behave. Advantages Of Sexual Health These are certainly rewarding Smart Home Systems..

关键词： Training Adaptation models Accuracy Extreme learning machines Smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

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Traffic Sign detection Problems using Convolutional Neural Techniques in Image Processing

Traffic Sign Detection Problems using Convolutional Neural T...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： A. S. F. Subhamathi C. Sathiyapriyan Anuradha. T A. Jose Anand Abdul rheem M. r. Arun Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamilnadu Department of Computer Science and Engineering Cyber Security Aarupadai Veedu Institute of Technology Chennai Department of Electrical and Electronics Engineering KCG College of Technolog Chennai Tamil Nadu India Department of ECE KCG College of Technology Karapakkam Chennai Department of Mathematics & Humanities MMEC Maharshi Markandeshwar (Deemed to be University) Mullana Ambala Haryana Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Traffic sign detection is a critical component in advancing intelligent transportation systems and ensuring road safety. Convolutional Neural Networks (CNNs) have emerged as powerful tools in image processing and computer vision, demonstrating remarkable success in various domains, including traffic sign recognition. This paper presents a comprehensive exploration of the challenges associated with traffic sign detection and the application of CNN techniques to address these issues. The paper reviews the traditional methods used for traffic sign detection and emphasizes the limitations that motivate the adoption of CNNs. The core of the paper delves into the application of convolutional neural techniques for traffic sign detection. Various CNN architectures are surveyed and highlight their strengths and weaknesses in handling specific challenges inherent to traffic sign recognition. Attention mechanisms, transfer learning, and other advancements in CNNs are explored for their potential to enhance detection accuracy and robustness. In conclusion, this paper contributes to the understanding of how convolutional neural techniques can be leveraged to tackle traffic sign detection challenges.

关键词： Adaptation models Attention mechanisms Transfer learning Transportation Computer architecture Sensor systems robustness

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Novel Gender and Age Prediction from real Time Facial Images using Linear regression Algorithm

Novel Gender and Age Prediction from Real Time Facial Images...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Nalini N d. Ferlin deva Shahila A. rosi M. Tamilselvi Jayant Giri Ala’a Al Sherideh Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of Cyber Security College of Information Technology Zarqa University zarqa jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The proposed novel algorithm utilizes linear regression in real-time facial image analysis for predicting both gender and age. By leveraging features extracted from facial data, the model aims to provide accurate and instantaneous predictions. The emergence of social media platforms has made the automatic classification of gender and age increasingly relevant. real-time frame processing is possible with OpenCV. The expected gender and age are provided as the output, and this frame is provided as the input. The capacity to autonomously ascertain age and gender from facial images because of its versatility in facial analysis applications. However, the current models are still behind the required accuracy level, which is required for these models to be used in real-world applications, because of the significant intraclass variance of face images (such as difference in lighting, position, scale, and occlusion). In this work, a deep learning framework uses an ensemble of residual and attentional convolutional networks to reliably determine the gender and age group of facial photographs. By employing an attention mechanism, this model can concentrate on the important and instructive aspects of the face, increasing its prediction accuracy to 90.15%.

关键词： Accuracy Social networking (online) Linear regression Training data Predictive models Prediction algorithms Feature extraction real-time systems data models Faces

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Investigation of Endorsement Guideline Protocol Actuality Problem Using Blowfish AES Algorithm

Investigation of Endorsement Guideline Protocol Actuality Pr...

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International Conference on Futuristic Technologies (INCOFT)

作者： r. rajmohan S. Saran raj A. Karthikram T. Ananth Kumar Syed Fiaz A S Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Computer Science Engineering – Cyber Security Madanapalle Institute of Technology and Science Andhra Pradesh India Department of Computer Science and Engineering IFET College of Engineering Tamil Nadu India

A larger an organization's dependence on, Potential distributed information systems for its everyday activities, the greater its vulnerability to security vulnerabilities, even as it gains efficiency and productivity advantages. These approaches for massive data must include a method for imposing a policy regarding the content, topic, credentials, and type of data being produced. Organizations that used FPT (Fixed Parameter Tractable) for transmitting client information experienced issues such as the following: Loss of data, transmission file restriction, unauthorized access to data, and confidentiality are all concerns. The data security area is confronted with several new challenges. Variables such as the evaluation of security issues and the decentralization of access to the information are taken into consideration. The proposed Blowfish AES algorithm is used to secure the data to overcome these issues. The Blowfish AES algorithm, which consists of encryption and decryption, makes use of a key to allow the receiver to unwrap the message. An individual can only access the data if all their qualifications have been verified and accepted by the system. The samples are the most important part of them because they are computation time destructive and have exactness problems. The authenticated people could indeed access the information to complete the task, and an acknowledgement is sent to the authenticated person.

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deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M. Balasubramani B. J. Job Karuna Sagar K. C. rajheshwari E. Saranya B. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MrI. More specifically, U-Net tiles are pre-trained using a big Brain COLOr atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited Brain COLOr atlas. We also elaborate on his method for processing Mr images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull resonance Telecommunication computing

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Secure and responsive Human-robot Interaction for dementia Patients Using LSTM and IoT

Secure and Responsive Human-Robot Interaction for Dementia P...

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Advancement in technology (ICONAT), International Conference for

作者： S. ramya M. radhika Subramani roychoudri S. Senthil G. Arunachalam M. Muthulekshmi Department of Cyber Security Paavai Engineering College Namakkal Tamil Nadu India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Usha Rama College of Engineering and Technology Vijayawada Andhra Pradesh India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

An innovative architecture that combines Long Short-Term Memory (LSTM) networks with Internet of Things (IoT) technology is presented in this paper to assist dementia patients. It solves Human-robot Interaction (HrI) difficulties in dementia care. It also ensures security and responsiveness in HrI to establish a secure and trustworthy environment for dementia patients. LSTM networks help the robot to recognize and to adapt based on the dementia patients’ behaviors. Through real-time data gathering and exchange, IoT devices offer seamless and context-aware interactions. The system protects user privacy and sensitive data with strong encryption and authentication processes. The framework uses adaptive learning methods to improve the robot’s comprehension of the user’s preferences and needs, offering a personalized and responsive assistance system. Simulations and real-world situations show that the proposed technology provides safe, responsive, and personalized human-robot interactions for dementia care. By presenting a potential solution to dementia care difficulties it advances assistive technology and improves quality of life for dementia patients.

关键词： Training Adaptation models Human-robot interaction robot sensing systems real-time systems Internet of Things security Long short term memory dementia resilience

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