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Advancing giga-strength and high-modulus aluminum matrix composites via nitrogen-induced self-forming process

Journal of Materials research and technology 2025年 36卷 8876-8894页

作者： Kanhu C. Nayak Se-Hoon Kim Jin-Won Lee donghyun Bae Jae-Pyong Ahn Kon-Bae Lee Hyun-Joo Choi Department of Materials Science and Engineering Kookmin University Seoul 02707 Republic of Korea Advanced Materials R&D Department Korea Automotive Technology Institute (KATECH) Cheonan 31214 Republic of Korea Department of Materials Science and Engineering Yonsei University Seoul 03722 Republic of Korea Data Center Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of Korea

Giga-strength Al/SiC composites with exceptional strength (>1 GPa) and stiffness (>200 GPa), yet low density (<3.0 g/cm 3 ), are produced through a simple yet transformative process that utilizes nitrogen's critical influence on the matrix-reinforcement interface, known as Nitrogen-Induced Self-Forming Aluminum Composites (NISFAC). The Al matrix, reinforced with 20–50 vol% microsized SiC particles, exhibited significant enhancements of mechanical properties, with a 471.93 % increase in compressive strength and a 189.33 % improvement in Young's modulus compared to monolithic aluminum. These improvements are attributed to the formation of aluminum nitride (AlN) and aluminum oxynitride (Al(O)N), which originate on the Al powder surface and integrate into the matrix and Al–SiC interface during the nitrogen-induced self-sintering process. detailed microstructural analysis revealed the critical role of these phases in load transfer from the matrix to the reinforcement. Mechanistic insights and novel predictive models further validated the contributions of reinforcements to the yield strength and Young's modulus. The Al/50 %SiC composite achieved compressive strength over 1 GPa and Young's modulus over 200 GPa, highlighting its potential for advanced structural applications.

关键词： Al/SiC composite Nitrogen-induced self-sintering Interface Giga-strength Nanoindentation Strengthening mechanism

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real Time Crop Health Monitoring Through Convolution Neural Networks

Real Time Crop Health Monitoring Through Convolution Neural ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Bommagani Sridevi Nanda Padma Puram Manjusha Sharanya Tirunagari Shanmugasundaram Hariharan S. Famila Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The agriculture sector, also known as the primary sector in India, is currently facing significant challenges due to the increasing number of pests and insects. These pests can cause serious damage to crop fields, negatively affect crop health, reduce yields, and lead to substantial economic losses for farmers. Early detection of crop diseases can help minimize yield losses by 20-40%, depending on the specific crop type and its various growth stages. The paper discusses the important use of deep learning algorithms in agriculture, specifically focusing on developing an effective approach for the automatic detection of crop diseases through advanced image analysis techniques. The proposed Convolutional Neural Network (CNN) model architecture consists of multiple layers that extract meaningful features from images using convolution and pooling operations. After the feature extraction process, additional layers are used for accurately classifying the images into different disease categories. This model is trained using the Adam optimizer, which is known for its efficiency in handling large datasets and improving the overall training speed. It employs sparse categorical cross-entropy loss over 50 epochs, with a batch size of 32, to effectively optimize the learning process. during the training phase, the model's accuracy is continuously monitored to assess its performance and make any necessary adjustments to enhance its effectiveness. One of the key innovations of this approach is the ability to perform real-time crop disease detection using a camera. This advancement eliminates the need for manual picture collection from the fields, making the entire process faster and much more efficient. By enabling farmers to quickly identify diseases, this cutting-edge technology can significantly help improve crop management practices and increase overall agricultural productivity. Ultimately, the goal is to enhance food security and reduce economic losses in the agriculture sect

关键词： Productivity Training Neural networks Crops Feature extraction Agriculture real-time systems Convolutional neural networks Monitoring diseases

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Machine Learning for Predictive Maintenance in Industrial Equipment: Challenges and Application 4

Machine Learning for Predictive Maintenance in Industrial Eq...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kalaiselvi, K. Niranjana, K. Prithivirajan, V. Kumar, K. Suresh Syambabu, V. Sathiya, B. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Science Department of Saveetha School of Law Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India Panimalar Engineering College Department of Mba Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Dhanalakshmi College of Engineering Department of Ai &ds Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In the industrial sector, unplanned equipment failures can lead to significant financial losses, safety hazards, and operational inefficiencies. Traditional maintenance strategies, such as reactive maintenance (repair after failure) and preventive maintenance (scheduled maintenance), often prove inadequate in addressing these issues. This not only minimizes downtime but also optimizes maintenance resources and extends the lifespan of equipment. Machine learning models used in Predictive maintenance (PdM) are designed to analyze large volumes of data generated by industrial equipment. reinforcement learning optimizes maintenance schedules by learning from interactions with the environment, balancing the trade-off between maintenance costs and equipment reliability. Black-box models, such as deep learning, often provide high accuracy but lack transparency. Explainable AI (XAI) techniques are being developed to make these models more interpretable, enabling users to gain insights into the decision-making process and build confidence in the predictions. The deployment of ML models in industrial environments also requires seamless integration with existing systems. This involves addressing issues related to real-time data processing, scalability, and model retraining. Edge computing and cloud-based solutions are being explored to handle large-scale data processing and storage, while online learning techniques allow models to adapt to new data continuously. This paper presents several case studies from industries such as manufacturing, energy, transportation, and healthcare, illustrating the successful application of ML in PdM. In manufacturing, ML models have been used to predict failures in machinery such as turbines, compressors, and conveyor belts, leading to significant reductions in downtime and maintenance costs. The energy sector has employed ML for the maintenance of critical infrastructure, including power grids and wind turbines, enhancing operational efficienc

关键词： Predictive maintenance

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Learning Vision-and-Language Navigation from YouTube Videos

Learning Vision-and-Language Navigation from YouTube Videos

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International Conference on Computer Vision (ICCV)

作者： Kunyang Lin Peihao Chen diwei Huang Thomas H. Li Mingkui Tan Chuang Gan South China University of Technology Information Technology R&D Innovation Center of Peking University Peking University Shenzhen Graduate School Key Laboratory of Big Data and Intelligent Robot Ministry of Education UMass Amherst MIT-IBM Watson AI Lab

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3d environments using natural language instructions. Existing VLN methods suffer from training on small-scale environments or unreasonable path-instruction datasets, limiting the generalization to unseen environments. There are massive house tour videos on YouTube, providing abundant real navigation experiences and layout information. However, these videos have not been explored for VLN before. In this paper, we propose to learn an agent from these videos by creating a large-scale dataset which comprises reasonable path-instruction pairs from house tour videos and pre-training the agent on it. To achieve this, we have to tackle the challenges of automatically constructing path-instruction pairs and exploiting real layout knowledge from raw and unlabeled videos. To address these, we first leverage an entropy-based method to construct the nodes of a path trajectory. Then, we propose an action-aware generator for generating instructions from unlabeled trajectories. Last, we devise a trajectory judgment pretext task to encourage the agent to mine the layout knowledge. Experimental results show that our method achieves state-of-the-art performance on two popular benchmarks (r2r and rEVErIE). Code is available at https://***/JeremyLinky/YouTube-VLN

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Smart Healthcare in IoT using Convolutional Based Cyber Physical System 2

Smart Healthcare in IoT using Convolutional Based Cyber Phys...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Suganyadevi, S. Priya, S. Shanmuga Menaha, r. Sathiya, S. Jha, Pooja Sakena Benazer, S. Kpr Institute of Engineering and Technology Department of ECE Coimbatore India Institute of Science and Technology Vel Tech Rangarajan Dr Sagunthala R&d Department of Cse Chennai India Sri Eshwar College of Engineering Department of It Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sandip University Department of Electrical Engineering Bihar India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The intelligent Internet of Things (IoT) through infinite networking possibilities for medical data investigation is elevating the interaction between technology and healthcare society. recent years have seen fruitful transformations in deep networks and the widespread use of health wearables. IoT enabled by deep Neural Networks brought about novel societal advances in medicine and brought new possibilities to the study of healthcare data. despite the improvements, there are still certain problems that need to be addressed in terms of service quality. In this research, we present Grey Filter Bayesian Convolution Neural Network (GFB-CNN), a deep Neural Network-driven IoT smart healthcare approach that makes use of real-time data. Here, we suggested a comprehensive AI-driven Internet of Things (IoT) eHealth architecture using a GFB-CNN to improve accuracy and efficiency across critical quality of service criteria. In order to evaluate the method's viability, a large-scale Mobile HEALTH (MHEALTH) dataset is analysed. From design ideas to matching accuracy, overheads, and time related to state-of-the-art approaches, this instructive example examines and addresses all relevant elements of the suggested method. The GFB-CNN approach has assessed beside state-of-the-art methods in a multiplicity of simulated settings. We demonstrate that our approach successfully analyses health information investigation for heart signs by efficiently differentiating among good and sick heart signals with low time and cost required for sensing and data collecting. © 2022 IEEE.

关键词： Internet of things

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Predicting Crops Based on Soil and Environmental Characteristics Using ArIMA and LSTM Model 4

Predicting Crops Based on Soil and Environmental Characteris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Naresh Kumar, U. Madhana Mohan, K. Pragadeeswaran, S. Scaria, Jobin M. Baseera, A. Seenivasan, d. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and rFE select features. The model was trained using a hybrid ArIMA-LSTM technique, which blends deep learning and time-series forecasting. The ArIMA-LSTM model outperforms solo ArIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ArIMA-LSTM can boost agricultural productivity while minimizing environmental impact. © 2024 IEEE.

关键词： crop productivity long short-term memory (lstm) soil management

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FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation

arXiv

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arXiv 2023年

作者： Sun, Xinyu Chen, Peihao Fan, Jugang Li, Thomas H. Chen, Jian Tan, Mingkui South China University of Technology China Pazhou Laboratory China Information Technology R&D Innovation Center of Peking University China Peking University Shenzhen Graduate School China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Hong Kong

Learning to navigate to an image-specified goal is an important but challenging task for autonomous systems. The agent is required to reason the goal location from where a picture is shot. Existing methods try to solve this problem by learning a navigation policy, which captures semantic features of the goal image and observation image independently and lastly fuses them for predicting a sequence of navigation actions. However, these methods suffer from two major limitations. 1) They may miss detailed information in the goal image, and thus fail to reason the goal location. 2) More critically, it is hard to focus on the goal-relevant regions in the observation image, because they attempt to understand observation without goal conditioning. In this paper, we aim to overcome these limitations by designing a Fine-grained Goal Prompting (FGPrompt) method for image-goal navigation. In particular, we leverage fine-grained and high-resolution feature maps in the goal image as prompts to perform conditioned embedding, which preserves detailed information in the goal image and guides the observation encoder to pay attention to goal-relevant regions. Compared with existing methods on the image-goal navigation benchmark, our method brings significant performance improvement on 3 benchmark datasets (i.e., Gibson, MP3d, and HM3d). Especially on Gibson, we surpass the state-of-the-art success rate by 8% with only 1/50 model size. Copyright © 2023, The Authors. All rights reserved.

关键词： Navigation

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Enhancement of IoT based Flood detection and Prevention using Arduino UNO with WiFi Module 9

Enhancement of IoT based Flood Detection and Prevention usin...

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

作者： Charaan, r. M. dilip Shobana, J. Krishnamoorthy, P. Princy, B. Anni Abinaya, r.J. Murugesan, K. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Tamil Nadu India Loyola Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT's WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does. © 2023 IEEE.

关键词： Floods

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Masked Motion Encoding for Self-Supervised Video representation Learning

Masked Motion Encoding for Self-Supervised Video Representat...

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Conference on Computer Vision and Pattern recognition (CVPr)

作者： Xinyu Sun Peihao Chen Liangwei Chen Changhao Li Thomas H. Li Mingkui Tan Chuang Gan South China University of Technology Information Technology R&D Innovation Center of Peking University Peking University Shenzhen Graduate School Key Laboratory of Big Data and Intelligent Robot Ministry of Education Pazhou Laboratory UMass Amherst MIT-IBM Watson AI Lab

How to learn discriminative video representation from unlabeled videos is challenging but crucial for video analysis. The latest attempts seek to learn a representation model by predicting the appearance contents in the masked regions. However, simply masking and recovering appearance contents may not be sufficient to model temporal clues as the appearance contents can be easily reconstructed from a single frame. To overcome this limitation, we present Masked Motion Encoding (MME), a new pretraining paradigm that reconstructs both appearance and motion information to explore temporal clues. In MME, we focus on addressing two critical challenges to improve the representation performance: 1) how to well represent the possible long-term motion across multiple frames; and 2) how to obtain fine-grained temporal clues from sparsely sampled videos. Motivated by the fact that human is able to recognize an action by tracking objects' position changes and shape changes, we propose to reconstruct a motion trajectory that represents these two kinds of change in the masked regions. Besides, given the sparse video input, we enforce the model to reconstruct dense motion trajectories in both spatial and temporal dimensions. Pre-trained with our MME paradigm, the model is able to anticipate long-term and fine-grained motion details. Code is available at https://***/XinyuSun/MME.

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Application of Magnetic resin based on Grey Clustering Algorithm in Advanced Treatment of Industrial Wastewater 2

Application of Magnetic Resin based on Grey Clustering Algor...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Mustare, Narendra B. Suguna, r. Saritha, P.S. Abraham, Arockia raj Jain, Sanjiv Kumar raja, Ch. Department of Electronics and Instrumentation Engineering CVR College Of Engineering Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Dhanalakshmi Srinivasan College Of Engineering Navakkarai Post Coimbatore India Department of CSE- Data Science Madanapalle Institute of Technology and Science Madanapalle Chittoor India Department of Electrical Engineering Medi-Caps University Indore AB M.P. Indore452001 India Department of ECE Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350305418

With the rapid development of industry, the types and quantities of wastewater have increased rapidly, and the pollution to water bodies has become increasingly widespread and serious. As far as water system is concerned, the information of water quality system is both known and unknown because of incomplete data information and great uncertainty of systematic observation information, such as data error, data quality and quantity influence, etc. Therefore, judging from the clarity of system information, water quality pollution assessment is actually a problem with definite extension but uncertain connotation. In this paper, a water quality detection method based on grey clustering algorithm is proposed to evaluate the treatment effect of industrial wastewater by magnetic resin adsorption, and the effectiveness of the proposed method is verified by comparative experiments. The results show that grey clustering algorithm can deal with the measured data of turbidity, dissolved oxygen and conductivity of water quality parameters without changing the basic trend of the measured data, and at the same time, it can remove some noise signals to get a smoother data curve. Through the analysis of practical data and the comparison of traditional water quality evaluation models, the prediction algorithm predicts the change trend of water turbidity more effectively, and improves the accuracy of water turbidity control in the process of water treatment. © 2023 IEEE.

关键词： Grey clustering algorithm Industrial wastewater Magnetic resin Testing water quality

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