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75th World Foundry Congress, WFC 2024

作者： Rakoczy, Lukasz Grudzień-Rakoczy, Malgorzata Cygan, Rafal Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow av. Mickiewicza 30 Kraków30-059 Poland Lukasiewicz Research Network Kraków Institute of Technology Zakopiańska 73 Kraków30-418 Poland Rzeszów University of Technology Research and Development Laboratory for Aerospace Materials Żwirki i Wigury 4 Rzeszów35-959 Poland

This work focused on the microstructure and mechanical properties of nickel-based superalloy René N5 prepared by investment casting. The microstructure of casting was systematically investigated by X-ray diffraction, light microscopy, energy-dispersive X-ray spectroscopy, and scanning electron microscopy. © 2024 75th World Foundry Congress, WFC 2024. All rights reserved.

关键词： Energy dispersive spectroscopy

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Effective Training Strategy for NN Models of Working Memory Classification with Limited Samples

Effective Training Strategy for NN Models of Working Memory ...

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IEEE International Symposium on Biomedical Imaging

作者： Pranav Suresh Bhaskar Ray Bishal Thapaliya Britny Farahdel Behnam Kazemivash Jiayu Chen Kuaikuai Duan Vince D. Calhoun Jingyu Liu Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Department of Computer Science Georgia State University Atlanta USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

In biomedical imaging, we often encounter tasks requiring machine learning models to analyze large amounts of variables. However, in practice, limited and imbalanced samples hinder the prediction power of the machine learning methods, particularly for data-hungry techniques like Neural Networks (NN). This study proposes a strategy for training NN models with limited samples. When the data condition result in a model with suboptimal solutions even under a shallow network configuration and best learning practices, we propose a simple yet efficient reusing and refining training strategy, termed self-transfer training. We demonstrated this strategy would improve the model performance. We modified the SFCN (Simple Fully Convolutional Network) model to classify the working memory capacity using structural MRI data. In this study, we analyzed 5469 subjects, of which 3501 have low working memory capacity, and 1968 have high-memory capacity. First, we fully trained the model using distinct sets of hyperparameters and obtained a mean balanced accuracy of 67.80%. Further training the model using the Self-Transfer Training strategy improved the model performance to 72.53%. We conclude that i) the brain imaging features presented in the shallow layers of the models are more generic and can be trained initially and reused, while brain imaging features in later layers are more task-specific and can be refined via further training, and ii) the self-transfer training will aid in improving the model’s performance with limited samples to learn.

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Optimizing Depression Detection and Analysis in Women with Machine Learning Approaches

Optimizing Depression Detection and Analysis in Women with M...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Lalit Khanna Shreenidhi H S Chitrakant O. Banchhor D. Bhanu Mithhil Arora Umamaheswari. B Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Depression affects people globally, especially women. This abstract discusses the several causes of depression in women. Women acquire depression due to sociocultural influences, biological variables, psychological stresses, and hormonal variations. The interconnectedness of gender, race, ethnicity, and financial background affects depression among women. Understanding these distinctions is essential to offering depression-specific support, therapy, and interventions to women. This abstract raise awareness of gender-specific mental health care challenges to help depressed women recover. Some disorders may worsen with depression: Asthma, arthritis, and cardiovascular disease. Diabetes, obesity It is important to recognise that depression is a normal aspect of life. Everyone faces unpleasant and upsetting circumstances. Across all trials and measures, the ensemble binary classifier beat all baseline methods. The suggested ensemble model was trained using the processed depression dataset. This study links depressed symptoms to quality-of-life metrics for women. Previous research and comparisons to current approaches show that the suggested strategy produces more accurate findings. Next, we assessed its performance using the mental screen inventory and discussed comparable expectations. Finally, we suggested future study directions. This information may help researchers and medical practitioners understand the origins of depression and other mental health concerns.

关键词： Obesity Accuracy Supervised learning Medical treatment Mental health Depression Prediction algorithms Real-time systems Predictive analytics Monitoring

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Development of a low-cost IoT device using ESP8266 and Atmega328 for real-time monitoring of Outdoor Air Quality with Alert 3

Development of a low-cost IoT device using ESP8266 and Atmeg...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Malik, Praveen Kumar Duggal, Angel Swastik Aluvala, Srinivas Sahithi, Rajidi Geetanjali Gehlot, Anita School of Electronics and Electrical Engineering Lovely Professional University Punjab Jalandhar India School of Computer Science & Artificial Intelligence Sr University Warangal India Bvrit College of Engineering for Women Hyderabad India Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad India Uttaranchal University Uit Division of Research and Innvovation Dehradun India

ISBN: (纸本)9798350330724

Identifying pollution sources, assessing their effects on human health and the environment, and developing plans to enhance air quality are all made possible by the practice of air quality monitoring, which is crucial to understanding and managing air pollution. The main project's objective is to develop an economical method of monitoring and alerting of outside air quality in real time. The system uses an ESP8266 Nuttyfi WiFi board as the slave device for internet access and an ATmega328 board as the master controller. Accurate air quality measurements are made possible by the device's integration of the MQ135 Oxygen air quality sensor, which can identify gases like ammonia, benzene, alcohol, and CO2. The monitoring capabilities are improved by the DHT11 humidity and temperature sensor, which also records environmental data. Effective connection between the ATmega328 and ESP8266 boards is achieved by utilizing the software serial technique, allowing for data transmission and internet connectivity. An external power source powers the instrument, assuring dependable and stable performance. The created Internet of Things device allows in-the-moment monitoring of outside air quality, giving customers useful information about the air they breathe. Additionally, an alarm system can be put in place to warn users when air quality is at risk, encouraging them to take immediate action to protect their health and wellbeing. This project serves as an example of a practical method for addressing the expanding demand for environmental monitoring systems. It is suitable for a variety of applications, including smart cities, industrial regions, and residential settings thanks to the integration of low-cost components and the use of IoT technology. © 2023 IEEE.

关键词： Costs

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IoTDSCreator: A Framework to Create Labeled Datasets for IoT Intrusion Detection Systems 25

IoTDSCreator: A Framework to Create Labeled Datasets for IoT...

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Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy

作者： Hyunwoo Lee Charalampos Katsis Alireza Lotfi Taejun Choi Soeun Kim Ashish Kundu Elisa Bertino Department of Engineering Korea Institute of Energy Technology Naju Republic of Korea Department of Computer Science Purdue University West Lafayette IN USA Cisco Research Cisco San Jose CA USA

ISBN: (纸本)9798400714764

Intrusion detection systems (IDSes) are critical building blocks for securing Internet-of-Things (IoT) devices and networks. Advances in AI techniques are contributing to enhancing the efficiency of IDSes, but their performance typically depends on high-quality training datasets. The scarcity of such datasets is a major concern for the effective use of machine learning for IDSes in IoT networks. To address such a need, we present IoTDSCreator - a tool for the automatic generation of labeled datasets able to support various devices, connectivity technologies, and attacks. IoTDSCreator provides a user with DC-API, an API by which the user can describe a target network and an attack scenario against it. Based on the description, the framework configures the network, leveraging virtualization techniques on user-provided physical machines, performs single or multi-step attacks, and finally returns labeled datasets. Thereby, IoTDSCreator dramatically reduces the manual effort for generating labeled and diverse datasets. We release the source code of IoTDSCreator and 16 generated datasets with 193 features based on 26 types of IoT devices, 2 types of communication links, and 15 types of IoT applications.

关键词： automated dataset generation

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An Improved Imitation Learning Algorithm Based on Markov Chain

An Improved Imitation Learning Algorithm Based on Markov Cha...

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IEEE International Conference on Control science and Systems engineering (CCSSE)

作者： Jiyang Yu Dan Huang Xiaobao He Yong Han Jianyu Wang Kaiwen Sun Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China China Research & Development Academy of Machinery Equipment Chongqing University of Technology Beijing Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

An improved imitation learning algorithm based on Markov chain is proposed, aiming to enhance the performance of autonomous driving in complex environments. Imitation learning learns task execution by observing expert behavior, while Markov chains are used to model state transition processes. The existing imitation learning algorithms have limited ability to handle complex tasks, especially in natural environments. The algorithm proposed in this article improves learning efficiency and autonomous control performance by introducing strategy iteration and reward function design for sudden obstacles. The core idea is to introduce Markov decision processes into traditional imitation learning frameworks to improve learning efficiency and performance. By acquiring a refined strategy, the objective is to mimic the agent's actions within the environment to closely resemble those of the expert. To achieve this, first train an initial strategy using expert examples. Then, use this strategy to generate new examples and update the strategy using these examples. This process iterates continuously until the strategy converges. The experimental results show that on the lunar environment autonomous driving datasets, the accuracy of our algorithm reaches 95% with a mean square error of 0.02, while the accuracy of the comparative algorithm is the highest at 90% with a mean square error of 0.05. This indicates that the improved algorithm can more accurately learn and imitate the driving behavior of experts. In addition, the performance of the algorithm is stable at different iteration times, indicating good convergence.

关键词： Accuracy Imitation learning Markov decision processes Supervised learning Mean square error methods Probability Systems engineering and theory Computational efficiency Autonomous vehicles Convergence

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Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs

arXiv

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arXiv 2024年

作者： Zhang, Tianyu Hou, Chengbin Jiang, Rui Zhang, Xuegong Zhou, Chenghu Tang, Ke Lv, Hairong The Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The School of Computer Science and Engineering Fuyao Institute of Technology Fuzhou350109 China The State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Fuzhou Institute of Data Technology Fuzhou350200 China

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://***/zhangtia16/LICAP Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Magnetic GAA (MAG-GAA) for Vertical and Horizontal Magnetic Field Detection

Magnetic GAA (MAG-GAA) for Vertical and Horizontal Magnetic ...

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International Electrical engineering Congress (iEECON)

作者： Khine Thandar Nyunt Swe Amporn Poyai Toempong Phetchakul Department of Electronics Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Industrial IOT and Automation Research Group (IIARG) National Electronics and Computer Technology Center National Science and Technology Development Agency (NSTDA) Pathumthani Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The Gate All Around (GAA) is introduced as a magnetic sensor to sense the vertical and horizontal magnetic field applied to the device. Magnetic GAA (MAG-GAA) consisting of one source contact, one gate contact wrapped around the channel, and four drain contacts are created and simulated for the two-dimensional magnetic field detection using Sentaurus. The proposed device structure of MAG-GAA is designed and operated based on the current mode of the Hall effect. The vertical magnetic field is sensed by activating the drain contacts D 1 and D 2 . To detect the horizontal magnetic field, the drain contacts D 3 and D 4 positioned at the top and bottom surfaces of the device are enabled. The differential drain current is obtained as the magnetic response in both detections. MAG-GAA in which the channel length and width are 10 nm and 5 nm respectively is simulated by applying the magnetic field 0.1 T, 0.2 T, 0.3 T, 0.4 T, and 0.5 T in +z, -z, +y, and -y directions. Their respective magnetic responses are illustrated, and the Hall current reaches the highest value at the magnitude of the magnetic field 0.5 T in both cases of magnetic field detection. The sensitivity of MAG-GAA for vertical magnetic field detection is better than its sensitivity for the horizontal magnetic field.

关键词： Performance evaluation Electrical engineering Sensitivity Magnetic field measurement Magnetic sensors Gallium arsenide Magnetic devices

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Improving Solar Power Generation with InceptionV3 Dust Detection on the Solar Panel Energy Systems

Improving Solar Power Generation with InceptionV3 Dust Detec...

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

作者： Milind Shah Mittal Joshi Pratik Patel Niyati Mevada Rashmika Baria Mayur Chauhan Department of Computer Engineering Faculty of Technology & Engineering (FTE) Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of Science & Technology (CHARUSAT) Changa India Department of Computer Science & Engineering Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India Gujarat Power Engineering & Research Institute (GPERI) A Constitutient College of Gujarat Technological University Mehsana Gujarat India

As the demand for renewable energy increases, solar (PV) innovation has become a matter of concern. Diverse research proposals have been developed to derive the most significant benefit from the sun's rays, but dust gathering on solar panels and air pollution are two significant challenges. The installation of 40GW of residential solar panels and solar capacity connected to the grid is a significant accomplishment for the Indian government. technology development to manage solar panel-based energy generation has been crucial for increasing reliability and decreasing costs. As a renewable energy source, solar panels produce zero pollution while generating electricity. When dust accumulates on the surface of the solar panel, the intensity of the light that falls on it is decreased. In turn, this reduces the amount of electricity the solar panel generates. Since solar panel cleaning is essential, continual surveillance and assessment are required to optimize these processes. This highlights the significance of using machine learning or deep learning to detect dirt and clean solar panels to enhance performance. In this research, we implemented dust detection of solar panels using the InceptionV3 model and obtained a remarkable 92.34 percent accuracy. The dataset was obtained from Kaggle and consisted of 1,200 images evenly divided between clean and dirty panels. Each image is 100 pixels by 100 pixels in size. The images have been assigned to the Clean and Dusty categories.

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An Ensembled Real-Time Hand-Gesture Recognition using CNN

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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International Conference on Computing and Networking technology (ICCNT)

作者： M N Kavitha S S Saranya M Prasad S Kaviyarasu N Ragunath P Rahul Department of Computer Science and Design Kongu Engineering College Erode TamilNadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore TamilNadu India Controller of Examinations K S Rangasamy College of Arts and Science (Autonomous) Namakkal TamilNadu India Bachelor of Science – Information Systems Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems.

关键词： Human computer interaction Deep learning computer vision Sign language Neural networks computer architecture Real-time systems Data models Ensemble learning Intelligent systems

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