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13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023

作者： Phero, Timothy L. Novich, Kaelee A. Fujimoto, Kiyo T. Khanolkar, Amey R. Johnson, Benjamin C. McMurtrey, Michael D. Estrada, David Jaques, Brian J. Micron School of Materials Science and Engineering Boise State University BoiseID United States Department of Electrical and Computer Engineering Boise State University BoiseID United States Center for Advanced Energy Studies Idaho FallsID United States Idaho National Laboratory Idaho FallsID United States

ISBN: (纸本)9780894487910

Real-time monitoring of materials in harsh environments is a crucial technique towards reducing innovation time in nuclear systems. The successful measurement of real time, in-situ strain measurements during nuclear reactor operation requires innovative sensing solutions, including novel sensing strategies as well as advanced sensor design, manufacturing, and materials selection. In this paper, we will discuss two primary strategies for in-situ strain measurement strategies: additively manufactured (AM) capacitive strain gauges (CSGs) and digital image correlation (DIC). Current commercial strain gauges have limited applications in reactors due to the harsh operating conditions and non-trivial attachment strategies (i.e., welding, epoxy-adhesive) that can affect both the sensing performance and the underlying substrate under testing. AM CSGs are a viable solution as they have a low profile, low hysteresis, and wireless sensing integration capabilities that will enhance nuclear sensing technologies. In this work, the mechanical and thermal performance of the AM CSGs were tested up to 300 °C using ASTM standardized testing procedures to simulate the temperatures found in existing light water reactors. The AM CSGs had a similar performance across multiple samples which correlates to analytical models. This work leads towards the development of CSGs designed for higher temperatures and additional environmental factors found in Generation-IV reactors. Non-contact sensors, such as DIC, offer a less destructive way to measure deformation of materials when compared to alternative methods of in-situ strain determination, such as weldable strain gauges. However, DIC requires high contrast surfaces, which often relies on the implementation of artificial patterns. Using traditional splatter techniques to fabricate these patterns have limitations, including poor surface adhesion and reproducibility. In this work, AM fabrication techniques were implemented to avoid such limitatio

关键词： Image correlation

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Continuous-time Trajectory Estimation: A Comparative Study Between Gaussian Process and Spline-based Approaches

arXiv

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arXiv 2024年

作者： Johnson, Jacob C. Mangelson, Joshua G. Barfoot, Timothy D. Beard, Randal W. The Electrical and Computer Engineering Department Brigham Young University ProvoUT84602 United States The University of Toronto Institute for Aerospace Studies TorontoON Canada

Continuous-time trajectory estimation is an attractive alternative to discrete-time batch estimation due to the ability to incorporate high-frequency measurements from asynchronous sensors while keeping the number of optimization parameters bounded. Two types of continuous-time estimation have become prevalent in the literature: Gaussian process regression and spline-based estimation. In this paper, we present a direct comparison between these two methods. We first compare them using a simple linear system, and then compare them in a camera and IMU sensor fusion scenario on SE(3) in both simulation and hardware. Our results show that if the same measurements and motion model are used, the two methods achieve similar trajectory accuracy. In addition, if the spline order is chosen so that the degree-of-differentiability of the two trajectory representations match, then they achieve similar solve times as well. © 2024, CC BY.

关键词： Linear systems

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Evaluation of Pruning Techniques

Evaluation of Pruning Techniques

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Shvetha S Kumar Reshma R Nayak Jismi S Kannampuzha Jeeho Ryoo Sahil Rai Lizy K John Department of Electrical and Computer Engineering University of Texas at Austin School of Computing and Academic Studies British Columbia Institute of Technology

CNNs are widely used in a variety of computer vision tasks such as image processing, image classification, etc. The state-of-the-art neural networks are bigger and have greater number of parameters which translates to greater computations and memory footprint. This makes it difficult to deploy the real-time applications using these models on resource constrained edge devices. To address this issue, pruning techniques have been explored which reduce the number of computations and memory requirements of modern CNNs with negligible loss in accuracy. In this paper, we analyze the performance of three different pruning techniques - L1-norm based filter pruning, channel pruning and weight pruning and compare the performance in terms of inference time and accuracy. We also compared the performance of the pruned networks on two different GPU architectures and found that the inference time of pruned networks is more boosted in NVIDIA V100 compared to NVIDIA GTX1080 Ti due to its superior architecture features. We also perform inference speedup comparison between the different pruning techniques and analyze performance benefits between the different pruning techniques.

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Rock / Mine Classification Using Supervised Machine Learning Algorithms

Rock / Mine Classification Using Supervised Machine Learning...

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Intelligent and Innovative Technologies in Computing, electrical and Electronics (IITCEE), International Conference

作者： Sivachandra. K Kumudham. R P Sathish Kumar Rajendran. V G.R.Jothi Lakshmi Department of ECE Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Nowadays, Artificial Intelligence appears in the domain of goetechnics, underwater acoustics, tunneling, geomorphology engineering and also in several fields too. This paper focused on the prospectivefor machine learning approaches which are sub field of artificial intelligence especially in underwater acoustics domain. In this proposal, machine learning approaches such as light gradient boosting, logistic regression, and random forest classifier algorithms are used for categorizing rocks or mines from collected sonar dataset. Based on performance metrics such as precision, F-score, recall, execution time, accuracy and confusion matrix, evaluate overall performance of machine learning models. Here, the experimental results shows that among all classifier algorithms, light gradient boosting achieves greater validation accuracy as 95% also training accuracy as 100* moreover, random forest classifier achieves 100% accuracy during training phase.

关键词： Training Machine learning algorithms Sonar measurements Sonar Tunneling Rocks Boosting

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Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data

arXiv

引用

arXiv 2023年

作者： Chang, Kai Chieh Hasegawa-Johnson, Mark McElwain, Nancy L. Islam, Bashima Department of Electrical and Computer Engineering University of Illinois Urbana Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States Department of Human Development and Family Studies University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States

Infant sleep is critical to brain and behavioral development. Prior studies on infant sleep/wake classification have been largely limited to reliance on expensive and burdensome polysomnography (PSG) tests in the laboratory or wearable devices that collect single-modality data. To facilitate data collection and accuracy of detection, we aimed to advance this field of study by using a multi-modal wearable device, LittleBeats (LB), to collect audio, electrocardiogram (ECG), and inertial measurement unit (IMU) data among a cohort of 28 infants. We employed a 3-branch (audio/ECG/IMU) large scale transformer-based neural network (NN) to demonstrate the potential of such multi-modal data. We pretrained each branch independently with its respective modality, then finetuned the model by fusing the pretrained transformer layers with cross-attention. We show that multi-modal data significantly improves sleep/wake classification (accuracy = 0.880), compared with use of a single modality (accuracy = 0.732). Our approach to multi-modal mid-level fusion may be adaptable to a diverse range of architectures and tasks, expanding future directions of infant behavioral research. © 2023, CC BY.

关键词： Electrocardiograms

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Photonic Bragg gratings with cladding asymmetry for polarization independent and rotation filter

Photonic Bragg gratings with cladding asymmetry for polariza...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Pimbi, Daniel Hasan, Mehedi Mia, Md Borhan Jaidye, Nafiz Kim, Sangsik Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Nano Tech Center Texas Tech University LubbockTX79409 United States Department of Physics and Astronomy Texas Tech University LubbockTX79409 United States School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

We present a single-etched polarization-independent Bragg filter using cladding asymmetry. An air cladding strongly couples TE and TM modes, rotating its polarization status and achieving polarization - in sensitive B... 详细信息

ISBN: (纸本)9781957171258

关键词： Polarization

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Femtosecond Laser based Post-Processing Free True Random Number Generator

Femtosecond Laser based Post-Processing Free True Random Num...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Sharma, Amritash Paithankar, Prasanna Varshney, Shailendra K. Group Advanced Photonics Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology West Bengal Kharagpur721302 India Department of Applied Optics and Photonics University of Calcutta Technology Campus JD 2 Salt Lake Kolkata700098 India Department of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India Authors contributed equally

We experimentally demonstrate a high-entropy source based on the intensity fluctuation in a femtosecond laser. The setup successfully passes statistical tests without any post-processing. The generator can be potentia... 详细信息

ISBN: (纸本)9781957171296

关键词： Femtosecond lasers Laser sources Mode-locked fiber lasers Quantum communications Quantum cryptography Ultrafast lasers

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Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke

Utilizing Gradient Boosting Models to Identify Risk Factors ...

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International Conference on Advances in Information Technology (ICAIT)

作者： Tamilselvi. P C Harini Guduri Padma Rao P V Narasimha Raju Subbulakshmi R Ugranada Channabasava Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Department of ECE Malla Reddy Engineering College(A) Maisammaguda Secunderabad Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh Information Technology Sagi Rama krishnam raju Engineering college Bhimavaram Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore Department of Information Science and Engineering Don Bosco Institute of Technology VTU Belagavi Karnataka

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals.

关键词： Hypertension Sensitivity Prevention and mitigation Surveillance Predictive models Stroke (medical condition) Boosting

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Addressing the Barriers of Knowledge Transfer: Using ePortfolios to Enhance Student Reflection in Technical Courses 53

Addressing the Barriers of Knowledge Transfer: Using ePortfo...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Thomas, Rebecca Appelhans, Sarah Kozick, Rich Matlack, Christa Asare, Philip Thompson, Michael Thomas, Stewart J. Nickel, Robert Cheville, Alan Bucknell University Department of Electrical and Computer Engineering LewisburgPA17837 United States Lafayette University Department of Engineering Studies EastonPA18042 United States Center for Career Advancement Bucknell University LewisburgPA17837 United States Institute for Studies in Transdisciplinary Engineering Education and Practice University of Toronto TorontoONM5S 1A4 Canada

ISBN: (纸本)9798350336429

Education literature has long emphasized the compounding benefits of reflective practice. Although reflection has largely been used as a tool for developing writing skills, contem-porary research has explored its contributions to other disciplines including professional occupations such as nursing, teaching and engineering. Reflective assignments encourage engineering students to think critically about the impact engineers can and should have in the global community and their future role in engineering. The department of electrical and computer engineering at a small liberal arts college adopted ePortfolios in a first-year design course to encourage students to reframe their experiences and cultivate their identities as engineers. Our recent work demonstrated that students who create ePortfolios cultivate habits of reflective thinking that continue in subsequent courses within our program's design sequence. However, student ability to transfer reflective habits across domains has remained unclear and encouraging critical engagement beyond the focused scope of technical content within more traditional core engineering courses is often difficult. In this work, we analyze students' ability to transfer habits of reflective thinking across domains from courses within a design-focused course sequence to technical content-focused courses within a degree program. Extending reflection into core courses in a curriculum is important for several reasons. First, it stimulates metacognition which enables students to transfer content to future courses. Second, it builds students' ability to think critically about technical subject matter. And third, it contributes to the ongoing development of their identities as engineers. Particularly for students traditionally underrepresented in engineering, the ability to integrate prior experiences and interests into one's evolving engineering identity may lead to better retention and sense of belonging in the profession. In the first-year d

关键词： ePortfolio first-year design reflections transfer of learning

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

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