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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

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Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

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Light(science & Applications) 2022年第11期11卷 2817-2831页

作者： Yueming Li Ying Jiang Lu Lan Xiaowei Ge Ran Cheng Yuewei Zhan Guo Chen Linli Shi Runyu Wang Nan Zheng Chen Yang Ji-Xin Cheng Department of Mechanical Engineering Boston UniversityBostonMA 02215USA Graduate Program for Neuroscience Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Chemistry Boston UniversitBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Division of Materials Science and Engineering Boston UniversityBostonMA 02215USA Department of Biological Engineering Massachusetts Institute of TechnologyCambridgeMA 02142USA

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological *** non-invasive neuromodulation methods offer limited precision at the milimeter ***,we report opticallygenerated focused ultrasound(OFUS)for non-invasive brain stimulation with ultrahigh *** is generated by a soft optoacoustic pad(SOAP)fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane *** generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83μm,which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound(tFUS).Here,we show effective OFUS neurostimulation in vitro with a single ultrasound *** demonstrate submillimeter transcranial stimulation of the mouse motor cortex in *** acoustic energy of 0.6 mJ/cm?,four orders of magnitude less than that of tFUS,is suffcient for successful OFUS *** offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision noninvasively.

关键词： SOAP stimulation precision

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Unsupervised Learning for Detection of Defects in Pulsed Infrared Thermography of Metals

Unsupervised Learning for Detection of Defects in Pulsed Inf...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Zhang, Xin Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States Nuclear Science and Engineering Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781665480093

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this paper, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. © 2022 IEEE.

关键词： Metals

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Frequency Shift Baseline Removal for Improved Flow Measurement using Microwave Cavity Resonator

Frequency Shift Baseline Removal for Improved Flow Measureme...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Fang, Tianyang Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Nuclear Science and Engineering Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781665480093

The flow velocity of the coolant in the high-temperature fluid advanced reactor is an important variable for reactor performance monitoring. However, due to the harsh environment inside the nuclear reactors, there are limited options for measuring flow velocities. A microwave resonant cavity-based flow sensor is a novel sensing approach that measures flow velocity through deflection of the structure due to dynamic pressure. Hollow metallic resonator offers resilience to high temperature and radiation. A key technical challenge of the microwave resonant cavity-based flow sensor is the measurement of the resonant frequency shift due to membrane displacement. This research proposes a baseline removal technique that overcomes the large baseline in the forward gain of the S21 parameter frequency sweep introduced by nonideal components in the microwave system. The signal processing technique allows locating the microwave resonances, thus enabling the enhanced measurement of flow velocities. Several smoothing filters that can remove the baseline, while preserving the resonant frequency are implemented and evaluated. This approach is then realized in the LabVIEW for an actual experiment. The experimental results show the measured frequency shifts match with theory, which indicates the proposed method provides a reliable way to measure the frequency shift for flow measurement. © 2022 IEEE.

关键词： Flow velocity

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Fast exact leverage score sampling from khatri-rao products with applications to tensor decomposition 23

Fast exact leverage score sampling from khatri-rao products ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Vivek Bharadwaj Osman Asif Malik Riley Murray Laura Grigori Aydin Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley and Computational Research Division Lawrence Berkeley National Lab Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute and Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Institute of Mathematics EPFL & Lab for Simulation and Modelling Paul Scherrer Institute Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Electrical Engineering and Computer Science Department UC Berkeley

We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao product and quadratic in its column count, with persistent space overhead at most the size of the input matrices. As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each. When used to sketch the linear least squares problems arising in CANDECOMP / PARAFAC tensor decomposition, our method achieves lower asymptotic complexity per solve than recent state-of-the-art methods. Experiments on billion-scale sparse tensors validate our claims, with our algorithm achieving higher accuracy than competing methods as the decomposition rank grows.

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

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Recent Advances in Quantum Computing for Drug Discovery and Development

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IEEE Nanotechnology Magazine 2023年第2期17卷 26-30页

作者： Wang, Pei-Hua Chen, Jen-Hao Yang, Yu-Yuan Lee, Chien Tseng, Yufeng Jane Chung Yuan Christian University Quantum Information Center Taoyuan City320314 Taiwan National Taiwan University College of Electrical Engineering and Computer Science Graduate Institute of Biomedical Electronics and Bioinformatics Taipei10617 Taiwan Chunghwa Telecom Co. Ltd. Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei10617 Taiwan National Taiwan University Center for Quantum Science and Engineering Taipei10617 Taiwan

Drug discovery and development is a time-consuming and cost-intensive process. computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.

关键词： Proteins

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SENTINEL: Scalable Edge Network for Telepresence and Integrated AI 1

SENTINEL: Scalable Edge Network for Telepresence and Integra...

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1st IEEE Conference on Telepresence, Telepresence 2024

作者： El-Hadedy, Mohamed Ung, James Kim, David Hwu, Wen-Mei He, Yutao Cheng, Benny California State Polytechnic University Department of Electrical and Computer Engineering Pomona United States University of Illinois Coordinated Science Laboratory Urbana-Champaign United States University of California Los Angeles United States Naval Surface Warfare Center Corona Division United States

ISBN: (纸本)9798331528249

This paper presents a research platform centered on an RC car with a hybrid system utilizing TurningPi and DeskPi6C, with multiple Raspberry Pi Compute Module 4 nodes for edge computing. The platform is designed to facilitate telepresence applications by integrating advanced computer vision (YOLO) and natural language processing (NLP) models (BERT), managed by a backend server. The system's NLP module achieves command processing latencies ranging from 1000 to 3000 μs in ideal scenarios, enabling near-instantaneous responses in telepresence scenarios. The YOLOv5s model, deployed on the edge, supports real-time visual feedback with an impressive inference speed of approximately 10 FPS. The system's scalability demonstrated through Kubernetes, ensures that YOLO inference speed significantly improves as more pods are added, up to the platform's 10-node capacity, thereby highlighting its potential for handling the increased workload. The system's efficiency and reliability are underscored by its power consumption and resource utilization metrics, which are critical for sustained telepresence operations. The central server maintains stable power usage at around 57.8 W, while the DeskPi Super 6C and Turing Pi nodes peak at 36.0 W and 20.7 W, respectively, under load. This ensures robust performance in remote environments, reassuring the audience of its suitability for sustained telepresence operations. This platform showcases the potential of integrating Kubernetes with edge computing to enhance telepresence applications, offering real-time AI processing and scalable operations. The research provides valuable insights into developing versatile, efficient systems for telepresence, enabling more prosperous and responsive remote interactions. © 2024 IEEE.

关键词： Natural language processing systems

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Fast multiplication of random dense matrices with sparse matrices

Fast multiplication of random dense matrices with sparse mat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tianyu Liang Riley Murray Aydın Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute Sandia National Laboratories

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, thereby increasing the algorithm’s parallel scalability in shared memory architectures. On the Intel Frontera architecture, our algorithm can achieve 2x speedups over libraries such as Eigen and Intel MKL on some examples. In addition, with 32 threads, we can obtain a parallel efficiency of up to 45%.We also present a theoretical analysis for the memory movement lower bound of our algorithm, showing that under mild assumptions, it’s possible to beat the data movement lower bound of general matrix-matrix multiply (GEMM) by a factor of M, where M is the cache size. Finally, we incorporate our sketching method into a randomized algorithm for overdetermined least squares with sparse data matrices. Our results are competitive with SuiteSparse for highly overdetermined problems; in some cases, we obtain a speedup of 10x over SuiteSparse.

关键词： Distributed processing Scalability Instruction sets Memory management Memory architecture Libraries Sparse matrices

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Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

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npj Flexible Electronics 2024年第1期8卷 192-203页

作者： Hee Kyu Lee Sang Uk Park Sunga Kong Heyin Ryu Hyun Bin Kim SangHoon Lee Danbee Kang Sun Hye Shin Ki Jun Yu Juhee Cho Joohoon Kang Il Yong Chun Hye Yun Park Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Department of Clinical Research Design and Evaluation SAIHSTSungkyunkwan UniversitySeoulRepublic of Korea Patient-Centered Outcomes Research Institute Samsung Medical CenterSeoulRepublic of Korea Center for Clinical Epidemiology Samsung Medical CenterSeoulRepublic of Korea Division of Pulmonary and Critical Care Medicine Department of MedicineSamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea School of Electrical and Electronic Engineering Yonsei UniversitySeoulRepublic of Korea School of Advanced Materials Science and Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Center for Neuroscience Imaging Research Institute for Basic ScienceSuwonRepublic of Korea

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning(ML)algorithm *** findings from these previous studies have established a strong foundation for the analysis of highresolution,intricate signals,typically through frequency domain *** this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 *** interface provides real-time data visualization and communication with the ML server,creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

关键词： system diagnosis mounted

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