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Quantitative phase imaging endoscopy with a metalens

Light(science & Applications) 2024年第12期13卷 3196-3209页

作者： Aamod Shanker Johannes E.Fröch Saswata Mukherjee Maksym Zhelyeznyakov Steven L.Brunton Eric J.Seibel Arka Majumdar Department of Electrical and Computer Engineering University of WashingtonSeattleWA 98195USA Center for Vision Science University of RochesterNew YorkNY 14623USA Department of Mechanical Engineering University of WashingtonSeattleWA 98105USA Department of Physics University of WashingtonSeattle 98195USA

Quantitative phase imaging(QPI)recovers the exact wavefront of light from intensity *** and optical density maps of translucent microscopic bodies can be extracted from these quantified phase *** demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid *** method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color *** 0.5mm aperture metalens shows robust quantitative phase imaging capability with a 28°field of view and 0.2πphase resolution(~0.1λin air)for experiments with an endoscopic fiber *** the spectral functionality is encoded directly in the imaging lens,the metalens acts both as a focusing element and a spectral *** use of a simple computational backend will enable real-time *** limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition,interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.

关键词： phase bundle exact

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Comprehensive Study of Advantages, Limitations, Applications and Similar Tool of ChatGPT

Comprehensive Study of Advantages, Limitations, Applications...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rathi, Nayan Bandavade, Prakash Chaubey, Himanshu Jaju, Santosh Thakur, Shruti G H Raisoni College of Engineering Computer Science Engineering Department Maharashtra Nagpur India G H Raisoni College of Engineering Mechanical Engineering Department Maharashtra Nagpur India

ISBN: (纸本)9798331517953

ChatGPT is nothing but a natural language processing model which takes input from humans and gives them the response which is appropriate according to him. ChatGPT gives the data which is already in the search engine and cannot create the data of its own. It is not creative, as it cannot create its own content. It is very powerful tool which helps in writing code, preparing for interview, calculating mathematical solution though it has various drawbacks such as it lacks logical reasoning, cannot calculate complex mathematical calculation like irrational number to the power of irrational number, it also makes grammatical error and it does not have information from recent scenario, you get information which occurred 2 years back. But it has industrial application like it is used as customer support in many companies, used to keep routine of employee in company by providing them proper schedule for work, it is also used to get code which makes works of employee lot more easier, So this are the key points about it. © 2024 IEEE.

关键词： Modeling languages

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Study on frictional behavior of SiC_(f)/SiC composite clad tube clamping condition under nuclear irradiation

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Friction 2024年第5期12卷 919-938页

作者： Chenglong XIAO Liangliang SHEN Tianqi ZHU Jianbo TANG Ximing XIE Xinyu FAN Jian XU Zhiying REN School of Mechanical Engineering Zhejiang University of TechnologyHangzhou310023China Engineering Laboratory of Advanced Energy Materials Ningbo Institute of Material Technology and EngineeringChinese Academy of SciencesNingbo315201China State Key Laboratory of Fine Chemicals Liaoning High-Performance Polymer Engineering Research CenterSchool of Chemical EngineeringDalian University of TechnologyDalian116024China School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou350116China School of Computer Science and Technology Shandong UniversityQingdao266237China Aviation Industry Corporation of China Ltd.Beijing100028China

Silicon carbide fiber reinforced silicon carbide matrix(SiC_(f)/SiC)composite is the key cladding material of nuclear fuel,which determines the safety and reliability of nuclear fuel storage and *** replacement of its storage and transportation scenario needs to be completed by the manipulator,but the application of SiC_(f)/SiC wear,fracture,and nuclear leakage in the snatching process of brittle-flexible-rigid contact in the irradiation environment has been seriously restricted due to unclear understanding of the damage ***,the effects of irradiation dose and clamping load on the friction characteristics of the contact interface between SiC_(f)/SiC clad tube are studied in this paper,and the effects of irradiation parameters and clamping force on the static friction coefficient of the contact interface between the clad tube and flexible nitrile are *** on the Greenwood-Williamson tribological model,a numerical model of the shape and structure of the contact micro-convex at the micro-scale of the clamping interface is constructed by introducing the multi-surface integral,and finally verified by *** research results show that there is a unique“Irradiation suppression zone”under the clamping condition of SiC_(f)/SiC cladding tube under the nuclear irradiation environment,and the growth of static friction coefficient slows down until stagnates after irradiation reaches a certain extent(600 kGy),and there will be a decline when the irradiation dose continues to increase,among which the clamping force of 15.2 N within the irradiation interval of 1,000 kGy can meet the safety of nuclear environment *** results of this paper can provide an important theoretical basis and application guidance for the safe operation of SiC_(f)/SiC cladding tubes in the storage and transportation clamping process.

关键词： SiC_(f)/SiC Clad tube nuclear irradiation conditions Greenwood-Williamson tribological model multi-surface integral

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Need for Artificial Neural Networks in today’s world 4th

Need for Artificial Neural Networks in today’s world

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4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Siddesh Kumar, N.M. Promod Kumar, B.M. Haloor, Bheem Kumar Haran, Pranadhi Manoj Gowda, K.S. Hemashree, S.S. Department of Mechanical Engineering PES College of Engineering Mandya India Department of Computer Science and Engineering PES College of Engineering Mandya India Department of Electrical and Engineering PES College of Engineering Mandya India Department of Information Science and Engineering PES PES College of Engineering Mandya India

ISBN: (纸本)9781032426853

Artificial Neural Networks (ANNs) are widely used in various fields due to their versatile applications. They are capable of handling complex functions and can adapt to new conditions. Their applications range from data interpretation, drug design, pattern recognition, text translation, credit card fraud detection, medical diagnosis and more. ANNs have become an essential tool in today’s world due to their ability to interpret large amounts of data, making them crucial in fields such as medical diagnosis and credit card fraud detection. The versatility of ANNs makes them applicable in many different industries and continues to contribute to their growing popularity. The current work focuses on exploring the wide range of applications of ANNs and their significance in our world today. © 2024 The Author(s).

关键词： Diagnosis

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An Efficient Approach for Appliances Energy Prediction using Differential Evolution Optimization and Light GBM Machine Learning Model

An Efficient Approach for Appliances Energy Prediction using...

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2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future, ICUE 2024

作者： Vakharia, Vinay Dave, Vipul Borade, Himanshu Agrawal, Hitesh Padia, Nandan Pandit Deendayal Energy University Department of Mechanical Engineering Gandhinagar India Parul Institute of Engineering and Technology Department of Mechanical Engineering Vadodara India Medi-Caps University Mechanical Engineering Department Indore India Pandit Deendayal Energy University Department of Computer Science Engineering Gandhinagar India

ISBN: (纸本)9798331517076

This study presents a new method for assessing the energy use of appliances in a low energy residence with the goal of improving energy consumption predictions. Information was collected at 10-minute intervals throughout a period of 137 days. The collected data includes a wide range of equipment, with a specific emphasis on lighting and the relationship between humidity levels and room occupancy. The dataset was divided into three subgroups depending on the lighting feature in order to assess the predictive importance of various parameters: all records, recordings without lighting, and records only including lighting. The suggested investigation employs a hybrid machine learning framework that integrates Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (Light GBM) to forecast energy consumption. In order to improve the precision of the model, a sophisticated differential evolution approach is used to optimize the model's parameters. The models were compared using several performance indicators, such as the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE). The prediction results showed low error rates and strong correlation coefficients for all of these measurements, highlighting the usefulness of our technique in improving operational efficiency and forecast accuracy in energy management. The combination of bio-inspired optimization algorithms with ensemble machine learning approaches has significant promise for improving energy consumption predictions, which are essential for sustainable home energy management. © 2024 IEEE.

关键词： Adversarial machine learning

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DrugEL: Ensemble Learning Model for Identification of Druggable Proteins

DrugEL: Ensemble Learning Model for Identification of Drugga...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Wu, Fang-Xiang University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada University of Saskatchewan Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the development of targeted therapies for diseases like cancer and metabolic disorders. Identifying druggable proteins involves various methods, including computational prediction models, mass spectrometry (MS), and biochemical assays, but achieving high accuracy remains a challenge. This study proposes DrugEL, an ensemble learning model that uses Bayesian inference to integrate predictions from multiple algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), LightGBM (LGBM), and Decision Tree (DT) with seven feature extraction methods (LSA, AAC, PAAC, GAAC, NMBroto, AAIndex, and KNN). The results show that DrugEL outperforms existing models in terms of accuracy (0.9758), MCC (0.9515), AUC (0.9758), sensitivity (0.9742) and specificity (0.9774), particularly excelling with the LSA method. © 2024 IEEE.

关键词： Contrastive Learning

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An enhanced prediction of tumor using transfer learning based framework 2

An enhanced prediction of tumor using transfer learning base...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Yadav, Surjeet Ramachandran, T. Pandey, Arvind Kumar Vivekananda Global University Department of Mechanical Engineering Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Switch mastering involves the transfer of understanding from one device mastering version to every other, with the purpose of getting better performance from the version being trained. This approach has currently been used to improve the effectiveness of deep-gaining knowledge of networks for most cancer detection. Specifically, the switch getting-to-know method is applied to best-song a pre-trained deep studying network with medical imaging records to research functions related to most cancer datasets. The first-class-tuned deep learning network is then used to classify newly affected person pics for analysis and prognosis of most cancers. The method of switching getting to know has the gain of substantially decreasing the amount of information needed to teach a deep learning version, in addition to offering multiplied accuracy of the model and improving generalization capability. Moreover, transfer studying may be used to investigate exclusive aspects of most cancers and discover new functions in cancer datasets. As such, transfer getting to know from deep learning networks is a powerful device for most cancer detection. © 2024 IEEE.

关键词： Medical imaging

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A Case Study on the Convergence of Direct Policy Search for Linear Quadratic Gaussian Control

A Case Study on the Convergence of Direct Policy Search for ...

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2024 American Control Conference, ACC 2024

作者： Keivan, Darioush Seiler, Peter Dullerud, Geir Bin, H. University of Illinois at Urbana-Champaign Department of Mechanical Science & Engineering United States University of Michigan Department of Electrical Engineering and Computer Science United States University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350382655

Policy optimization has gained renewed attention from the control community, serving as a pivotal link between control theory and reinforcement learning. In the past few years, the global convergence theory of direct policy search on state-feedback linear control benchmarks has been developed. However, it remains difficult to establish the global convergence of policy optimization on the linear quadratic Gaussian (LQG) problem, marked by the presence of suboptimal stationary points and the lack of cost coerciveness. In this paper, we revisit the policy optimization intricacies of LQG via a case study on first-order single-input single-output (SISO) systems. For this case study, while the issue related to suboptimal stationary points can be easily fixed via parameterizing the policy class more carefully, the non-coerciveness of the LQG cost function still poses a substantial obstacle to a straightforward global convergence proof for the policy gradient method. Our contribution, within the scope of this case study, introduces an approach to construct a positive invariant set for the policy gradient flow, addressing the non-coerciveness issue in the global convergence proof. Based on our analysis, the policy gradient flow can be guaranteed to converge to the globally optimal full-order dynamic controller in this particular scenario. In summary, although centered on a specific case study, our work broadens the comprehension of how the absence of coerciveness impacts LQG policy optimization, highlighting inherent complexities. © 2024 AACC.

关键词： Cost functions

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Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第3期19卷 29-59页

作者： Simon Elias Bibri John Krogstie Amin Kaboli Alexandre Alahi School of Architecture Civil and Environmental Engineering(ENAC)Civil Engineering Institute(IIC)Visual Intelligence for Transportation(VITA)Swiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway School of Engineering Institute of Mechanical EngineeringSwiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland

The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart *** strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental *** aligns with the visionary concept of smarter eco-cities,an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental ***,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying *** bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leadingedge AI and AIoT solutions for environmental *** ensure thoroughness,the study employs a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis *** the core of this study lie these subsequent research inquiries:What are the foundational underpinnings of emerging smarter eco-cities,and how do they intricately interrelate,particularly urbanism paradigms,environmental solutions,and data-driven technologies?What are the key drivers and enablers propelling the materialization of smarter eco-cities?What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices,and what potential benefits and opportunities do they offer for smarter eco-cities?What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities?The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologi

关键词： Smarter eco-cities Smart eco-cities Smart cities Artificial intelligence Artificial intelligence of things Machine learning Environmental sustainability Climate change

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Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks 63

Stability and Performance Analysis of Discrete-Time ReLU Rec...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Noori, Sahel Vahedi Hu, Bin Dullerud, Geir Seiler, Peter University of Michigan Department of Electrical Engineering & Computer Science United States University of Illinois Department of Electrical and Computer Engineering Urbana-Champaign United States University of Illinois Department of Mechanical Science and Engineering Urbana-Champaign United States

ISBN: (纸本)9798350316339

This paper presents sufficient conditions for the stability and 2-gain performance of recurrent neural networks (RNNs) with ReLU activation functions. These conditions are derived by combining Lyapunov/dissipativity theory with Quadratic Constraints (QCs) satisfied by repeated ReLUs. We write a general class of QCs for repeated ReLUs using known properties for the scalar ReLU. Our stability and performance condition uses these QCs along with a 'lifted' representation for the ReLU RNN. We show that the positive homogeneity property satisfied by a scalar ReLU does not expand the class of QCs for the repeated ReLU. We present examples to demonstrate the stability / performance condition and study the effect of the lifting horizon. © 2024 IEEE.

关键词： Recurrent neural networks

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