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2023 Laser Applications Conference, LAC 2023

作者： Wang, Ruixuan Theis, Logan Homa, Daniel Wang, Anbo Pickrell, Gary Center for Photonics Technology The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Center for Photonics Technology Department of Material Science and Engineering Virginia Tech BlacksburgVA United States

This paper reports on a wire horn structure to enhance acoustic-optical fiber coupling. Compared with the conventional schemes, it was proved to enhance the ultrasonic wave peak-to-peak amplitude coupling efficiency b... 详细信息

关键词： Ultrasonic waves

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Contrast enhancement through an air-water interface with polarization imaging 11

Contrast enhancement through an air-water interface with pol...

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Polarization Science and Remote Sensing XI 2023

作者： Venkatesulu, Erica Buglione, Kyndra L. Field, Nathaniel J. Doney, Kirstin D. Shaw, Joseph A. Electrical and Computer Engineering Department Optical Technology Center Montana State University P.O. Box 173515 Bozeman United States Lockheed-Martin Space Palo AltoCA94304 United States

ISBN: (纸本)9781510665941

It is well known that underwater objects become more readily visible when viewed through a vertical polarizer that suppresses horizontally polarized reflections from the air-water interface. However, quantitative measurements of the contrast enhancement achieved with a polarizer do not seem to have been reported in the literature. To measure the polarization-enabled contrast enhancement, we placed white and black tiles next to each other, immersed in water, then measured the optical contrast between them as a function of viewing angle (relative to the surface normal) with a polarization camera that simultaneously recorded images with linear polarization oriented 0°, 90°, and 45°from horizontal. Images were recorded with an RGB polarization camera through approximately 45 cm of water at Bozeman Pond and with a monochrome polarization camera through approximately 5 cm of water at Bozeman Beach. Images also were recorded with the monochrome camera and a filter to isolate the near infrared band of approximately 750 to 1000 nm. Indoor laboratory measurements also were recorded to verify the role of the color of the reflecting background. All experiments used carefully calibrated division-of-focal-plane polarization cameras. The observed contrast decreased with viewing angle, but less so for the vertically polarized images. The contrast enhancement, represented by the ratio of vertically polarized to unpolarized contrast, increased with viewing angle, even past the Brewster angle (approx. 53°). The contrast enhancement only began decreasing for viewing angles larger than 70°. In outdoor experiments with a mostly clear sky, the highest contrast enhancement was in the blue spectral band. The contrast was essentially the same for red, green, and blue bands with a white background. In all measurements, the black tile exhibited much larger degree of linear polarization, which is an example of the Umov effect. In this paper we describe the experiments, show and explain polariza

关键词： Polarization

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Time Series Slow Feature Extraction for Dow Data Problem

Time Series Slow Feature Extraction for Dow Data Problem

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Chinese Control and Decision Conference, CCDC

作者： Jiajun Xu Changzhe Lei Daming Xu Xiuli Zhu Department of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Handling process data characterized by strong nonlinearity, high dimensionality, cross-correlations, and auto-correlations presents a considerable hurdle for data-driven soft sensor modeling. While traditional slow feature analysis (SFA) adeptly captures slow and static features from linear data, it may falter in capturing the nonlinear, high-dimensional, and dynamic features inherent in time series data. Conversely, although Long Short-Term Memory (LSTM) networks are designed to address long-term dependencies within sequences, they encounter challenges, particularly with very lengthy data sequences, in effectively capturing these dependencies. Consequently, relying solely on SFA or LSTM may prove inadequate for addressing the complexities of time series data in industrial processes. To confront these challenges, this study proposes an innovative approach termed Slow LSTM (SLSTM), which amalgamates the hidden layers of LSTM and SFA to enhance feature extraction. These extracted features are subsequently fed into a fully connected layer for prediction. The efficacy of this proposed method is validated through comparisons with various methods, including SFA-FC, LSTM, and RNN.

关键词： Sensitivity Soft sensors Impurities Time series analysis Feature extraction Prediction algorithms Data models

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Formal Modeling of Road Network-Based Autonomous Vehicle Validation Scenarios With Intersections and Pedestrians

Formal Modeling of Road Network-Based Autonomous Vehicle Val...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Kutlu, Ilke Goss, Quentin Akinci, Tahir Cetin Ilhan Akbas, Mustafa İstanbul Technical University Department of Electrical Engineering İstanbul Turkey Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science FL United States University of California Riverside Center for Environmental Research and Technology RiversideCA United States

ISBN: (纸本)9798350367942

Autonomous vehicles (AVs) are an exciting emerging technology that reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, increasing driving quality and safety. Validation testing of AVs provides the assurance of quality and safety, and is also an integral part of new AV research and development. Following in suite of their predecessor, i.e. non-autonomous vehicles, scenario-based validation testing is a safe, time and cost effective means of testing these complex, costly, and safety critical systems. In this paper we propose an open-source implementation of a scenario-based AV validation testing approach for AVs in traffic environments with pedestrians and intersections. Furthermore, we utilize the approach to implement scenarios of previously unimplementable AV scenarios from the Atomic Blocks dataset. © 2024 IEEE.

关键词： Automobile testing

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The Optimal Shape of the Surface Hardening Head Obtained by Numerical Modeling 15

The Optimal Shape of the Surface Hardening Head Obtained by ...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Mihaela Cornelia Codrean, Marius Gordan, Mircea Codrean, Mihaela Oproescu, Mihai Novac, Ovidiu Constantin Slovac, Francisc University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Computers and Electrical Engineering University of Pitesti Faculty of Electronics Communication and Computer Science Department of Elecytronics Pitesti Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania

ISBN: (纸本)9798350321388

This paper presents some aspects of the numerical modeling performed in order to study the process of induction heating applied to a workpiece, using Flux 2D and FEMM (Finite Element Method Magnetics), two software tools specifically designed for electromagnetic simulations. The paper presents a significant technical finding regarding the optimal shape of the surface hardening head. Various shapes of the inductor were analyzed, focusing on solving the eddy current problem using the commercial program FEMM. The induction heating process was numerically simulated using the FLUX 2D program for a semi-finished punch made of OLC 45 steel, specifically for hot surface heat treatment. The simulations were conducted utilizing the program's simulation capabilities. © 2023 IEEE.

关键词： Induction heating

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Chip-scale spontaneous quasi-phase matched second harmonic generation in a micro-racetrack resonator

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Science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 66-74页

作者： Tingge Yuan Jiangwei Wu Yi'an Liu Xiongshuo Yan Haowei Jiang Hao Li Zhaokang Liang Qiang Lin Yuping Chen Xianfeng Chen State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and Astronomy.Shanghai Jiao Tong UniversityShanghai 200240China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal UniversityJinan 250358China Department of Electrical and Computer Engineering University of RochesterNew York 14627USA

In this paper,we demonstrate efficient spontaneous quasi-phase matched(SQPM)second harmonic generation(SHG)in a microracetrack resonator on X-cut thin film lithium *** approach does not involve poling,but exploits the anisotropy of the crystals to allow the phase-matching condition to be fulfilled spontaneously as the TE-polarized light circulates in a specifically designed racetrack *** experiment,normalized on-chip conversion efficiencies of 1.01×10-4/W and 0.43×10-4/W are achieved by 37th-order and 111th-order SQPM,*** configurable SQPM will benefit the application of nonlinear frequency conversion and quantum source generation in chip-scale integrated photonics compatible with standard CMOS fabrication processes.

关键词： lithium niobate on insulator quasi-phase matching second harmonic generation

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Fiber-optic sensor arrays for liquid identification 28

Fiber-optic sensor arrays for liquid identification

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28th International Conference on optical Fiber Sensors, OFS 2023

作者： Naku, Wassana Nambisan, Anand K. Roman, Muhammad Zhu, Chen Gerald, Rex E. Huang, Jie School of Engineering University of Phayao Phayao56000 Thailand Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China

We propose an array of fiber-optic sensors based on the Fabry-Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOL). © Optica Publishing Group 2023, ©... 详细信息

ISBN: (纸本)9781957171302

关键词： Fiber optic sensors

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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