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26th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2015

作者： Seltzer, D.M. Wang, X. Nassar, A.R. Schiano, J.L. Reutzel, E.W. School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States Department of Mechanical and Nuclear Engineering Pennsylvania State University University ParkPA16802 United States Applied Research Laboratory Pennsylvania State University University ParkPA16802 United States

Additive manufacturing of metal parts is a complex process where many variables determine part quality. In addition to manipulated process variables, such as travel speed, feedstock flow pattern, and energy distribution, other exogenous inputs also determine part quality. For example, changing build geometry and a growing global temperature. In addition, there are random external disturbances such as spatter on a cover lens. Both manipulated process variables and exogenous inputs affect dimensional tolerance, microstructure, and other properties that determine the final part quality. Our long term aim is to improve part quality through real-time regulation of measurable process variables using vision-based feedback control. As a starting point, we present a process model that relates scanning speed and laser power to build height and melt pool width. These results demonstrate the necessity for using multi-input multi-output feedback control techniques and provide information for refining the frame rate and spectral sensitivity of the imaging system. © SFF *** rights reserved.

关键词： Feedback control

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Comparison of gas in black-box scenarios 19

Comparison of gas in black-box scenarios

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2019 Genetic and Evolutionary Computation Conference, GECCO 2019

作者： Yang, Cheng-Hao Cheong, Hou-Teng Yu, Tian-Li Taiwan Evolutionary Intelligence Laboratory Department of Electrical Engineering National Taiwan University Taiwan Taiwan Evolutionary Intelligence Laboratory Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9781450367486

In black-box optimization scenarios, researchers have no control over the fitness functions, and hence genetic algorithms (GAs) are usually compared by the number of function evaluations. Commonly used statistics include arithmetic mean, median, and standard deviation. However, these statistics can be misleading. For example, when there exist unsolvable instances within limited time, median simply ignores those instances, and arithmetic mean is not applicable at all. In this paper, we propose comparison methods from a practical point of view. Specifically, we propose three use cases which cover most of the situations that GA practitioners may encounter. Among these three use cases, two of them are matchups, which requires a pair of GAs to be compared with each other, while the other provides as a standalone performance indicator of GAs. © 2019 Association for Computing Machinery.

关键词： Genetic algorithms

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Generation of super-resolved optical needle and multifocal array using graphene oxide metalenses

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Opto-Electronic Advances 2021年第2期4卷 1-11页

作者： Hongtao Wang Chenglong Hao Han Lin Yongtian Wang Tian Lan Chengwei Qiu Baohua Jia Centre for Translational Atomaterials Faculty of ScienceEngineering and TechnologySwinburne University of TechnologyP.O.Box 218Hawthorn VIC 3122Australia Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Engineering Product Development Pillar Singapore University of Technology and Design8 Somapah Road487372Singapore

Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial ***-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like *** this work,we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material *** experimentally validate our strategies via demonstrations of two graphene oxide metalenses:one with an ultra-long(~16λ)optical needle,and the other with axial multifocal spots,at the wavelength of 632.8 nm with a 200 nm thin *** proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.

关键词： femtosecond laser reduction graphene oxide metalens multifocal spots optical needle

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PuzzleTuning: Explicitly Bridge Pathological and Natural Image with Puzzles

arXiv

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arXiv 2023年

作者： Zhang, Tianyi Lyu, Shangqing Lei, Yanli Chen, Sicheng Ying, Nan He, Yufang Liu, Zeyu Zhao, Yu Feng, Yunlu Jin, Yueming Lee, Hwee Kuan Zhang, Guanglei The Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China Singapore138410 Singapore The School of Microelectronics Xi’an Jiaotong University Xi’an710049 China The Department of Pathology Peking Union Medical College Hospital Beijing100006 China The Department of Gastroenterology Peking Union Medical College Hospital Beijing100006 China The Department of Biomedical Engineering National University of Singapore Singapore117417 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117417 Singapore The School of Computing National University of Singapore Singapore117417 Singapore School of Biological Sciences Nanyang Technological University 639798 Singapore 169856 Singapore International Research Laboratory on Artifical Intelligence 138632 Singapore Singapore Institute for Clinical Sciences Singapore117609 Singapore

Pathological image analysis is a crucial field in computer vision. Due to the annotation scarcity in the pathological field, pre-training with self-supervised learning (SSL) is widely applied to learn on unlabeled images. However, the current SSL-based pathological pre-training: (1) does not explicitly explore the essential focuses of the pathological field, and (2) does not effectively bridge with and thus take advantage of the knowledge from natural images. To explicitly address them, we propose our large-scale PuzzleTuning framework, containing the following innovations. Firstly, we define three task focuses that can effectively bridge knowledge of pathological and natural domain: appearance consistency, spatial consistency, and restoration understanding. Secondly, we devise a novel multiple puzzle restoring task, which explicitly pre-trains the model regarding these focuses. Thirdly, we introduce an explicit prompt-tuning process to incrementally integrate the domain-specific knowledge. It builds a bridge to align the large domain gap between natural and pathological images. Additionally, a curriculum-learning training strategy is designed to regulate task difficulty, making the model adaptive to the puzzle restoring complexity. Experimental results show that our PuzzleTuning framework outperforms the previous state-of-the-art methods in various downstream tasks on multiple datasets. The code, demo, and pre-trained weights are available at https://***/sagizty/PuzzleTuning. © 2023, CC BY-NC-SA.

关键词： Image analysis

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Ferroelectricity in Hafnia Controlled via Surface Electrochemical State

arXiv

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arXiv 2022年

作者： Kelley, Kyle P. Morozovska, Anna N. Eliseev, Eugene A. Liu, Yongtao Fields, Shelby S. Jaszewski, Samantha T. Mimura, Takanori Ihlefeld, Jon F. Kalinin, Sergei V. Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Institute of Physics National Academy of Sciences of Ukraine pr. Nauki 46 Kyiv03028 Ukraine Institute for Problems of Materials Science National Academy of Sciences of Ukraine Krjijanovskogo 3 Kyiv03142 Ukraine Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Ferroelectricity in binary oxides including hafnia and zirconia have riveted the attention of the scientific community due to highly unconventional physical mechanisms and the potential for integration of these materials into semiconductor workflows. Over the last decade, it has been argued that behaviors such as wake-up phenomena and an extreme sensitivity to electrode and processing conditions suggests that ferroelectricity in these materials is strongly coupled with additional mechanisms, with possible candidates including the ionic subsystem or strain. Here we argue that the properties of these materials emerge due to the interplay between the bulk competition between ferroelectric and structural instabilities, similar to that in classical antiferroelectrics, coupled with non-local screening mediated by the finite density of states at surfaces and internal interfaces. Via decoupling of electrochemical and electrostatic controls realized via environmental and ultra-high vacuum PFM, we show that these materials demonstrate a rich spectrum of ferroic behaviors including partial pressure- and temperature-induced transitions between FE and AFE behaviors. These behaviors are consistent with an antiferroionic model and suggest novel strategies for hafnia-based device optimization. © 2022, CC BY.

关键词： Ferroelectricity

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Allocation of a Variable Series Reactor Considering AC Constraints and Contingencies

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CSEE Journal of Power and Energy Systems 2019年第1期5卷 63-72页

作者： Xiaohu Zhang Chunlei Xu Di Shi Zhiwei Wang Qibing Zhang Guodong Liu Kevin Tomsovic Aleksandar Dimitrovski GEIRI North America San JoseCAUSA State Grid Jiangsu Electric Power Company Nanjing 210024China Power and Energy Group Oak Ridge National LaboratoryTNUSA Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Central FloridaFLUSA

The Variable Series Reactors(VSRs)can efficiently control the power flow through the adjustment of the line *** they are appropriately allocated in the power network,the transmission congestion and generation cost can be *** paper proposes a planning model to optimally allocate VSRs considering AC constraints and multi-scenarios including base case and *** planning model is originally a non-convex large scale mixed integer nonlinear program(MINLP),which is generally *** proposed Benders approach decomposes the MINLP model into a mixed integer linear program(MILP)master problem and a number of nonlinear *** case studies based on IEEE 118-bus demonstrate the high performance of the proposed approach.

关键词： N-1 contingencies optimization optimal power flow variable series reactor transmission network

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Deep reinforcement learning for quantum Hamiltonian engineering

arXiv

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arXiv 2021年

作者： Peng, Pai Huang, Xiaoyang Yin, Chao Joseph, Linta Ramanathan, Chandrasekhar Cappellaro, Paola Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

engineering desired Hamiltonian in quantum many-body systems is essential for applications such as quantum simulation, computation and sensing. Conventional quantum Hamiltonian engineering sequences are designed using human intuition based on perturbation theory, which may not describe the optimal solution and is unable to accommodate complex experimental imperfections. Here we numerically search for Hamiltonian engineering sequences using deep reinforcement learning (DRL) techniques and experimentally demonstrate that they outperform celebrated sequences on a solid-state nuclear magnetic resonance quantum simulator. As an example, we aim at decoupling strongly-interacting spin-1/2 systems. We train DRL agents in the presence of different experimental imperfections and verify robustness of the output sequences both in simulations and experiments. Surprisingly, many of the learned sequences exhibit a common pattern that had not been discovered before, to our knowledge, but has an meaningful analytical description. We can thus restrict the searching space based on this control pattern, allowing to search for longer sequences, ultimately leading to sequences that are robust against dominant imperfections in our experiments. Our results not only demonstrate a general method for quantum Hamiltonian engineering, but also highlight the importance of combining black-box artificial intelligence with understanding of physical system in order to realize experimentally feasible applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Optical coherence encryption with structured random light

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PhotoniX 2021年第1期2卷 385-399页

作者： Deming Peng Zhaofeng Huang Yonglei Liu Yahong Chen Fei Wang Sergey A.Ponomarenko Yangjian Cai School of Physical Science and Technology Soochow UniversitySuzhou 215006China Shandong Provincial Engineering and Technical Center of Light Manipulation&Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal UniversityJinan 250014China Department of Electrical and Computer Engineering Dalhousie UniversityHalifaxNova Scotia B3J 2X4Canada Department of Physics and Atmospheric Science Dalhousie UniversityHalifaxNova Scotia B3H 4R2Canada.

Information encryption with optical technologies has become increasingly importantdue to remarkable multidimensional capabilities of light ***,the opticalencryption protocols proposed to date have been primarily based on the first-orderfield characteristics,which are strongly affected by interference effects and make thesystems become quite unstable during light–matter ***,we introduce analternative optical encryption protocol whereby the information is encoded into thesecond-order spatial coherence distribution of a structured random light beam via ageneralized van Cittert–Zernike *** show that the proposed approach hastwo key advantages over its conventional ***,the complexity ofmeasuring the spatial coherence distribution of light enhances the encryption ***,the relative insensitivity of the second-order statistical characteristicsof light to environmental noise makes the protocol robust against the environmentalfluctuations,e.g,the atmospheric *** carry out experiments todemonstrate the feasibility of the coherence-based encryption method with the aid ofa fractional Fourier *** results open up a promising avenue for furtherresearch into optical encryption in complex environments.

关键词： Structured random light Spatial coherence Optical encryption Atmospheric turbulence

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Hypermultiplexed integrated photonics-based optical tensor processor

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science advances 2025年第23期11卷 eadu0228页

作者： Shaoyuan Ou Kaiwen Xue Lian Zhou Chun-Ho Lee Alexander Sludds Ryan Hamerly Ke Zhang Hanke Feng Yue Yu Reshma Kopparapu Eric Zhong Cheng Wang Dirk Englund Mengjie Yu Zaijun Chen Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA 90089 USA. Department of Electrical Engineering and Computer Science University of California Berkeley CA 94720 USA. Research Laboratory of Electronics MIT Cambridge MA 02139 USA. PHI Laboratories NTT Research Inc. 940 Stewart Drive Sunnyvale CA 94085 USA. Department of Electrical Engineering & State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon Hong Kong China. Opticore Inc. Berkeley CA 94705 USA.

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), Internet of Things (IoT), and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here, we demonstrate a hypermultiplexed tensor optical processor that can perform trillions of operations per second using space-time-wavelength three-dimensional optical parallelism, enabling O(N) operations per clock cycle with O(N) modulator devices. The system is built with wafer-fabricated III/V micrometer-scale lasers and high-speed thin-film lithium niobate electro-optics for encoding at tens of femtojoules per symbol. Lasing threshold incorporates analog inline rectifier (ReLU) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clock rates, energy-efficient processing, and programmability unlocks the potential of light for low-energy AI accelerators for applications ranging from training of large AI models to real-time decision-making in edge deployment.

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High thermal conductivity and thermal boundary conductance of homoepitaxially grown gallium nitride (GaN) thin films

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Physical Review Materials 2021年第10期5卷 104604-104604页

作者： Yee Rui Koh Md Shafkat Bin Hoque Habib Ahmad David H. Olson Zeyu Liu Jingjing Shi Yekan Wang Kenny Huynh Eric R. Hoglund Kiumars Aryana James M. Howe Mark S. Goorsky Samuel Graham Tengfei Luo Jennifer K. Hite W. Alan Doolittle Patrick E. Hopkins Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville Virginia 22904 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame Indiana 46556 USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Department of Materials Science and Engineering University of California Los Angeles California 90095 USA Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22904 USA School of Materials Science and Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA U.S. Naval Research Laboratory Washington D.C. 20375 USA Department of Physics University of Virginia Charlottesville Virginia 22904 USA

Gallium nitride (GaN) has emerged as a quintessential wide band-gap semiconductor for an array of high-power and high-frequency electronic devices. The phonon thermal resistances that arise in GaN thin films can result in detrimental performances in these applications. In this work, we report on the thermal conductivity of submicrometer and micrometer thick homoepitaxial GaN films grown via two different techniques (metal-organic chemical vapor deposition and molecular beam epitaxy) and measured via two different techniques (time domain thermoreflectance and steady-state thermoreflectance). When unintentionally doped, these homoepitaxial GaN films possess higher thermal conductivities than other heteroepitaxially grown GaN films of equivalent thicknesses reported in the literature. When doped, the thermal conductivities of the GaN films decrease substantially due to phonon-dopant scattering, which reveals that the major source of phonon thermal resistance in homoepitaxially grown GaN films can arise from doping. Our temperature-dependent thermal conductivity measurements reveal that below 200 K, scattering with the defects and GaN/GaN interface limits the thermal transport of the unintentionally doped homoepitaxial GaN films. Further, we demonstrate the ability to achieve the highest reported thermal boundary conductance at metal/GaN interfaces through in situ deposition of aluminum in ultrahigh vacuum during molecular beam epitaxy growth of the GaN films. Our results inform the development of low thermal resistance GaN films and interfaces by furthering the understanding of phonon scattering processes that impact the thermal transport in homoepitaxially grown GaN.

关键词： Thermal conductivity Thermal properties

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