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The Optimal Design of TMD for Offshore Structures

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China Ocean Engineering 1999年第2期14卷 133-144页

作者： Sau Lon james hu Tomotsuka TAKAYAMA Dept. of Ocean Engrg. University of Rhode Island RI02881 Professor Professor Disaster Prevention Res. Inst. Kyoto Univ. Gokasho Uji Kyoto 611-0011 Japan.

This paper presents the optimal design procedure of Tuned Mass Damper (TMD) for reducing vibration of an actual steel jacket offshore platform excited by random wave loading. In this study, a frequency domain is taken. The force on the structure is determined by use of the linearized Morison equation for an input Power Spectral Density (PSD) of wave elevation. The sensitivity of optimum values of TMD to characteristic parameters of random wave spectrum is analyzed. An optimized TMD design for the modeled platform is given based on design conditions and the findings of the study.

关键词： tuned mass damper design Morison equation power spectral density offshore structures

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Template-assisted growth of Co-BaTiO_(3) vertically aligned nanocomposite thin films with strong magneto-optical coupling effect

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Nano Research 2024年第4期17卷 3130-3138页

作者： Zedong hu Juanjuan Lu Hongyi Dou Jianan Shen james PBarnard Juncheng Liu Xinghang Zhang Haiyan Wang School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47906USA School of Materials Engineering Purdue UniversityWest LafayetteIN 47906USA

Oxide-metal based nanocomposite thin films have attracted great interests owing to their unique anisotropic structure and physical properties.A wide range of Au-based oxide-metal nanocomposites have been demonstrated,while other metal systems are scarce due to the challenges in the initial nucleation and growth as well as possible interdiffusions of the metallic *** this work,a unique anodic aluminum oxide(AAO)template was used to grow a thin Co seed layer and the following self-assembled metal-oxide(Co-BaTiO_(3))vertically aligned nanocomposite thin film *** AAO template allows the uniform growth of Co-seeds and successfully deposition of highly ordered Co pillars(with diameter<5 nm and interval between pillars<10 nm)inside the oxide *** magnetic anisotropy and strong magneto-optical coupling properties have been observed.A thin Au-BaTiO_(3) template was also later introduced for further enhanced nucleation and ordered growth of the *** the advantage of such a unique nanostructure,a large out-of-plane(OP)coercive field(Hc)of~5000 Oe has been achieved,making the nanocomposite an ideal candidate for high density perpendicular magnetic tunneling junction(p-MTJ).A strong polar magneto-optical Kerr effect(MOKE)has also been observed which inspires a novel optical-based reading method of the MTJ states.

关键词： vertically aligned nanocomposite(VAN) Co BaTiO_(3) thin film magneto-optical coupling magnetic anisotropy

Multiple-Step Predictive Control for Offshore Structures

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China Ocean Engineering 1999年第3期14卷 231-246页

作者：李华军 Sau-Lon james hu College of Engrg. Ocean University of Qingdao Professor Member of ASCEDepartment of Ocean EngrgUniversity of Rhode IslandNarragansett

Ocean wave propagation is slow, visible and measurable, so a wave theory can be used to approximately predict the imminnent wave force on an offshore structure based on measured, real-time wave elevation near the structure. This predictability suggests the development of a more efficient algorithm, than those that have been developed for structures under wind and seismic loads, for the active vibration control of offshore structures. The present study delveops a mutiple-step predictive optimal control (MPOC) algorithm that accounts for multiple step external loading in the determination of optimal control forces. The control efficiency of the newly developed MPOC algorithm has been Investigated under both regular (single-frequency) and irregular (multiple-frequency) wave loads, and compared with that of two other well-known optimal control algorithms: classical linear optimal control(CLOC) and instantaneous optimal control(IOC).

关键词： active control offshore structure predictive control control algorithm wave load vibration

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Brain regulates weight bearing bone through PGE2 skeletal interoception: implication of ankle osteoarthritis and pain

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Bone Research 2024年第1期12卷 107-121页

作者： Feng Gao Qimiao hu Wenwei Chen Jilong Li Cheng Qi Yiwen Yan Cheng Qian Mei Wan james Ficke Junying Zheng Xu Cao Department of Orthopedic Surgery Johns Hopkins University School of MedicineBaltimoreMaryland 21205USA Department of Biomedical Engineering Johns Hopkins University School of MedicineBaltimoreMaryland 21205USA

Bone is a mechanosensitive tissue and undergoes constant remodeling to adapt to the mechanical loading ***,it is unclear whether the signals of bone cells in response to mechanical stress are processed and interpreted in the *** this study,we found that the hypothalamus of the brain regulates bone remodeling and structure by perceiving bone prostaglandin E2(PGE2)concentration in response to mechanical *** PGE2 levels are in proportion to their weight *** weight bearing changes in the tail-suspension mice,the PGE2 concentrations in bones change in line with their weight bearing *** of cyclooxygenase-2(COX2)in the osteoblast lineage cells or knockout of receptor 4(EP4)in sensory nerve blunts bone formation in response to mechanical ***,knockout of TrkA in sensory nerve also significantly reduces mechanical load-induced bone ***,mechanical loading induces cAMP-response element binding protein(CREB)phosphorylation in the hypothalamic arcuate nucleus(ARC)to inhibit sympathetic tyrosine hydroxylase(TH)expression in the paraventricular nucleus(PVN)for ***,we show that elevated PGE2 is associated with ankle osteoarthritis(AOA)and ***,our data demonstrate that in response to mechanical loading,skeletal interoception occurs in the form of hypothalamic processing of PGE2-driven peripheral signaling to maintain physiologic bone homeostasis,while chronically elevated PGE2 can be sensed as pain during AOA and implication of potential treatment.

关键词： pain elevated PGE2

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Reinforcement Learning with Deep Quantum Neural Networks

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Journal of Quantum Information Science 2019年第1期9卷 1-14页

作者： Wei hu james hu Department of Computer Science Houghton College Houghton NY USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

The advantage of quantum computers over classical computers fuels the recent trend of developing machine learning algorithms on quantum computers, which can potentially lead to breakthroughs and new learning models in this area. The aim of our study is to explore deep quantum reinforcement learning (RL) on photonic quantum computers, which can process information stored in the quantum states of light. These quantum computers can naturally represent continuous variables, making them an ideal platform to create quantum versions of neural networks. Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment. Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards collected. In summary, our findings suggest that having more layers in deep quantum RL can enhance the learning outcome.

关键词： Continuous-Variable Quantum Computers Quantum Machine Learning Quantum Reinforcement Learning Deep Learning Q Learning Actor-Critic Grid World Environment

Distributional Reinforcement Learning with Quantum Neural Networks

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Intelligent Control and Automation 2019年第2期10卷 63-78页

作者： Wei hu james hu Department of Computer Science Houghton College Houghton NY USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

Traditional reinforcement learning (RL) uses the return, also known as the expected value of cumulative random rewards, for training an agent to learn an optimal policy. However, recent research indicates that learning the distribution over returns has distinct advantages over learning their expected value as seen in different RL tasks. The shift from using the expectation of returns in traditional RL to the distribution over returns in distributional RL has provided new insights into the dynamics of RL. This paper builds on our recent work investigating the quantum approach towards RL. Our work implements the quantile regression (QR) distributional Q learning with a quantum neural network. This quantum network is evaluated in a grid world environment with a different number of quantiles, illustrating its detailed influence on the learning of the algorithm. It is also compared to the standard quantum Q learning in a Markov Decision Process (MDP) chain, which demonstrates that the quantum QR distributional Q learning can explore the environment more efficiently than the standard quantum Q learning. Efficient exploration and balancing of exploitation and exploration are major challenges in RL. Previous work has shown that more informative actions can be taken with a distributional perspective. Our findings suggest another cause for its success: the enhanced performance of distributional RL can be partially attributed to its superior ability to efficiently explore the environment.

关键词： Continuous-Variable Quantum Computers Quantum Reinforcement Learning Distributional Reinforcement Learning Quantile Regression Distributional Q Learning Grid World Environment MDP Chain Environment

Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

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Natural Science 2019年第1期11卷 17-27页

作者： Wei hu james hu Department of Computer Science Houghton College Houghton NY USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.

关键词： Continuous-Variable Quantum Computers Quantum Machine Learning Quantum Reinforcement Learning Contextual Multi-Armed Bandit Problem

Q Learning with Quantum Neural Networks

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Natural Science 2019年第1期11卷 31-39页

作者： Wei hu james hu Department of Computer Science Houghton College Houghton NY USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.

关键词： Continuous-Variable Quantum Computers Quantum Machine Learning Quantum Reinforcement Learning Q Learning Grid World Environment

Molecular characterization of an outbreak-involved Bacillus anthracis strain confirms the spillover of anthrax from West Africa

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Infectious Diseases of Poverty 2024年第1期13卷 93-94页

作者： Shuchao Wang Roland Suluku Mohamed B.Jalloh Ahmed F.Samba Baogui Jiang Yubiao Xie Doris Harding Mengyao Zhang Foday Sahr Mahmud E.Sesay james S.Squire Mohamed A.Vandi Moinina N.Kallon Shoufeng Zhang Rongliang hu Yuee Zhao Zhiqiang Mi Changchun Veterinary Research Institute Chinese Academy of Agricultural Sciences Changchun China Department of Animal Sciences School of Agriculture and Food Sciences Njala University Njala Sierra Leone Department of Microbiology College of Medicine and Allied Health Sciences University of Sierra Leone Freetown Sierra Leone Ministry of Agriculture and Food Sciences Freetown Sierra Leone Beijing Institute of Microbiology and Epidemiology Beijing China Ministry of Health and Sanitation Freetown Sierra Leone Beijing Institute of Biotechnology Beijing China

Background Anthrax,a zoonotic disease caused by the spore-forming bacterium Bacillus anthracis,remains a major global public health concern,especially in countries with limited *** Leone,a West African country historically plagued by anthrax,has almost been out of report on this disease in recent *** this study,we described a large-scale anthrax outbreak affecting both animals and humans and attempted to characterize the *** The causative agent of the animal outbreak in Port Loko District,Sierra Leone,between March and May 2022 was identified using the nanopore sequencing technique.A nationwide active surveillance was implemented from May 2022 to June 2023 to monitor the occurrence of anthrax-specific symptoms in *** cases were subsequently verified using quantitative polymerase chain ***-genome sequencing was accomplished by combining long-read and short-read sequencing *** phylogenetic analysis was performed based on the full-chromosome single nucleotide *** The outbreak in Port Loko District,Sierra Leone,led to the death of 233 animals between March 26th and May 16th,*** ruled out the initial suspicion of Anaplasma species and successfully identified *** as the causative agent of the *** a result of the government's prompt response,out of the 49 suspected human cases identifed during the one-year active surveillance,only 6 human cases tested positive,all within the frst month after the official declaration of the *** phylogenetic analysis indicated that the BaSL2022 isolate responsible for the outbreak was positioned in the ***.153 clade within the TransEuroAsian group of *** We successfully identifed a large-scale anthrax outbreak in Sierra *** causative isolate of ***,BaSL2022,phylogenetically bridged other lineages in ***.153 clade and neighboring genetic groups,***.144 and ***.148,

关键词： Molecular identification Anthrax Bacillus anthracis Sierra Leone Phylogeny Single nucleotide polymorphism