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1,186 results on '"Zhang, Tianhao"'

557. O2-03-03: Non-Resilient Brain Aging in Association with Cardiovascular Risk and White Matter Hyperintensities: the Ship Study

Author

Habes, Mohamad, primary, Guray, Erus, additional, Toledo, Jon B., additional, Zhang, Tianhao, additional, Bryan, R.N., additional, Janowitz, Deborah, additional, Doshi, Jimit, additional, von Sarnowski, Bettina, additional, Hegenscheid, Katrin, additional, Voelzke, Henry, additional, Schminke, Ulf, additional, Hoffmann, Wolfgang, additional, Grabe, Hans J., additional, and Davatzikos, Christos, additional

Published
2016
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563. Manufacturing excellence using multi-platform ellipsometry thickness measurement fleet on advanced nodes

Author

Lenahan, Michael, primary, Vaid, Alok, additional, Mahendrakar, Sridhar, additional, Seipp, Steven, additional, Jayez, David, additional, Yueh, Alice, additional, Saxena, Shweta, additional, Solecky, Eric, additional, Gizzi, Samuel, additional, Heller, Amir, additional, Zhang, Tianhao, additional, Song, Da, additional, Yoon, Nam Hee, additional, Camp, Janay, additional, and Venkataraman, Kartik, additional

Published
2016
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564. A Multimodal MRI Study in Mild Traumatic Brain Injury: Correlation of Imaging Features with Clinical Symptoms (S11.002)

Author

Marinelli, Luca, primary, Chebrolu, Venkata, additional, Gallenberg, Amy, additional, Gupta, Sandeep, additional, Joel, Suresh, additional, Li, Xia, additional, Lo Castro, Eve, additional, Madhavan, Radhika, additional, Middione, Matthew, additional, Mullick, Rakesh, additional, Prastawa, Marcel, additional, Ravishankar, Hariharan, additional, Shankaranarayanan, Ajit, additional, Sperl, Jonathan, additional, Tan, Ek Tsoon, additional, Worters, Pauline, additional, Zhang, Tianhao, additional, Niogi, Sumit, additional, Tsiouris, A. John, additional, Miranda, Victor, additional, and Shetty, Teena, additional

Published
2016
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566. White matter hyperintensities and imaging patterns of brain ageing in the general population

Author

Habes, Mohamad, primary, Erus, Guray, additional, Toledo, Jon B., additional, Zhang, Tianhao, additional, Bryan, Nick, additional, Launer, Lenore J., additional, Rosseel, Yves, additional, Janowitz, Deborah, additional, Doshi, Jimit, additional, Van der Auwera, Sandra, additional, von Sarnowski, Bettina, additional, Hegenscheid, Katrin, additional, Hosten, Norbert, additional, Homuth, Georg, additional, Völzke, Henry, additional, Schminke, Ulf, additional, Hoffmann, Wolfgang, additional, Grabe, Hans J., additional, and Davatzikos, Christos, additional

Published
2016
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567. Path-following Control of Fish-like Robots: A Deep Reinforcement Learning Approach ⁎⁎This work was supported in part by grants from the National Natural Science Foundation of China (NSFC, No. 61973007, 61633002, U1909206.

Author

Zhang, Tianhao, Tian, Runyu, Wang, Chen, and Xie, Guangming

Abstract

In this paper, we propose a deep reinforcement learning (DRL) approach for path-following control of a fish-like robot. The desired path may be a randomly generated Bézier curve. First, to implement the locomotion control of the fish-like robot, we design a modified Central Pattern Generated (CPG) model, using which the fish achieves varied swimming behaviors just by adjusting a single control input. To reduce the reality gap between simulation and the physical system, using the experimental data of the real fish-like robot, we build a surrogate simulation environment, which also well balances the accuracy and the speed of training. Second, for the path-following control, we select the advantage actor-critic (A2C) approach and train the control policy in the surrogate simulation environment with a straight line as the desired path. Then the trained control policy is directly deployed on a physical fish-like robot to follow a randomly generated Bézier curve. The experimental results show that our proposed approach has good practical applicability in view of its efficiency and feasibility in controlling the physical fishlike robot. This work shows a novel and promising way to control biomimetic underwater robots in the real world.

Published
2020
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568. Front Cover Image.

Author

Wu, Fengming, Du, Zhong, Zhang, Tianhao, Jiang, Lei, Zhang, Lijie, and Ge, Siqin

Subjects
COWPEA weevil
Abstract

The front cover image is based on the Research Article A neurotransmitter histamine mediating phototransduction and photopreference in Callosobruchus maculatus by Fengming Wu et al., https://doi.org/10.1002/ps.7475. Image Credit: Runzhi Zhang. [ABSTRACT FROM AUTHOR]

Published
2023
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569. Ti3C2TxMXene-mediating near- and long-range electronic effect on atomically dispersed Co for efficient lithium-sulfur batteries

Author

Wang, Haiyan, Zhang, Hucheng, Wang, Lijing, Li, Yonghui, Zhang, Tianhao, Lu, Zhansheng, Jiang, Hao, Li, Chunzhong, and Wang, Jianji

Abstract

Sluggish sulfur conversion kinetics pose an ongoing challenge in lithium-sulfur batteries (LSBs). Here, we present a solution through far-reaching long-range electronic regulation (LRER) on single-atom active sites. N-doped carbons (Co-NC) are implanted with densely-distributed Co single atoms, and supported on Ti3C2TxMXene substrates to assemble 3D Co-NC/MXene catalyst. MXene effectively mediates interlayer charge transfer (∼0.70 |e|) contrasted with popular carbon materials (∼0.06 |e|) to produce LRER through surrounding carbon atoms. The synergy of LRER with near-range electronic regulation (NRER) tunes electronic structures, and enhances heterostructural stability, thus provoking desirous catalytic kinetics of Co single atoms in sulfur reduction. Thereby, the Co-NC/MXene/S cathodes exhibit impressive rate performance and excellent cycling stability (only 0.015% capacity decay per cycle over 600 cycles at 4 C) in LSBs, surpassing state-of-the-art sulfur cathodes. This work reveals the importance of LRER for improved catalysis, and provides new guidance to tailor heterostructures to achieve high-efficient catalysts in various process.

Published
2024
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570. Separating Homogeneous and Inhomogeneous Line Widths of Heavy- and Light-Hole Excitons in Weakly Disordered Semiconductor Quantum Wells

Author

Bristow, Alan D., Zhang, Tianhao, Siemens, Mark E., Cundiff, Steven T., and Mirin, R. P.

Abstract

Optical two-dimensional Fourier-transform spectroscopy is used to study the heavy- and light-hole excitonic resonances in weakly disordered GaAs quantum wells. Homogeneous and inhomogeneous broadening contribute differently to the two-dimensional resonance line shapes, allowing separation of homogeneous and inhomogeneous line widths. The heavy-hole exciton exhibits more inhomogeneous than homogeneous broadening, whereas the light-hole exciton shows the reverse. This situation occurs because of the interplay between the length scale of the disorder and the exciton Bohr radius, which affects the exciton localization and scattering. Utilizing this separation of line widths, excitation-density-dependent measurements reveal that many-body interactions alter the homogeneous dephasing, while disorder-induced dephasing is unchanged.

Published
2024
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571. An Optimization-based Approach to Dosimetry Planning for Brachytherapy

Author

Zhang, Tianhao Tiam

Subjects
Prostate Cancer, Dosimetry, Lung Cancer, LDR Brachytherapy, Pre­ planning
Abstract

Prostate cancer is the second leading cause of death from cancer in North American men, with a reported 32,050 deaths in the U.S. alone for 2010; lung cancer is reported as the number one leading cause of death from cancer in both men and women in North America, its estimated death toll in the U.S. alone in 2010 is over 157,000. One method of treating prostate cancer patients nowadays is by Low Dose Rate Brachytherapy, a process where radioactive seeds are placed in or near the tumor site to kill cancerous cells. For lung cancer, brachytherapy has begun to attract attention due to the advent of robotics assistance and there is increasing research currently in the area. While brachytherapy is gaining popularity as a commonly practiced method for treating cancer patients, the procedure itself has several drawbacks that require further research. One such drawback is that the dosimetry plan created based on the pre-operative imaging may not be accurate due to (a) the change in the tumor’s size as a result of the time elapsed between pre-operative imaging and seed implantation; and (b) movement of the organ under treatment from the position and orientation in pre­ operative imaging; this is particularly important in the case of lung brachytherapy as it would have to take into account lung deflation and respiratory and cardiac motions as well. In addition, seeds may be misplaced during implantation as a result of limitation of the manual or robotic procedures. When this happens, the final dose coverage of the tumor is no longer the same as the intended coverage in the dosimetry plan. In this thesis, the development, implementation and evaluation of two algorithms are presented.The first algorithm is the pre-planning algorithm, which aims to reduce the errors in the dosimetry plan caused by the change in the tumor’s size by providing a mechanism to perform dosimetry planning on-line. By doing this, the first algorithm can also eliminate the need for the patient to be imaged twice, so that the same set of images can be used for dosimetry planning as well as seed implantation. The second algorithm deals with intra-operative dynamic dose optimization, where real­ time seed compensation is performed to compensate for any seed misplacements so that an optimal final coverage can be achieved. The results of the experimental evaluation performed in this project indicate that these algorithms are feasible and have the potential to be applied in the operating room following appropriate animal and clinical validation.

Published
2011

573. Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search

Author

Zhang, Tianhao, Kahn, Gregory, Levine, Sergey, Abbeel, Pieter, Zhang, Tianhao, Kahn, Gregory, Levine, Sergey, and Abbeel, Pieter

Abstract

Model predictive control (MPC) is an effective method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters. However, application of MPC can be computationally demanding, and typically requires estimating the state of the system, which can be challenging in complex, unstructured environments. Reinforcement learning can in principle forego the need for explicit state estimation and acquire a policy that directly maps sensor readings to actions, but is difficult to apply to unstable systems that are liable to fail catastrophically during training before an effective policy has been found. We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment. This data is used to train a deep neural network policy, which is allowed to access only the raw observations from the vehicle's onboard sensors. After training, the neural network policy can successfully control the robot without knowledge of the full state, and at a fraction of the computational cost of MPC. We evaluate our method by learning obstacle avoidance policies for a simulated quadrotor, using simulated onboard sensors and no explicit state estimation at test time.

Published
2015

577. Research of Optical Fiber Fabry-Perot Acoustic Vibration Sensing Demodulation System Based on Dual Tunable Lasers

Author

何盼 He Pan, 张天昊 Zhang Tianhao, 刘琨 Liu Kun, 刘铁根 Liu Tiegen, 樊茁 Fan Zhuo, and 王双 Wang Shuang

Subjects
Optical fiber, Materials science, business.industry, Distributed acoustic sensing, Laser, Atomic and Molecular Physics, and Optics, law.invention, Dual (category theory), Optics, law, Demodulation, Electrical and Electronic Engineering, business, Fabry–Pérot interferometer, Acoustic vibration
Published
2016
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584. Multimodal Magnetic Resonance Imaging Study of Treatment-Naïve Adults with Attention-Deficit/Hyperactivity Disorder

Author

Chaim, Tiffany M., primary, Zhang, Tianhao, additional, Zanetti, Marcus V., additional, da Silva, Maria Aparecida, additional, Louzã, Mário R., additional, Doshi, Jimit, additional, Serpa, Mauricio H., additional, Duran, Fabio L. S., additional, Caetano, Sheila C., additional, Davatzikos, Christos, additional, and Busatto, Geraldo F., additional

Published
2014
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593. Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey.

Author

Zhang, Tianhao, Aftab, Waqas, Mihaylova, Lyudmila, Langran-Wheeler, Christian, Rigby, Samuel, Fletcher, David, Maddock, Steve, and Bosworth, Garry

Subjects
*SUICIDE prevention, *COMPUTER network security, *JOINT use of railroad facilities, *VIDEO surveillance, *CLOSED-circuit television, *OBJECT recognition (Computer vision), *COMPUTER vision
Abstract

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered. [ABSTRACT FROM AUTHOR]

Published
2022
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