8 results on '"Zhang, Qiyang"'
Search Results
2. Sparse Reward Exploration Method Based on Trajectory Perception
- Author
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ZHANG Qiyang, CHEN Xiliang, ZHANG Qiao
- Subjects
artificial intelligence ,knowledge transfer ,deep reinforcement learning ,asynchronous advantage actor-critic ,exploration-utilization problem ,Computer software ,QA76.75-76.765 ,Technology (General) ,T1-995 - Abstract
When dealing with sparse reward problems,existing deep RL algorithms often lead to hard exploration,they often only rely on the pre-designed environment reward,so it is difficult to achieve good results.In this situation,it is necessary to design rewards more carefully,make more accurate judgments and feedback on the exploration status of agents.The asynchronous advantage actor-critic(A3C) algorithm improves the training efficiency through parallel training,and improves the training speed of the original algorithm.However,for the environment with sparse rewards,it cannot well solve the problem of difficult exploration.To solve the problem of poor exploration effect of A3C algorithm in sparse reward environment,A3C based on exploration trajectory perception(ETP-A3C) is proposed.The algorithm can perceive the exploration trajectory of the agent when it is difficult to explore in training,further judge and decide the exploration direction of the agent,and help the agent get out of the exploration dilemma as soon as possible.In order to verify the effectiveness of ETP-A3C algorithm,a comparative experiment is carried out with baseline algorithm in five different environments of Super Mario Brothers.The results show that this method has significantly improved the learning speed and model stability.
- Published
- 2023
- Full Text
- View/download PDF
3. Choosing the Right Tool for the Job: Screening Tools for Systematic Reviews in Education.
- Author
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Zhang, Qiyang and Neitzel, Amanda
- Subjects
CHOICE (Psychology) ,MACHINE learning ,ARTIFICIAL intelligence ,RESEARCH personnel - Abstract
In recent years, the rapid development of artificial intelligence has enabled the launch of many new screening tools. This review aims to facilitate screening tool selection through a systematic narrative review and feature analysis. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight studies reported screening tools. More research is needed to understand the reasons behind this phenomenon. After consulting various sources, 26 available screening tools in the market were found. Among them, we identified and evaluated 12 screening tools for educational reviewers and ranked them in descending order of feature score: Covidence (1), DistillerSR (2, tied), EPPI-Reviewer (2, tied), CADIMA (4), Swift-Active (5), Rayyan (6, tied), SysRev (6, tied), Abstrackr (8, tied), ReLiS (8, tied), RevMan (8, tied), ASReview (11), and Excel (12). In the discussion, we provide insights into the promise and bias in tools' machine learning algorithms. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Ta2NiSe5/MoTe2/Graphene van der Waals Heterostructures Toward Ultrabroadband and Polarization‐Sensitive Imaging.
- Author
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Zhang, Qiyang, Wu, Ziqiao, Chen, Xiqiang, Gao, Wei, Yang, Mengmeng, Xiao, Ye, Yao, Jiandong, Liang, Ying, Zheng, Zhaoqiang, Tao, Lili, and Li, Jingbo
- Subjects
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ARTIFICIAL intelligence , *HETEROSTRUCTURES , *DATA mining , *VISIBLE spectra , *PHOTODETECTORS - Abstract
Ultrabroadband and polarization‐sensitive imaging are essential for pioneering advancements in intelligent technology, offering a pivotal pathway to multidimensional information extraction. However, the dearth of appropriate photosensitive semiconductors in the infrared region and the lack of suitably efficient device architecture for the separation and collection of photocarriers significantly impede the development of ultra‐broadband and polarization‐sensitive photodetectors. To address these challenges, a sandwiched Ta2NiSe5/MoTe2/Graphene heterostructure device is engineered. In this device, efficient and polarization‐sensitive photocarriers are generated in the top Ta2NiSe5 layer, rapidly separated through the middle MoTe2 layer, and effectively collected by the bottom graphene layer. As a result, the developed photodetector exhibits an ultra‐broadband and polarization‐sensitive photoresponse that extends from visible light (520 nm) to short‐wave infrared (2200 nm). At 2200 nm, the device displays a notable responsivity of 5.79 A W−1, a specific detectivity of 1010 Jones, and an anisotropic ratio of 1.44. Furthermore, this device successfully demonstrates high‐resolution ultra‐broadband and polarized light imaging capabilities. This study thus presents an intriguing blueprint for the development of advanced 2D imaging platforms for future‐generation intelligent systems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
5. Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology.
- Author
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Zhang, Qiyang, Hu, Yingying, Zhou, Chao, Zhao, Yumo, Zhang, Na, Zhou, Yun, Yang, Yongfeng, Zheng, Hairong, Fan, Wei, Liang, Dong, and Hu, Zhanli
- Subjects
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COMPUTED tomography , *ARTIFICIAL intelligence , *CHILD patients , *NETWORK performance , *POSITRON emission tomography , *IMAGE fusion - Abstract
Objectives: This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. Methods: A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Results: Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Conclusion: Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Improved Policy Optimization Algorithm Based on Curiosity Mechanism.
- Author
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ZHANG Qiyang, CHEN Xiliang, CAO Lei, and LAI Jun
- Subjects
OPTIMIZATION algorithms ,REINFORCEMENT learning ,REWARD (Psychology) ,CURIOSITY ,ARTIFICIAL intelligence - Abstract
In the generation process of reinforcement learning decision model, due to the complex environment and incomplete observation of state information, the classical proximal policy optimization algorithm faces problems such as low exploration and utilization efficiency and poor effect of generated strategies, this paper proposes an MNAEK-PPO (proximal policy optimization based on maximum number of arrival & expert knowledge algorithm) based on curiosity mechanism. Focusing on the difficult problem of exploring the strategy space, by constructing the exploration frequency matrix of the agent in the training process, the exploration frequency is treated as an internal reward to participate in the agent's reinforcement learning and training process. In addition, expert knowledge is added to assist the agent in making decisions. Through experiments in the intelligent battlefield simulation environment, the best construction method of internal rewards in MNAEK-PPO is determined, and a series of comparative experiments are carried out. The experimental results show that MNAEK-PPO greatly improves the exploration efficiency of decision space, and the convergence speed and game score are significantly improved, which provides a new solution for promoting the application and development of deep reinforcement learning in the generation of intelligent tactical strategies. [ABSTRACT FROM AUTHOR]
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- 2023
- Full Text
- View/download PDF
7. Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging
- Author
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Yongshuai Ge, Yang Yongfeng, Changhui Jiang, Zheng Hairong, Hu Zhanli, Zhang Qiyang, Liang Dong, and Liu Xin
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Artifact (error) ,Scanner ,Mean squared error ,Artificial neural network ,Image quality ,business.industry ,Computer science ,Deep learning ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Upsampling ,Data acquisition ,Computer vision ,Artificial intelligence ,business - Abstract
In dental computed tomography (CT) scanning, high-quality images are crucial for oral disease diagnosis and treatment. However, many artifacts, such as metal artifacts, downsampling artifacts and motion artifacts, can degrade the image quality in practice. The main purpose of this article is to reduce motion artifacts. Motion artifacts are caused by the movement of patients during data acquisition during the dental CT scanning process. To remove motion artifacts, the goal of this study was to develop a dental CT motion artifact-correction algorithm based on a deep learning approach. We used dental CT data with motion artifacts reconstructed by conventional filtered back-projection (FBP) as inputs to a deep neural network and used the corresponding high-quality CT data as labeled data during training. We proposed training a generative adversarial network (GAN) with Wasserstein distance and mean squared error (MSE) loss to remove motion artifacts and to obtain high-quality CT dental images. In our network, to improve the generator structure, the generator used a cascaded CNN-Net style network with residual blocks. To the best of our knowledge, this work describes the first deep learning architecture method used with a commercial cone-beam dental CT scanner. We compared the performance of a general GAN and the m-WGAN. The experimental results confirmed that the proposed algorithm effectively removes motion artifacts from dental CT scans. The proposed m-WGAN method resulted in a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and a lower root-mean-squared error (RMSE) than the general GAN method.
- Published
- 2019
8. Stationary Digital Breast Tomosynthesis System that Uses a Carbon Nanotube X-ray Source Array: a Feasibility Study
- Author
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Dong Liang, Jiang Changhui, Shi Wei, Jun Li, Hong Xuda, Zhang Qiyang, Yang Yongfeng, Xin Liu, Hairong Zheng, Hu Zhanli, and Yongshuai Ge
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medicine.diagnostic_test ,Image quality ,business.industry ,Computer science ,Detector ,Response time ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,030220 oncology & carcinogenesis ,Miniaturization ,medicine ,Mammography ,Computer vision ,Tomography ,Artificial intelligence ,Projection (set theory) ,business ,Image resolution - Abstract
Breast cancer is the most common type of cancer in women. Mammography has been a good method of performing early screening to detect breast cancer. An alternative technique, digital breast tomosynthesis (DBT), which is based on limited-angle tomography, solves the overlapping problem associated with mammography but also has limitations. To decrease the limitations of motion artifacts and poor reconstructed image quality that are imposed by the use of a rotating anode with a single X-ray source in conventional DBT, we have developed and demonstrated a stationary DBT (s-DBT) system that uses a carbon nanotube (CNT) X-ray source array. This arc array consists of 9 X-ray-generating focal spots distributed across a 38.7° angle. Compared with a traditional thermal emission X-ray source, a CNT X-ray source has several desirable attributes, such as miniaturization, fast response time, efficient focusing and improved control of emitted electrons; as a result, the tested system can acquire projection images by electronically switching individual X-ray sources on and off conveniently without mechanical motion of either the X-ray source or the detector, leading to improved spatial resolution of acquired images and reduced radiation doses. The tested system was developed in an attempt to find a feasible method to avoid adverse effects and improve image quality relative to conventional DBT systems, and the results reconstructed using a modified form of FDK algorithm indicate the achievement of this objective.
- Published
- 2018
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