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Stroke-GAN Painter:Learning to paint artworks using stroke-style generative adversarial networks

Computational Visual Media 2023年第4期9卷 787-806页

作者： Qian Wang Cai Guo Hong-Ning Dai Ping Li School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina School of Computing and Information Engineering Hanshan Normal UniversityChaozhouChina Department of Computer Science Hong Kong Baptist UniversityHong KongChina School of Design The Hong Kong Polytechnic UniversityHong KongChina

It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke *** advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitations:they(i)lack flexibility to choose different art-style strokes,(ii)lose content details of images,and(iii)generate few artistic styles for *** this paper,we propose a stroke-style generative adversarial network,called Stroke-GAN,to solve the first two ***-GAN learns styles of strokes from different stroke-style datasets,so can produce diverse stroke *** design three players in Stroke-GAN to generate pure-color strokes close to human artists’strokes,thereby improving the quality of painted *** overcome the third limitation,we have devised a neural network named Stroke-GAN Painter,based on Stroke-GAN;it can generate different artistic styles of *** demonstrate that our artful painter can generate various styles of paintings while well-preserving content details(such as details of human faces and building textures)and retaining high fidelity to the input images.

关键词： AI painting painting strokes artistic style

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Research on workflow recognition for liver rupture repair surgery

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Mathematical Biosciences and engineering 2024年第2期21卷 1844-1856页

作者： Men, Yutao Zhao, Zixian Chen, Wei Wu, Hang Zhang, Guang Luo, Feng Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Medical Support Technology Research Department Systems Engineering Institute Academy of Military Sciences People’s Liberation Army Tianjin300161 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons.

关键词： Image classification

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The Innovation Development of Data Prediction and Clustered Compressive Sensing (CCS) in Environmental Application 2

The Innovation Development of Data Prediction and Clustered ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kukreja, Sonia Jain, Garima Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Greater Noida India

ISBN: (纸本)9798350347456

Data prediction in environmental applications is a quickly evolving field that provides tremendous potential for improving environmental management and decision-making. By leveraging the power of data, predictive models can be developed to identify potential environmental risks, mitigate the impacts of climate change, and improve the efficiency of environmental management. Data prediction in environmental applications can be used to better understand the relationships between environmental phenomena and the environment. For instance, predictive models can be used to identify areas that are more likely to experience extreme weather events and inform the development of strategies for responding to such events. Predictive models can also be used to analyze the effects of changing climate on ecosystems and help inform decisions regarding the management of natural resources. Data prediction in environmental applications can also be used to develop more effective management strategies. Predictive models can be used to determine which areas are more likely to experience water scarcity or air pollution, and which areas are more likely to benefit from certain conservation practices. By leveraging data, predictive models can also help predict how certain management practices will affect the environment and inform decision-makers on how to best allocate resources. © 2023 IEEE.

关键词： Decision making

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Multi-physics Coupling Analyses of Nuclear Thermal Propulsion Reactor 23rd

Multi-physics Coupling Analyses of Nuclear Thermal Propulsio...

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23rd Pacific Basin Nuclear Conference, PBNC 2022

作者： Han, Wenbin Guan, Zechuan Huang, Shanfang Deng, Jian Department of Engineering Physics Tsinghua University Beijing China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China China Institute of Atomic Energy Beijing China

ISBN: (纸本)9789819910229

Nuclear thermal propulsion (NTP) reactors have high-temperature solid-state characteristics and significant thermal expansion, which therefore require multi-physics coupling analyses. In this paper, the framework of Neutronics, Thermal-Hydraulics and Mechanics coupling (N/T-H/M) of nuclear thermal propulsion reactor is developed, and the typical reactor XE-2 is analyzed with this method. The results show that the N/T-H/M coupling will bring -1049 pcm negative reactivity, of which the thermal expansion effect accounts for 22%, indicating that the nuclear thermal propulsion reactor has a certain capacity for self-regulation. However, thermal expansion will lead to 0.88 mm peak deformation and 233 MPa peak stress, which will severely threaten the mechanical tolerance of the materials. Therefore, there is a trade-off between the advantages and disadvantages of the high-temperature solid-state core while designing NTP reactors. © 2023, The Author(s).

关键词： Thermal expansion

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Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD)

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BMC Psychiatry 2024年第1期24卷 1-21页

作者： Yuan, Haihang Yang, Tianyi Xie, Qiaolian Lledos, Guilhem Chou, Wen-Huei Yu, Wenwei Department of Medical Engineering Chiba University Chiba Japan Institute of Rehabilitation Engineering and Technology University of Shanghai for Science and Technology Shanghai China UPSSITECH - Paul Sabatier University of Toulouse Toulouse France Department of Digital Media Design National Yunlin University of Science and Technology Yunlin Taiwan Center for Frontier Medical Engineering Chiba University Chiba Japan

With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment. © The Author(s) 2024.

关键词： BPSD models Home monitoring robots Identification of occurrence of BPSD

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Numerical Simulation of Flow Boiling Heat Transfer in Helical Tubes Under Marine Conditions 23rd

Numerical Simulation of Flow Boiling Heat Transfer in Helica...

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23rd Pacific Basin Nuclear Conference, PBNC 2022

作者： Yuan, Leqi Cheng, Kun Bian, Haozhi Liao, Yaping Jiang, Chenxi Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineering University Heilongjiang Harbin China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Sichuan Chengdu China

ISBN: (纸本)9789819910229

Lead-based cooled reactors in most countries and some small reactors at sea use helical tube steam generators. Compared with U-tubes, the convection heat transfer coefficient in the spiral tube is higher, the structure is more compact, and the secondary flow is generated under the action of centrifugal force and gravity, which can achieve the effect of wetting the inner wall of the tube. However, due to the importance of the steam generator in the reactor and the complexity of the flow and boiling in the helical tube, the aggregation behavior of bubbles, the distribution of the two-phase interface and the secondary flow in the tube will significantly affect the heat transfer characteristics, so the gas-liquid phase in the tube is studied. Distribution, changes in heat transfer coefficients, and fluid flow characteristics are very important. In order to study the boiling heat transfer characteristics of helical once-through steam generators under static and marine conditions to provide safe and reliable energy supply for offshore facilities such as marine floating, this study uses STAR-CCM+ software, VOF method and Rohsenow boiling model to study the heat transfer capacity and flow characteristics of flow boiling in a helical tube under swaying and tilting conditions. The gas-liquid phase distribution characteristics, secondary flow variation characteristics and convective heat transfer coefficient of the fluid under different swing functions and inclined positions are obtained by numerical calculation, and the law of physical parameters changing with the cycle is found. The research results show that the secondary flow and heat transfer capacity in the tube change with the cycle, and the change is most obvious at the tube length of 0.8m. 5% of the normal condition;when the inclination angle is 45°, the maximum increase of the convection heat transfer coefficient is 16.8%, and the maximum decrease is 6.6%. © 2023, The Author(s).

关键词： Computational fluid dynamics

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Stock Monitoring and Localization systems for FIFO Method Using RFID Based Pressure Sensing Tag and AI

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2024年第9期144卷 894-900页

作者： Hadi, Danang Kumara Song, Zequn Rahmadya, Budi Sun, Ran Takeda, Shigeki Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan Department of Agroindustrial Technology Universitas Muhammadiyah Jember Jember68124 Indonesia Computer System Department Faculty of Information Technology Andalas University Padang25175 Indonesia

This research introduces real-time monitoring and localizing product stock using the First-In-First-Out (FIFO) method with radio frequency identification (RFID) pressure sensing tags. The proposed FIFO system has RFID pressure sensing tags that detect pressure changes, generate sensor codes, and estimate a stock location. Integrating artificial intelligence (AI) improves location estimation accuracy of up-to-date stock visibility. The ability of RFID pressure sensing tags and AI to detect stock from sensor code data enables more effective asset management, optimizes stock management, reduces product waste, and contributes to better efficiency in product inventory management. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Inventory control

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Evolution of Heuristics: Towards Efficient Automatic Algorithm design Using Large Language Model 41

Evolution of Heuristics: Towards Efficient Automatic Algorit...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Fei Tong, Xialiang Yuan, Mingxuan Lin, Xi Luo, Fu Wang, Zhenkun Lu, Zhichao Zhang, Qingfu Department of Computer Science City University of Hong Kong Hong Kong Huawei Noah's Ark Lab Canada School of System Design and Intelligent Manufacturing Southern University of Science and Technology China

Heuristics are widely used for dealing with complex search and optimization problems. However, manual design of heuristics can be often very labour extensive and requires rich working experience and knowledge. This paper proposes Evolution of Heuristic (EoH), a novel evolutionary paradigm that leverages both Large Language Models (LLMs) and Evolutionary Computation (EC) methods for Automatic Heuristic design (AHD). EoH represents the ideas of heuristics in natural language, termed thoughts. They are then translated into executable codes by LLMs. The evolution of both thoughts and codes in an evolutionary search framework makes it very effective and efficient for generating high-performance heuristics. Experiments on three widely studied combinatorial optimization benchmark problems demonstrate that EoH outperforms commonly used handcrafted heuristics and other recent AHD methods including FunSearch. Particularly, the heuristic produced by EoH with a low computational budget (in terms of the number of queries to LLMs) significantly outperforms widely-used human hand-crafted baseline algorithms for the online bin packing problem. Copyright 2024 by the author(s)

关键词： Budget control

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Deep learning for land use classification: a systematic review of HS-LiDAR imagery

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Artificial Intelligence Review 2025年第9期58卷 1-43页

作者： Rehman, Muhammad Zia Ur Islam, Syed Mohammed Shamsul Blake, David Ulhaq, Anwaar Janjua, Naeem School of Science Edith Cowan University Joondalup Australia School of Engineering and Technology Central Queensland University 2000 400 Kent Street SydneyNSW Australia College of Science and Engineering Flinders University Bedford Park Australia Department of Computing and Information System Daffodil International University Daffodil Smart City Bangladesh

Remote sensing (RS) technologies have significantly advanced Earth observation capabilities, enhancing the characterization and identification of surface materials through both spaceborne and airborne systems. These advancements are crucial for improving environmental monitoring and urban planning. As RS datasets have become more accessible, their increased complexity has necessitated a shift from traditional machine learning techniques to more robust deep learning approaches, particularly convolutional neural networks (CNNs) and transformer-based models known for their superior feature extraction capabilities. This systematic review focuses on the application of these deep learning techniques in land use classification, emphasizing the fusion of hyperspectral (HS) and LiDAR data. It critically examines the transition from traditional methods to advanced deep learning models, details comparative methodologies between different deep learning approaches, and discusses challenges in multimodal data fusion. The review also highlights potential areas for future research that can benefit researchers in developing robust and generalized techniques for land use classification. © The Author(s) 2025.

关键词： Convolutional neural networks

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UWM-Net: A Mixture Density Network Approach with Minimal Dataset Requirements for Underwater Image Enhancement

UWM-Net: A Mixture Density Network Approach with Minimal Dat...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jun Huang Zongze Li Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The learning-based underwater image enhancement, which is suitable for batch processing, is a pivotal research direction in underwater image processing. Extensive paired image data are required in existing learning-based methods, which necessitate considerable preprocessing and hinder the application of these methods. To address these limitations, we propose a semi-supervised approach called UWM-Net: firstly, we use a compact dataset of underwater image pairs to train the Mixture Density Network (MDN) with an underwater scene setting; subsequently, U-Net can learn underwater image enhancement more efficiently. The MDN can transform standard images into underwater scenes, reducing the reliance on paired data and making much smaller training datasets. In experimental studies, UWM-Net using only 18 pairs of underwater image data achieves highly competitive results in terms of 3 metrics compared with advanced models.

关键词： Measurement Training Learning systems Image quality Image color analysis Refining Training data

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