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超算与机器学习辅助高渗透性海水反渗透膜系统优化设计

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Science Bulletin 2023年第4期68卷 397-407,M0004页

作者：罗玖李明恒 Eric M.V.Hoek 衡益 School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China National Supercomputing Center in Guangzhou(NSCC-GZ) Guangzhou 510006China Guangdong Province Key Laboratory of Computational Science Guangzhou 510006China Department of Chemical and Materials Engineering California State Polytechnic UniversityPomona CA 91768USA Department of Civil&Environmental Engineering California NanoSystems Institute and Institute of the Environment&SustainabilityUniversity of CaliforniaLos Angeles(UCLA)Los Angeles CA 90095USA Energy Storage&Distributed Resources Division Lawrence Berkeley National LaboratoryBerkeley CA 94720USA

当前,高渗透性反渗透膜材料的研究引起了广泛的关注,然而高渗透导致的浓差极化与膜污染加剧等瓶颈问题限制了高性能膜材料的应用发展.本工作采用机器学习结合超级计算提出了针对先进反渗透膜材料的组件进水隔网(亚毫米级)与系统(米级)... 详细信息

当前,高渗透性反渗透膜材料的研究引起了广泛的关注,然而高渗透导致的浓差极化与膜污染加剧等瓶颈问题限制了高性能膜材料的应用发展.本工作采用机器学习结合超级计算提出了针对先进反渗透膜材料的组件进水隔网(亚毫米级)与系统(米级)的多尺度优化设计新方法.在进料含盐度35,000 ppm,回收率50%典型工况下,对标目前国际先进海水反渗透淡化工艺,本文提出的优化方案能使淡水制备比能耗(1.66 k Wh/m^(3))降低27.5%,所需膜面积减少约37.2%,系统最大浓差极化因子控制在工程允许范围以内(<1.20),可有效缓解高渗透膜系统中膜污染问题,为高性能膜材料精准设计提供理论依据、计算工具和大数据支撑,有重要的应用潜力.本文提出的机器学习结合超算的多尺度设计新研究范式,突破了基于“试错法”的传统单一尺度组件设计限制,高通量并行计算规模可扩展至93,120核以上,较串行算法计算效率提升3000倍以上,可大幅度缩短高性能膜组件的设计周期.

关键词： Reverse osmosis desalination High permeability membrane Multiscale design optimization Multilayer artificial neural networks Supercomputing Enhancement of mass transfer

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

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arXiv 2022年

作者： Yin, Huan Lin, Zhiyi Yeoh, Justin K.W. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Civil and Environmental Engineering National University of Singapore Singapore Innovation Research Institute Co. Ltd Hangzhou China

Conventional sensor-based localization relies on high-precision maps, which are generally built using specialized mapping techniques involving high labor and computational costs. In the architectural, engineering and construction industry, Building Information Models (BIM) are available and can provide informative descriptions of environments. This paper explores an effective way to localize a mobile 3D LiDAR sensor on BIM-generated maps considering both geometric and semantic properties. First, original BIM elements are converted to semantically augmented point cloud maps using categories and locations. After that, a coarse-to-fine semantic localization is performed to align laser points to the map based on iterative closest point registration. The experimental results show that the semantic localization can track the pose successfully with only one LiDAR sensor, thus demonstrating the feasibility of the proposed mapping-free localization framework. The results also show that using semantic information can help reduce localization errors on BIM-generated maps. © 2022, CC BY-NC-ND.

关键词： Semantics

A Look at The 3GPP Standard’s Current Positioning of 5G Networks

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A Look at The 3GPP Standard’s Current Positioning of 5G Net...

Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

作者： Mohammed S. Khalaf Aeizaal Azman A. Wahab Haider A Al-Wzwazy Ahmed Salih Al-Khaleefa Murtadha A. Alazzawi Mohammed Almohamadi School of Electrical & Electronic Engineering Universiti Sains Malaysia Pulau Pinang Malaysia Halfaya Oilfield Division Maysan Iraq Department of Physics Faculty of Education University of Misan Maysan Iraq Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

Here, we depict the ongoing 3GPP Delivery 16 particularly for situating in 5G organizations, which is currently being developed. To address the issues of an extensive variety of administrative, business, and modern use cases, the detail indicates situating signals, estimations, methods, and framework engineering. Accordingly, when contrasted with what was beforehand conceivable with LTE, 5G essentially advances situating capacities. Utilizing 3GPP reenactment situations that have been settled upon, the demonstrative situating execution is assessed, and it is found that the 90 percentile precision goes from a couple of meters to a couple of decimeters, contingent upon the procedures and suppositions utilized.

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LLM Assistance for Pediatric Depression

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arXiv 2025年

作者： Ignashina, Mariia Bondaronek, Paulina Santel, Dan Pestian, John Ive, Julia University College London Institute of Health Informatics United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center University of Cincinnati CincinnatiOH United States

Introduction: Depression is a complex mental health condition, particularly prevalent among young people aged 10–24, a group experiencing a sharp rise in cases. Traditional screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical limitations. AI has potential to help but the scarcity of annotated datasets in mental health, combined with the computational costs of training, highlights the need for efficient, zero-shot approaches. Large Language Models (LLMs) offer a promising, computationally affordable zero-shot solution by extracting relevant text segments from electronic patient notes to support clinicians in identifying depressive symptoms, as well as for downstream AI. In this work, we investigate the feasibility of state-of-the-art LLMs for depressive symptom extraction in pediatric settings (ages 6–24). This approach aims to complement traditional screening and minimize diagnostic errors. Methods: We examined free text of the EHRs of pediatric patients with the diagnosis of depression or related mood disorders (age groups 6-24, 1.8K patients) from at the Cincinnati Children’s Hospital Medical Center. We noticed drastic inconsistencies in the application and documentation of PHQ-9 screening highlighting the difficulty in obtaining comprehensive diagnostic data in these conditions. We manually annotated notes for 22 patients with 16 depression-related symptom categories. We leveraged the combination of Beck’s Depression Inventory (BDI) and the PHQ-9 to develop tailored categories specifically suited for pediatric depression symptoms. We then applied three state-of-the-art Large Language Models (LLMs) (FLAN T5, Llama and Phi) to automate the identification of these symptom categories. Results: Our findings show that all LLMs are 60% more efficient than word match with Flan leading in precision (average F1: 0.65, precision: 0.78), excelling in the extraction of more rare symptoms like "sleep problem

关键词： Pediatrics

Fairness Enhancement of UAV Systems with Hybrid Active-Passive RIS

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arXiv 2023年

作者： Nguyen, Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu Van Wu, Qingqing Tölli, Antti Chatzinotas, Symeon Juntti, Markku Wireless Communications University of Oulu P.O.Box 4500 FI-90014 Finland The College of Engineering and Computer Science The Center for Environmental Intelligence VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam The Faculty of Electronic and Telecommunication Engineering The University of Danang University of Science and Technology Da Nang50000 Viet Nam The Department of Electronic Engineering Shanghai Jiao Tong University 200240 China University of Luxembourg L-1855 Luxembourg

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% − 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements. © 2023, CC BY.

关键词： Beamforming

Study on the Intelligent Control Model of a Greenhouse Flower Growing Environment

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Study on the Intelligent Control Model of a Greenhouse Flowe...

作者： Jinyang Zhen Rui Xu Jian Li Shiming Shen Jianhui Wen School of Computer Science and Information Security Guilin University of Electronic Technology Satellite Navigation Positioning and Location Service Engineering Research Center Guilin University of Electronic Technology Ecological and Environmental Monitoring Center of Guangxi

Intelligent control of the greenhouse planting environment plays an important role in improving planting efficiency and guaranteeing the quality of precious flowers. Among them, how to adapt the air humidity, temperature and light intensity in greenhouses to the different needs of the flower growth cycle is the key problem of intelligent control. Therefore, an intelligent flower planting environment monitoring and control system model(named) based on the Internet of Things and fuzzy-GRU network adaptive learning is proposed. The above three parameters in the greenhouse were used as model input parameters. The optimal growth humidity, temperature and illumination intensity of flowers are determined by the model, and the output temperature, humidity and illumination intensity act on the executing organ of the greenhouse room by the single-chip *** model was evaluated using field greenhouse crops. The results show that the performance of this model is better than that of the PID model and fuzzy control model in simulation experiments and actual scene control. Compared with the flowers in the natural state, the plants of the flowers under systematic control were approximately 6 cm higher than those in the natural state on average, the blooming time of the flowers was approximately two days longer than that in the natural state, and the quality of the flowers was stable.

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Risk-Averse Certification of Bayesian Neural Networks

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arXiv 2024年

作者： Zhang, Xiyue Wang, Zifan Gao, Yulong Romao, Licio Abate, Alessandro Kwiatkowska, Marta Department of Computer Science University of Oxford United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Wind and Energy Systems Technical University of Denmark Denmark

In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In this work, we propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN. Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN, represented using a set of template polytopes. To enhance robustness evaluation, we integrate a coherent distortion risk measure—Conditional Value at Risk (CVaR)—into the certification framework, providing probabilistic guarantees based on empirical distributions obtained through sampling. We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method. The results show that RAC-BNN effectively quantifies robustness under worst-performing risky scenarios, and achieves tighter certified bounds and higher efficiency in complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

IPMN Risk Assessment Under Federated Learning Paradigm 22

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IPMN Risk Assessment Under Federated Learning Paradigm

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Pan, Hongyi Hong, Ziliang Durak, Gorkem Keles, Elif Aktas, Halil Ertugrul Taktak, Yavuz Medetalibeyoglu, Alpay Zhang, Zheyuan Velichko, Yury Spampinato, Concetto Schoots, Ivo Bruno, Marco J. Tiwari, Pallavi Bolan, Candice Gonda, Tamas Miller, Frank Keswani, Rajesh N. Wallace, Michael B. Xu, Ziyue Bagci, Ulas Northwestern University Department of Radiology ChicagoIL United States Istanbul University Istanbul Faculty of Medicine Department of Radiology Istanbul Turkey Istanbul University Istanbul Faculty of Medicine Department of Internal Medicine Istanbul Turkey University of Catania Department of Electrical Electronic and Computer Engineering Catania Italy Erasmus Medical Center Department of Radiology and Nuclear Medicine Rotterdam Netherlands Erasmus Medical Center Department of Gastroenterology and Hepatology Rotterdam Netherlands University of Wisconsin Department of Biomedical Engineering and Radiology MadisonWI United States Mayo Clinic Division of Gastroenterology and Hepatology JacksonvilleFL United States New York University Division of Gastroenterology and Hepatology New YorkNY United States Northwestern University Department of Gastroenterology and Hepatology ChicagoIL United States Nvidia BethesdaMD United States

ISBN: (纸本)9798331520526

Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers. © 2025 IEEE.

关键词： Federated learning

Energy Minimization in STAR-RIS Assisted UAV Enabled SWIPT Systems with FHB Protocol

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Energy Minimization in STAR-RIS Assisted UAV Enabled SWIPT S...

IEEE International Conference on Communications (ICC)

作者： Ji Wang Lijuan Qin Wenwu Xie Xingwang Li Shouyin Liu Thippa Reddy Gadekallu Gautam Srivastava Department of Electronics and Information Engineering Central China Normal University Wuhan China School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India Haiyan County Zhongda Group Jiaxing City Zhejiang Province China Department of Mathematics and Computer Science Brandon University Brandon Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper investigates how to improve the energy efficiency of unmanned aerial vehicle (UAV)-enabled simultane-ous wireless information and power transfer (SWIPT) systems with multiple outdoor energy receivers (ERs) and multiple indoor wired-charging information receivers (IRs) by utilizing simul-taneously transmitting and reflecting reconfigurable intelligence (STAR-RIS), in which the UAV avoids flying over the indoor no-fly zone. Specifically, the total UAV energy consumption is minimized, while ensuring that the energy harvesting require-ment (EHR) of each ER and the communication throughput requirement (CTR) of each IR are met. To achieve this, the total UAV energy consumption is minimized by optimizing the STAR-RIS phase-shifts, the UAV trajectory, and hovering time using an iterative technique based on the fly-hover-broadcast (FHB) protocol. The technique allows the UAV to radiate energy-carrying information signals for the ERs and IRs at a limited number of hover positions. Simulation results demonstrate that the proposed design significantly outperforms other benchmark schemes, demonstrating its potential for improving the energy efficiency of UAV-enabled SWIPT systems while meeting the EHRs of each ER and the CTR of each IR.

关键词： Wireless communication Energy consumption Protocols Simulation Receivers Autonomous aerial vehicles Throughput