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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年 332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Study of Helicopter Optimal Autorotation Landing Procedure in Tail Rotor Drive Failure

Study of Helicopter Optimal Autorotation Landing Procedure i...

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IEEE International Conference on Robotics and Biomimetics

作者： Xufei Yan Renliang Chen Shiqiang Zhu Anhuan Xie Jason Gu Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics College of Aeronautics Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Intelligent Robot Research Center Zhejiang Lab Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

This paper investigates the optimal landing trajectory and control procedure when a helicopter undergoes autorotation due to tail rotor drive failure (TRDF), in which an optimal control methodology is proposed. First, a helicopter flight dynamics model with TRDF was developed. Then, the autorotation in TRDF was converted to be a nonlinear optimal control problem, solved by direct node collocation method and sequential quadratic programming algorithm. Finally, a model helicopter (Z11) with single main rotor and tail rotor was used to demonstrate the proposed approach. An optimal autorotation landing procedure in TRDF was determined accordingly. Results indicate that the airframe will immediately respond to the excess torque generated by the main rotor via yawing, sideslip and rolling when TRDF occurs. The pilot is recommended to shut down the engine and perform a series of critical operations to stabilize the violent yaw and roll movements. In addition, flight test data were used to validate the numerical simulations of autorotation landing. The proposed optimal control approach provides a useful tool to investigate helicopter TRDF autorotation landing procedure.

关键词： Torque Biological system modeling Helicopters Rotors Optimal control Tail Numerical simulation

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Personalized Heart Disease Detection via ECG Digital Twin Generation

arXiv

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arXiv 2024年

作者： Hu, Yaojun Chen, Jintai Hu, Lianting Li, Dantong Yan, Jiahuan Ying, Haochao Liang, Huiying Wu, Jian College of Computer Science and Technology Zhejiang University China Computer Science Department University of Illinois at Urbana-Champaign United States Medical Big Data Center Guangdong Provincial People’s Hospital Southern Medical University China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China School of Public Health Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://***/huyjj/LAVQ-Editor. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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Remote Spin-Center Shift Enables Activation of Distal Benzylic C─O and C─N Bonds

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Angewandte Chemie 2025年第24期137卷

作者： Li-Wen Hui Yee Lin Phang Chen-Yang Ye Jin-Yu Lai Feng-Lian Zhang Yao Fu Yi-Feng Wang State Key Laboratory of Precision and Intelligent Chemistry Anhui Provincial Key Laboratory of Biomass Chemistry Department of Chemistry University of Science and Technology of China 96 Jinzhai Road Hefei Anhui 230026 China Institute of Advanced Technology University of Science and Technology of China 96 Jinzhai Road Hefei Anhui 230026 China

A spin-center shift (SCS) is a radical process that commonly involves a 1,2-radical shift along with the elimination of an adjacent leaving group by a two-electron ionic movement. The conventional SCS process is largely limited to 1,2-radical translocation, while a remote SCS event involving 1, n -radical translocation over a greater distance to enable distal bond functionalization remains largely underexplored. Herein, we report the boryl radical-promoted distal deoxygenation and deamination of free benzylic alcohols and simple benzylic amines, respectively, through a remote SCS event. The reaction was initiated by the addition of a 4-dimethylaminopyridine (DMAP)-boryl radical to the carbonyl oxygen atom of a benzoate or benzamide. Then, radical translocation took place across the aromatic ring to promote benzylic C─O or C─N bond cleavage. The resulting radical intermediate subsequently coupled with various alkenes to afford a wide range of alkylated products. The proposed mechanistic pathway was supported by experimental investigations.

关键词： Boryl radicals Radical deamination Radical deoxygenation Remote functionalization Spin center shift

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Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy

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Advanced Photonics Nexus 2023年第1期2卷 109-117页

作者： Jianyong Wang Junchao Fan Bo Zhou Xiaoshuai Huang Liangyi Chen Peking University Institute of Molecular MedicineCollege of Future TechnologyCenter for Life SciencesState Key Laboratory of Membrane BiologyBeijing Key Laboratory of Cardiometabolic Molecular MedicineBeijingChina Peking University School of Software and MicroelectronicsBeijingChina Chongqing University of Posts and Telecommunications College of Computer Science and TechnologyChongqing Key Laboratory of Image CognitionChongqingChina Peking University Biomedical Engineering DepartmentBeijingChina Peking University International Cancer InstituteBeijingChina PKU-IDG/McGovern Institute for Brain Research BeijingChina Beijing Academy of Artificial Intelligence BeijingChina National Biomedical Imaging Center BeijingChina

Structured illumination microscopy(SIM)has been widely used in live-cell superresolution(SR)***,conventional physical model-based SIM SR reconstruction algorithms are prone to artifacts in handling raw images with low signal-to-noise ratios(SNRs).Deep-learning(DL)-based methods can address this challenge but may lead to degradation and *** combining the physical inversion model with a total deep variation(TDV)regularization,we propose a hybrid restoration method(TDV-SIM)that outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining *** demonstrate the performance superiority of TDV-SIM in restoring actin filaments,endoplasmic reticulum,and mitochondrial cristae from extremely low SNR raw *** TDV-SIM represents the ideal method for prolonged live-cell SR imaging with minimal exposure and ***,TDV-SIM proves the power of integrating model-based reconstruction methods with DL ones,possibly leading to the rapid exploration of similar strategies in high-fidelity reconstructions of other microscopy methods.

关键词： structured illumination microscopy superresolution reconstruction deep learning

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Optimizing AoI in RIS-aided Wireless Powered Backscatter-Active Communication Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Hu, Huimin Zhang, Ruichen Ding, Zhiguo Jiang, Fan Jiang, Ruihong Niyato, Dusit Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China Nanyang Technological University College of Computing and Data Science Singapore Singapore Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we investigate the average age of information (AoI) in wireless powered backscatter-active communication networks (WPBACN) assisted by a reconfigurable intelligent surface (RIS). To minimize the average AoI, we jointly optimize the phase shift of the RIS, the backscatter-active communication time allocation factor, and the transmitted power of IoT devices. Since the optimization problem is composed of coupled variables, the optimization variables are implicit in the objective function and the problem is non-convex, which makes it challenging to solve. To address this challenge, we decompose the problem into an RIS phase shift optimization subproblem and a joint power and time allocation subproblem. Simulation results indicate that the proposed scheme has a significant performance advantage in reducing the average AoI compared to the scheme using random RIS phase shifts and the scheme without RIS and supports the challenging quality of service required by real-time information update applications. © 1967-2012 IEEE.

关键词： age of information Reconfigurable intelligent surface WPBACN

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Is out-of-distribution detection learnable? 22

Is out-of-distribution detection learnable?

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Zhen Fang Yixuan Li Jie Lu Jiahua Dong Bo Han Feng Liu Australian Artificial Intelligence Institute University of Technology Sydney Department of Computer Sciences University of Wisconsin-Madison State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences and ETH Zurich Switzerland Department of Computer Science Hong Kong Baptist University Australian Artificial Intelligence Institute University of Technology Sydney and School of Mathematics and Statistics University of Melbourne

ISBN: (纸本)9781713871088

Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability and diversity of OOD data, good generalization ability is crucial for effective OOD detection algorithms. To study the generalization of OOD detection, in this paper, we investigate the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem. First, we find a necessary condition for the learnability of OOD detection. Then, using this condition, we prove several impossibility theorems for the learnability of OOD detection under some scenarios. Although the impossibility theorems are frustrating, we find that some conditions of these impossibility theorems may not hold in some practical scenarios. Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios. Lastly, we also offer theoretical supports for several representative OOD detection works based on our OOD theory.

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Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

arXiv

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arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Reconfigurable intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization

arXiv

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arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

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A hybrid model for post-treatment mortality rate classification of patients with breast cancer

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Healthcare Analytics 2023年 4卷 100254-100254页

作者： Folorunso, Sakinat Oluwabukonla Awotunde, Joseph Bamidele Adigun, Adepeju Abeke Prasad, L.V. Narasimha Lalitha, Vuyyuru Lakshmi Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago-Iwoye Nigeria Artificial Intelligent Systems Research Group (ArISRG) Department of Mathematical Sciences Olabisi Onabanjo University Ago-iwoye Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin 240003 Nigeria Department of Information and Communication Technology Osun State University Osogbo Nigeria Department of CSE Institute of Aeronautical Engineering Hyderabad India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation India

Terminal cancer is not curable and eventually results in death. Breast cancer (BC) is a prevalent malignancy affecting women. Although there are prognostic indicators, BC prognosis is still challenging because of the intricate connections between various survival factors and influencing factors. This study proposes an ensemble classifier for predicting BC survivability using a new BC post-treatment dataset for the number of survivals. However, the classes survival cases dataset for BC is skewed, which caused a sub-optimal classification performance. Hence, a hybrid sampling scheme of Synthetic Minority Over-Sampling TEchnique (SMOTE) and Wilson's Edited Nearest Neighbor (ENN) is employed to treat the class imbalance in the dataset. Random Forest (RF) ensemble classifier is for classifying the dataset. The proposed framework performs well in terms of accuracy, recall of the two classes, Receiver Operating Characteristics (ROC) and Kappa Statistic (KS) metric on the dataset. The results demonstrated that the RF, with 97.0% accuracy on the holdout sample, is the best predictor. This prediction accuracy is superior to any noted in the literature, compared with Logistic Regression (LR) and Bagging classifiers. © 2023 The Authors

关键词： Breast cancer Class imbalance Ensemble learners Machine learning Random forest Sampling

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