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Enhancing the performance of CaTiO3-GDC heterostructure electrolytes in LT-CFCs

Ceramics International 2025年第15期51卷 20131-20142页

作者： Li, Dongchen Cui, Yingying Hong, Xiaofang Shah, M.A.K. Yousaf Mushtaq, Naveed Rauf, Sajid Arshad, Naila Irshad, Sultan Alodhayb, Abdullah N. Yousaf, Muhammad Lu, Yuzheng Department of Electrical and Automation Shandong Labor Vocational and Technical College Jinan250300 China Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Joint Research Center School of Energy and Environment Southeast University No.2 Si Pai Lou Nanjing210096 China College of Mechatronics and Control Engineering Shenzhen University Guangdong Province 518000 China School of Physics Electronics and Intelligent Manufacturing Huaihua University Hunan Huaihua418008 China College of Electronics and Information Engineering Shenzhen University Guangdong Province 518000 China Department of Physics and Astronomy College of Science King Saud University Riyadh11451 Saudi Arabia College of Chemistry and Environmental Engineering Shenzhen University Guangdong Province Shenzhen518055 China School of Electronic Engineering Nanjing Xiaozhuang University Nanjing211171 China

Designing ceramic fuel cells (CFCs) with fast ionic conduction and superior electrochemical performance at low temperatures is critical for advancing hydrogen applications. Here, we developed a heterostructure electrolyte by combining the ionic conductor Gd0.1Ce0.9O2 (GDC) with the semiconductor material CaTiO3 (CTO) to shape into the composite heterostructure (semiconductor ionic heterostructure) CTO-GDC. The designed heterostructure with different fermi levels established the depletion zone due to the redistribution of charges at the interface, leading to enhancement of the ionic conduction;meanwhile, the established built-in electric field suppresses the electronic conduction speed of the ion's transportation at the interface of the constructed heterostructure (CTO-GDC). The formation of heterostructure introduces a number of oxygen vacancies, as confirmed via x-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR), benefiting the transportation of ions in the composite heterostructure. The prepared composite heterostructure shows the highest power output of 810 mW/cm2 at a low operational temperature of 520 °C. The CTO-GDC shows high ionic conductivity of 0.17 S cm−1 at 520 °C and low activation energy of 0.785 eV at 520-420 °C. The composite containing a 50 % molar ratio of GDC and CTO demonstrated exceptional performance by successfully inhibiting electron transit, improving ionic conductivity, and achieving outstanding power output. The enhanced ionic conductivity with low activation energy of composite heterostructure suggests that designing a heterostructure seems an efficient approach to fuel cell application. © 2025

关键词： Paramagnetic resonance

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Semi-supervised Learning with GAN for Semantic Segmentation of Wood Defect Image

Semi-supervised Learning with GAN for Semantic Segmentation ...

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Chinese automation Congress (CAC)

作者： Kunxin Zheng Song Zheng Zhezhuang Xu Ye Lin Yuhang Zhu Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct pixel-level labels of wood surface defects due to the complicated characteristics. To mitigate the cost of acquiring high-quality datasets, this study proposes a semi-supervised defect detection approach based on generative adversarial networks, which contains a generator and a discriminator. The generator is utilized to generate pixel-level segmentation results of wood defect images. To improve the segmentation performance, the discriminator learns against the generator by judging the goodness of the prediction results and generates supervised signals for the prediction results of unlabeled images. With this learning model, the proposed method can enhance segmentation performance using unlabeled images and alleviate the burden of data annotation. We validate the effectiveness of the proposed approach on a collected dataset of wood defects. Experimental results demonstrate the remarkable performance of our method even when utilizing only a small amount of annotated data.

关键词： Semantic segmentation Semisupervised learning Generative adversarial networks Generators Robustness Product design Data models Quality assessment Data mining Defect detection

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A VIO-Based Localization Approach in GPS-denied Environments for an Unmanned Surface Vehicle 8

A VIO-Based Localization Approach in GPS-denied Environments...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Liu, Xinze Hu, Zhihuan Sun, Zhijian Lu, Jinhui Xie, Wei Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China Crossocean of Suzhou Technology No. 218 East Qingdao Road Suzhou China National Engineering Research Center of Ship & Shipping Control System

ISBN: (纸本)9798350300178

In this paper, we propose a visual inertial odometer (VIO)-based localization approach for USV in GPS-denied environments. Considering the difficulty in capturing reliable features on the water surface, we set the camera's pitch angle properly to capture high-quality features, including points and lines, distributed on the bridge wall. Our work is based on the state-of-the-art simultaneous localization and mapping (SLAM) framework, VINS-Fusion. The point and line features observed by the left and right cameras are both added into the nonlinear optimization system along with the inertial measurement unit (IMU) measurement between frames. Through the joint optimization of landmark information and system state variables, the precise positioning of USV is realized. To validate the effectiveness of the proposed localization method, experimental results are presented and analyzed. © 2023 IEEE.

关键词： Cameras

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Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm

Optimization of Crane Scheduling Time for Steel Plate Outbou...

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2024 China automation Congress, CAC 2024

作者： Xiang, Jiang Xu, Zhezhuang Wang, Jinlong Chen, Boyu Zhang, Qingdong Wu, Xiuren Xia, Yuxiong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming365000 China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798350368604

Steel plate is a highly customized product, and steel plates from the same order may be stored in different stacks. However, during the outbound process, the steel plates of the next order can only be dispatched from the warehouse once all plates from the preceding order have been fully completed. In this case, the outbound scheduling of steel plates is a challenging problem since its efficiency and complexity are affected by the shuffling of steel plates. In order to address the challenge, this paper formulates the steel plate outbound scheduling as a dynamic programming model with the aim of minimizing the crane operation time. To solve the problem, an decision tree-based backtracking search algorithm (DTBSA) is developed, in which the search tree is pruned through lower bound functions and dominance rules, so as to reduce the search range of the solution space. Experimental results prove that the proposed method can obtain optimal scheduling results within a reasonable time range. © 2024 IEEE.

关键词： Dynamic programming

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PID control of data-driven patient response with fixed ratio co-administration of drugs for depth of hypnosis

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IFAC-PapersOnLine 2024年第7期58卷 222-227页

作者： Clara M. Ionescu Hamed Farbakhsh Cristina Muresan Robin De Keyser Erhan Yumuk Ghada Ben Othman Amani Rayene Ynineb Isabela Birs Dana Copot Martine Neckebroek Ghent University Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 9052 Zwijnaarde Belgium Technical University of Cluj-Napoca Department of Automation Memorandumului no. 28 Cluj-Napoca 400114 Romania Istanbul Technical University Department of Control and Automation Engineering Systems and Control Istanbul Turkey Ghent University Hospital Department of Anesthesia C. Heymanslaan 10 9000 Ghent Belgium

In this work we make use of knowledge from clinical practice to deliver a trusted environment for feedback control of co-administration of two drugs to induce depth of hypnosis during general anesthesia. The clinical data we have provides valuable insight into the ratio used to determine the Propofol and Remifentanil infusion rates by means of specifying their corresponding effect site concentrations. A feedback closed loop with a fixed parameter PID controller tuned on population dynamics is used. To account the strong variations in gain of the system (patient) we propose an online identification of the dose-effect response from clinical data available during clinical protocol. Theoretical analysis is provided to justify the approach and simulations with real data from patient are given to support the theoretical insight. Limitations of this approach are discussed as to justify why ratio based co-administration is used in practice solely during the induction phase of general anesthesia.

关键词： Closed-Loop control of Anesthesia PID control Depth of Hypnosis Ratio control Model Estimation Data Driven control

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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Online Vehicle Forensics Method of Responsible Party for Accidents Based on LSTM-BiDBN External Intrusion Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 1161-1168页

作者： LIU Wen XU Jianxin YANG Genke CHEN Yuanfang Ningbo Industrial Internet Institute Ningbo 315000ZhejiangChina Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University Ningbo 315000ZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China School of Cyberspace Hangzhou Dianzi UniversityHangzhou 310018China

Vehicle data is one of the important sources of traffic accident digital *** propose a novel method using long short-term memory-deep belief network by binary encoding(LSTM-BiDBN)controller area network identifier(CAN ID)to extract the event sequence of CAN IDs and the semantic of CAN IDs *** of detecting attacks only aimed at a specific CAN ID,the proposed method fully considers the potential interaction between electronic control *** this means,we can detect whether the vehicle has been invaded by the outside,to online determine the responsible party of the *** use our LSTM-BiDBN to distinguish attack-free and abnormal situations on *** results show that our proposed method is more effective in identifying anomalies caused by denial of service attack,fuzzy attack and impersonation attack with an accuracy value of 97.02%,a false-positive rate of 6.09%,and a false-negative rate of 1.94%compared with traditional methods.

关键词： digital forensics deep belief network(DBN) long short-term memory(LSTM) binary encoding controller area network identifier(CAN ID) responsible party

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A Survey Research on the Fiber Optic Demand in the Philippines Using CART Analysis with Predictive ANN Model Creation

A Survey Research on the Fiber Optic Demand in the Philippin...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Lazaga, Chrisjonpane F. Tan, Gerhard P. Rosales, Marife A. Graduate School Polytechnic University of the Philippines Master of Sci. in Electronics Eng. with Specialization in Artificial Intelligence and Automation Manila Philippines Graduate School Polytechnic University of the Philippines Department of Electronics Engineering Manila Philippines De la Salle University Manila Philippines

ISBN: (纸本)9798350310689

This study provides help to the department of Information and Communication Technology with statistical data and analysis on the ICT's SDG plans that are ramping up in the Philippines. The researchers use special-purpose surveys to produce an Internal-External Matrix in determining DICT's strategy in terms of strengths, weakness, opportunities, and threats in an environment where fiber optic connectivity is available. CART and ANN models are used for analyzing and predicting the individual's behaviors in taking fiber optic internet as their selected internet mode. The IE Matrix showed that the current strategies benefit and produce results, but not enough to make their organization known to the respondents. The CART and ANN models both resulted in approximately 86% of prediction accuracy aligned that the CART model's top 3 feature importance results are;Certification Platform, Internet in Public Sector, and Professional Status, while the ANN model showed similar on CART results having the latter;Age, Incentive Motivation, and Salary of an individual are deemed integral in the created predicting model. The researcher hopes that this study will provide insight and solutions in improving the strategy, plans and market penetration in the future where fiber optic internet is widely available in the Philippines. © 2023 IEEE.

关键词： Forecasting

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Double layer scheduling mechanism in Virtual power plant

Double layer scheduling mechanism in Virtual power plant

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Cui, Huahu Zhang, Qian Wang, Daxin Zhang, Weishi Qi, Hui Fan, Lei Anhui University School of Electrical Engineering and Automation Anhui Key Laboratory of Industrial Energy-Saving and Safety Hefei China Ministry of Education Anhui University School of Electrical Engineering and Automation Engineering Research Center of Power Quality Hefei China Anhui Power Exchange Center Co. Ltd Transaction Department Hefei China Anhui University Anhui Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control Hefei China

ISBN: (纸本)9798350332308

Aiming at the problems of operation economy and stability of power systems with a high proportion of renewable energy, this paper proposes a two-layer optimal scheduling model based on internal trading of the VPP (Virtual power plant) energy layer. Firstly, the users participating in the smart contract and the VPP within the region are regarded as different interest subjects. And the maximum opportunity benefit of users is taken as the internal optimization goal. Secondly, taking the overall benefit maximization of the VPP as the overall optimization objective,. Thus, a two-layer optimal scheduling model including energy storage is established, and the multi-objective particle swarm optimization algorithm was applied to solve the problem. The local consumption of new energy is taken as the criterion, and the price of some local consumption energy is determined by the bidding mechanism of producers and sellers. Finally, the thermal power units are used as auxiliary units. In the two scenarios with or without energy storage, comparative tests are made on the internal trading of the energy layer of VPPs. The optimal scheduling results show that the internal trading of the energy layer of VPP contributes to the local consumption of new energy and improves the overall internal income level of VPP. © 2023 IEEE.

关键词： Virtual Power Plants

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