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Flexible temperature sensor with high sensitivity ranging from liquid nitrogen temperature to 1200℃

International Journal of Extreme Manufacturing 2023年第1期5卷 355-366页

作者： Zhaojun Liu Bian Tian Zhuangde Jiang Shuimin Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Peng Shi Qijing Lin State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049People’s Republic of China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore

Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic ***,most of the current flexible temperature sensors only work well in a narrow temperature range,with their application at high or low temperatures still being a big *** work proposes a flexible thermocouple temperature sensor based on aerogel blanket substrate,the temperature-sensitive layer of which uses the screen-printing technology to prepare indium oxide and indium tin *** has good temperature sensitivity,with the test sensitivity reaching 226.7μV℃^(−1).Most importantly,it can work in a wide temperature range,from extremely low temperatures down to liquid nitrogen temperature to high temperatures up to 1200℃,which is difficult to be achieved by other existing flexible temperature *** temperature sensor has huge application potential in biomedicine,aerospace and other fields.

关键词： flexible sensor temperature thermocouple

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Research on Methods of Decoupling Current and Friction in Electromagnetic Launching 10

Research on Methods of Decoupling Current and Friction in El...

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10th Euro-Asian Pulsed power Conference, 25th International Conference on High-power Particle Beams and 20th International Symposium on Electromagnetic Launch Technology, EAPPC-BEAMS-EML 2024

作者： Xu, Weidong Wang, Jiong Ye, Wenyi Wang, Zengji Xu, Rong Cao, Yiting Cheng, Wenping Yan, Ping Chinese Academy of Sciences Key Laboratory of High Density Electromagnetic Power and Systems Institute of Electrical Engineering Beijing China

ISBN: (纸本)9789038661353

Electromagnetic rail launch is an effective way to achieve high velocity. Multiple physical fields such as electricity, magnetism, heat and force are coupled during the launching. The wear of the armature is mainly caused by the joule heat generated by current heating and the frictional heat. Electromagnetic force is mainly determined by the magnitude of the current, so joule heat and frictional heat are two factors that are difficult to separate. It means that the proportion of frictional heat and Joule heat to armature wear cannot be distinguished, which has a certain impact on armature design. The electromagnetic launch device includes simple type and augmented type. The inductance gradient of the simple device is about 0.45uH/m, and the inductance gradient of the augmented device is about 0.9uH/m. According to the electromagnetic force formula, it can be inferred that when the same current is passed through, the electromagnetic force acting on the armature in the two types of launchers differs by half. By controlling the magnitude of the current and the mass of the projectile, it is possible to achieve the same amount of electromagnetic force and the same speed curve on both devices, and obtain experimental comparative analysis of the same amount of friction force. Friction and current can be compared and analyzed by this method. © 2024 Eindhoven University of Technology.

关键词： Inductance

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Small Object Detection Without Attention for Aerial Surveillance 13th

Small Object Detection Without Attention for Aerial Surveil...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Choi, Jehwan Nguyen, Duy-Linh Vo, Xuan-Thuy Jo, Kang-Hyun Intelligent Systems Laboratory Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan44610 Korea Republic of

ISBN: (纸本)9783031741265

This paper introduces the development of an essential deep-learning model for surveillance systems utilizing high-mounted CCTV or drones. Objects seen from elevated angles often look smaller and may appear at different angles compared to ground-level observations. To improve the detection of small objects, we propose a network incorporating an element-wise multiplication module based on the vanilla Vision Transformer (ViT) architecture. However, traditional transformer models need significant computational resources, which may not be practical for edge devices like CCTV cameras or drones. Therefore, we apply the Attention-Free Transformer (AFT) to reduce computational requirements enabling real-time operation on low-capacity devices. We validate the performance by combining ViT and AFT with the YOLOv5 real-time object detection model. Practical applicability is confirmed by implementing it on the low-capacity device named ODROID H3+. Validation datasets include Autonomous Driving Drone, VisDrone, AerialMaritime, and PKLot, all containing numerous small-sized objects. Experimental results on the VisDrone dataset show that YOLOv5 nano + AFT reduces parameter count by 4.6% while increasing accuracy by 1%, making it an efficient network. The model size is suitable for edge device implementation at 3.7 MB. Similarly, Aerial Maritime and PKLot datasets indicate a decreased amount of parameters and increased accuracy. Hence, the proposed deep learning model is applicable for aerial surveillance systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drones

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Optimal and efficient text counterfactuals using Graph Neural Networks 7

Optimal and efficient text counterfactuals using Graph Neura...

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7th BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2024

作者： Lymperopoulos, Dimitris Lymperaiou, Maria Filandrianos, Giorgos Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798891761704

As NLP models become increasingly integral to decision-making processes, the need for explainability and interpretability has become paramount. In this work, we propose a framework that achieves the aforementioned by generating semantically edited inputs, known as counterfactual interventions, which change the model prediction, thus providing a form of counterfactual explanations for the model. We frame the search for optimal counterfactual interventions as a graph assignment problem and employ a GNN to solve it, thus achieving high efficiency. We test our framework on two NLP tasks - binary sentiment classification and topic classification - and show that the generated edits are contrastive, fluent and minimal, while the whole process remains significantly faster than other state-of-the-art counterfactual editors. 1 ©2024 Association for Computational Linguistics.

关键词： Graph neural networks

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A Decision Support Tool for Aggregation and Prioritisation of Energy Efficiency Investments in Buildings 15

A Decision Support Tool for Aggregation and Prioritisation o...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Andreoulaki, Ioanna Stoian, Daniela Papapostolou, Aikaterini Stavrakaki, Andriana Marinakis, Vangelis School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

Reducing buildings' energy consumption through energy efficiency interventions has become a key priority to mitigate climate change. To this end, stakeholders involved in the implementation and financing of energy efficiency projects in the building sector could benefit from the development of specific methodologies and user-friendly tools that can assist them by supporting them in managing large datasets and by simplifying decision making processes. In this context, the aim of this paper is to propose a decision-making tool for triggering investments in energy efficiency. The tool's workflow consists of three layers: project selection (layer 1) - narrowing down the list of potential energy efficiency projects according to investor preferences, aggregation (layer 2) - leveraging an optimisation algorithm to create clusters of similar projects, and finally, prioritisation (layer 3) - applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to rank the clusters. The tool brings significant added value to stakeholders involved in financing energy efficiency interventions, offering standardisation and automation of procedures. © 2024 IEEE.

关键词： Decentralized finance

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Puzzle Solving using Reasoning of Large Language Models: A Survey

Puzzle Solving using Reasoning of Large Language Models: A S...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Giadikiaroglou, Panagiotis Lymperaiou, Maria Filandrianos, Giorgos Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798891761643

Exploring the capabilities of Large Language Models (LLMs) in puzzle solving unveils critical insights into their potential and challenges in AI, marking a significant step towards understanding their applicability in complex reasoning tasks. This survey leverages a unique taxonomy-dividing puzzles into rule-based and rule-less categories-to critically assess LLMs through various methodologies, including prompting techniques, neuro-symbolic approaches, and fine-tuning. Through a critical review of relevant datasets and benchmarks, we assess LLMs' performance, identifying significant challenges in complex puzzle scenarios. Our findings highlight the disparity between LLM capabilities and human-like reasoning, particularly in those requiring advanced logical inference. The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency and contribute to AI's logical reasoning and creative problem-solving advancements. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Fully privacy-preserving distributed optimization in power systems based on secret sharing

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iEnergy 2022年第3期1卷 351-362页

作者： Nianfeng Tian Qinglai Guo Hongbin Sun Xin Zhou The State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy ***,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical *** paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing *** framework decomposes the collaborative optimization problem into a master problem and several *** process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among *** relationships of agents are completely equal,and there is no privileged agent or any third *** process of solving subproblems is conducted by agents *** to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private *** data among agents are encrypted and no private information disclosure is ***,the framework maintains a limited and acceptable increase in computational costs while guaranteeing *** studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.

关键词： Secure multi-party computation privacy preservation secret sharing distributed optimization.

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Parasitic Capacitance Analysis of Integrated Transformers with Parallel Windings

Parasitic Capacitance Analysis of Integrated Transformers wi...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yuan, Tianlong Jin, Feng Li, Qiang Virginia Polytechnic and State University Center for Power Electronics Systems Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

The electric transportation market has experienced rapid growth in recent years, driven by increasing environmental concerns. Resonant converters play a crucial role in this sector due to their ability to provide isolation and regulation functions with high efficiency. A resonant inductance embedded transformer is a critical component of the LLC resonant converter, significantly influencing the power density of the entire system, particularly in high-power applications. To achieve higher power density, the operating frequency often exceeds hundreds of kilohertz. At such high frequencies, parasitic capacitance cannot be ignored, as it may cause electromagnetic interference (EMI) issues. In high-power applications, integrated transformers with parallel windings are commonly used, adding complexity to the system. Measuring the parasitic capacitance of these transformers becomes increasingly challenging. This paper presents various measurement methods for an integrated transformer with parallel windings in a 30 kW LLC resonant converter. The results obtained from different measurement techniques were consistent with each other and with simulation results, validating the accuracy of the methods employed. © 2024 IEEE.

关键词： Resonant converters

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Aggregated Flexibility Dynamic Forecasting of Residential Energy Prosumers for DR Programs 15

Aggregated Flexibility Dynamic Forecasting of Residential En...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Papias, Ioannis Michalakopoulos, Vasilis Sarmas, Elissaios Marinakis, Vangelis School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

In today's dynamic energy landscape, Demand Response (DR) programs have become essential for ensuring grid stability and operational efficiency. These programs empower prosumers to adjust their electricity consumption collectively or voluntarily, often against a predefined baseline load. Accurate estimation of this flexibility is pivotal for the success of DR programs, particularly in scenarios where only aggregated building data is available. This study highlights the significance of constructing a building's flexibility baseline load, which approximates its electricity consumption and production in the absence of specific DR events. The deviation of energy consumption and production from this baseline, by predetermined margins, signifies potential flexibility. However, accurately forecasting next-day load variations is essential for evaluating flexibility spread around the baseline. In addressing this need, we employ Long Short-Term Memory (LSTM) networks, optimizing their parameters to significantly enhance performance in predicting energy consumption and production for all weekdays. To validate our proposed solution, we conduct real-world experiments using prosumer data collected from a building in Austria. © 2024 IEEE.

关键词： Demand response

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