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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zhong, Hanwen Chen, Jiaxin Zhang, Yutong Huang, Di Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University China School of Computer Science and Engineering Beihang University Beijing China

Multi-Task Learning (MTL) for Vision Transformer aims at enhancing the model capability by tackling multiple tasks simultaneously. Most recent works have predominantly focused on designing Mixture-of-Experts (MoE) structures and integrating Low-Rank Adaptation (LoRA) to efficiently perform multi-task learning. However, their rigid combination hampers both the optimization of MoE and the effectiveness of reparameterization of LoRA, leading to sub-optimal performance and low inference speed. In this work, we propose a novel approach dubbed Efficient Multi-Task Learning (EMTAL) by transforming a pre-trained Vision Transformer into an efficient multi-task learner during training, and reparameterizing the learned structure for efficient inference. Specifically, we firstly develop the MoEfied LoRA structure, which decomposes the pre-trained Transformer into a low-rank MoE structure and employ LoRA to fine-tune the parameters. Subsequently, we take into account the intrinsic asynchronous nature of multi-task learning and devise a learning Quality Retaining (QR) optimization mechanism, by leveraging the historical high-quality class logits to prevent a well-trained task from performance degradation. Finally, we design a router fading strategy to integrate the learned parameters into the original Transformer, archiving efficient inference. Extensive experiments on public benchmarks demonstrate the superiority of our method, compared to the state-of-the-art multi-task learning approaches. The project page is available at https://***/Yewen1486/EMTAL. © 2024 Neural information processing systems foundation. All rights reserved.

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Investigation of a Photonic Reservoir Computing based Equalizer for Short Reach Applications

Investigation of a Photonic Reservoir Computing based Equali...

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2023 Integrated Photonics Research, Silicon and Nanophotonics, IPR 2023 in Advanced Photonics Congress - Part of Advanced Photonics Congress 2023

作者： Brestas, Giorgos Kanakis, Giannis Spyropoulou, Maria Avramopoulos, Hercules Photonics Communications Research Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

We propose an optical pre-equalizer which relaxes the need for power-hungry DSP in IM/DD applications. A reservoir computing-based approach is pursued demonstrating under FEC performance up to 6km at 50Gbaud NRZ outpe... 详细信息

ISBN: (纸本)9781957171265

关键词： Equalizers

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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State of Charge Estimation Using Data-Driven Models for Inverter-Based systems

State of Charge Estimation Using Data-Driven Models for Inve...

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2023 IEEE Design Methodologies Conference, DMC 2023

作者： Hussein, Hossam Donekal, Abhishek Aghmadi, Ahmed Rafin, S M Sajjad Hossain Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350315547

Lithium-ion batteries are playing a critical role in many applications nowadays, from small-scale electronic devices to grid-scale storage systems. To maintain its continuous operation and increase its life span, the state of charge of the battery should be determined to ensure safe operating conditions. Among the existing charge estimation methods, data-driven models are flourishing these days. This work presents a state of charge estimation for the eFlex 52.8V/5.4 kWh lithium iron phosphate battery pack at the Energy systems Research laboratory (ESRL) at FIU. Three different machine learning models were implemented and trained through Python code to achieve the most accurate SoC estimation. Although the three proposed models can efficiently estimate the battery's SoC with an acceptable error percentage, the random forest regression model has proven its outperformance among the selected models with a percentage mean square error less than 0.01. © 2023 IEEE.

关键词： Lithium-ion batteries

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ANN-Based Secure Control of Islanded Microgrid Under False Data Injection Cyber-Attack

ANN-Based Secure Control of Islanded Microgrid Under False D...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Kharchouf, Ibtissam Abdelrahman, Mahmoud S. Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350320169

AC microgrids may be a target of cyber-attacks such as False Data Injection attack (FDIA) or Denial of Service attacks due to the use of communication technologies. These types of attacks target the communication infrastructure or the control system itself. For instance, FDIAs aim to inject false data into AC microgrids to disrupt the control system. Techniques that detect and mitigate the effect of such attacks are needed. This paper presents an AC microgrid that is structured by parallel inverters and controlled by distributed consensus control strategies to control the frequency and voltage magnitude through active and reactive power. We will investigate the FDIAs effect on the AC microgrid operation. A viable solution to address the FDIA attacks in AC microgrids is developed using an ANN-based reference tracking application to detect and mitigate the attack. The simulation results proved that the proposed strategy could detect and mitigate FDIA attacks especially if the ANN is well trained. © 2023 IEEE.

关键词： Neural networks

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Frequency Stability Enhancement in Low-Inertia Power System Using an Optimal Control Scheme

Frequency Stability Enhancement in Low-Inertia Power System ...

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2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power systems Europe, EEEIC / I and CPS Europe 2023

作者： Ali, Ola Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350347432

Power system dynamics have significantly changed with the global integration of renewable energy sources (RESs). Traditionally, the stored kinetic energy is used for compensating the generation mismatch using the rotating mass of the Synchronous Generators (SGs). With the increase in RESs penetration, SGs are being replaced by inverter-based RESs generation systems which are decoupled from the power systems through power converters. Therefore, the total inertia of the system has decreased, and the system's stability is significantly impacted as the value of the rate of change of frequency (ROCOF) and frequency deviations will be increased. To overcome these challenges, inertia emulation from various energy storage systems (ESS) is used to act as a virtual source of inertia power. This work presents an isolated microgrid (MG) that includes conventional power generation sources and RESs, and an optimal virtual inertia (VI) control loop is proposed for frequency stability enhancement. Supercapacitors (SC) bank with a PID controller presents the VI control loop and acts as a supplementary source for inertia power to support the frequency stability of the system. A genetic algorithm (GA) is used as an optimization technique to get the optimal values of the PID controller gains. The performance of the islanded MG with the proposed optimal control scheme is examined under different operating conditions with varying levels of RESs penetration and system parameters uncertainties. The proposed technique showed its effectiveness and robustness over the system's operation without VI control for keeping the system stable under different operating conditions. © 2023 IEEE.

关键词： Genetic algorithms

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Enhancing DC Microgrid Stability under Pulsed Load Conditions through Hybrid Energy Storage Control Strategy

Enhancing DC Microgrid Stability under Pulsed Load Condition...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Aghmadi, Ahmed Ali, Ola Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798350320169

Despite the increasing prevalence of DC microgrids, they encounter instability issues due to imbalances in supply and demand, especially when dealing with specific loads such as pulse load or variable pulse load. The system faces severe changes which reduce the effectiveness of typical PV-Battery control systems. The solution to this problem frequently involves the use of energy storage devices such as supercapacitors SCs. When exposed to transients, batteries risk having their lifespans shortened and are challenged in keeping the stability of DC bus voltage and reducing overall transients. In order to increase the stability of DC microgrids and thereby increase their reliability and resilience, it is possible to combine the advantages of batteries and supercapacitors. Integrating both batteries and supercapacitors effectively enables DC microgrids to manage both steady-state and transient conditions. The main goal of this research paper is to investigate how the hybrid energy storage system (HESS) improves the stability of DC microgrids. According to sets of results, the proposed scheme has successfully reduced DC bus voltage variations regardless of variations in solar irradiance and the different pulsed load profiles. © 2023 IEEE.

关键词： Supercapacitor

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Recent progress in printing flexible electronics: A review

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Science China(Technological Sciences) 2024年第8期67卷 2363-2386页

作者： BI Sheng GAO BuHan HAN Xu HE ZhengRan METTS Jacob JIANG ChengMing ASARE-YEBOAH Kyeiwaa Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education School of Mechanical EngineeringDalian University of TechnologyDalian 116024China Center for Materials for Information Technology The University of AlabamaTuscaloosaAL 35487USA Department of Electrical and Computer Engineering Penn State BehrendEriePA 16563USA

Miniaturization and flexibility are becoming the trend in the development of electronic products. These key features are driving new methods in the manufacturing of such products. Printed electronics technology is a novel additive manufacturing technique that uses active inks to print onto a diverse set of substrates, realizing large-area, low-cost, flexible and green manufacturing of electronic products. These advantageous properties make it extremely compatible with flexible electronics fabrication and extend as far as offering revolutionary methods in the production of flexible electronic devices. In this paper, the details of a printing process system are introduced, including the materials that can be employed as inks, common substrates, and the most recently reported printing strategies. An assessment of future setbacks and developments of printed flexible electronics is also presented.

关键词： flexible electronics printing process conductive ink flexible substrate

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Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines 30

Recommending Missed Citations Identified by Reviewers: A New...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Long, Kehan Li, Shasha Wang, Pancheng Bao, Chenlong Tang, Jintao Wang, Ting School of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

ISBN: (纸本)9782493814104

Citing comprehensively and appropriately has become a challenging task with the explosive growth of scientific publications. Current citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. However, none of the existing work focuses on already included citations of full papers, which are imperfect and still have much room for improvement. In the scenario of peer reviewing, it is a common phenomenon that submissions are identified as missing vital citations by reviewers. This may lead to a negative impact on the credibility and validity of the research presented. To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR. We conduct an extensive evaluation of several state-of-the-art methods on CitationR. Furthermore, we propose a new framework RMCNet with an Attentive Reference Encoder module mining the relevance between papers, already-made citations, and missed citations. Empirical results prove that RMC is challenging, with the proposed architecture outperforming previous methods in all metrics. We release our dataset and benchmark models to motivate future research on this challenging new task. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Paper

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