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arXiv 2024年

作者： Ye, Qilang Yu, Zitong Liu, Xin The School of Computing and Information Technology Great Bay University Dongguan523000 China The School of Computer Science and Engineering Chongqing University of Technology Chongqing401300 China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland

Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer. Although mining deeper layers of audio-visual information to interact with questions facilitates the multimodal fusion process, the redundancy of audio-visual parameters tends to reduce the generalization of the inference engine to multiple question-answer pairs in a single video. Indeed, the natural heterogeneous relationship between audiovisuals and text makes the perfect fusion challenging, to prevent high-level audio-visual semantics from weakening the network’s adaptability to diverse question types, we propose a framework for performing mutual correlation distillation (MCD) to aid question inference. MCD is divided into three main steps: 1) firstly, the residual structure is utilized to enhance the audio-visual soft associations based on self-attention, then key local audio-visual features relevant to the question context are captured hierarchically by shared aggregators and coupled in the form of clues with specific question vectors. 2) Secondly, knowledge distillation is enforced to align audiovisual-text pairs in a shared latent space to narrow the cross-modal semantic gap. 3) And finally, the audio-visual dependencies are decoupled by discarding the decision-level integrations. We evaluate the proposed method on two publicly available datasets containing multiple question-and-answer pairs, i.e., Music-AVQA and AVQA. Experiments show that our method outperforms other state-of-the-art methods, and one interesting finding behind is that removing deep audio-visual features during inference can effectively mitigate overfitting. The source code is released at http://***/rikeilong/MCD-forAVQA. © 2024, CC BY.

关键词： Distillation

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Online demand peak shaving with machine-learned advice in digital twins

Digital Twins and Applications

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Digital Twins and Applications 2024年第1期1卷 38-50页

作者： Minxi Feng Wei Li Boyu Qin Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneySydneyNew South WalesAustralia State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical EngineeringXi'an Jiaotong UniversityXi'anChina

As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin *** load management is central to energy systems within digital twins,which significantly impacts operational *** demand loads can lead to substantial monthly utility expenses without proper ***,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand *** leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load *** provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive *** trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.

关键词： operations maintain battery

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Tenacity prediction of polyester materials based on an ensemble soft measurement model

Tenacity prediction of polyester materials based on an ensem...

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2024 China Automation Congress, CAC 2024

作者： Duan, Jiaxu Wang, Jun Tao, Youji Ma, Zhiping Wu, Xin Hu, Zhou State Key Laboratory of Environmental Adaptability for Industrial Products China National Electric Apparatus Research Institute Co. Ltd Guangzhou China Kinte Materials Science and Technology Co. Ltd Guangzhou China Department of Computer Science Hunan University of Technology and Business Changsha China School of Electronic Information and Electrical Engineering Yangtze University Jingzhou China

ISBN: (纸本)9798350368604

In the development process of polyester powder coating materials, it has great guiding significance for the development of new materials as the polyester material properties can be predicted through the given monomer structure and formula. In order to meet the requirements of rapid development of polyester materials, this paper establishes a prediction method based on an ensemble soft measurement model (ESM) for one key property-tenacity in prompting the acceleration of development cycle of polyester products. The processed data employed to train and test the model were collected from a local material company, and it covers over 100 kinds of polymerized resins. Firstly, the molecular expressions of monomers are converted to SMILES codes and further translated to ECFP fingerprints. Then, the principal component analysis (PCA) method is adopted to extract the elements with higher importance in each fingerprint. Finally, the ensemble model that consists of 4 machine learning models was established to make the multi-classification decision in tenacity prediction. The effectiveness of the prediction model is evaluated by comparing the predicted and the practical tensile test results. The results demonstrate that this approach achieves better performance in the material property prediction as well as generalization capability. © 2024 IEEE.

关键词： Tensile testing

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Editorial of special issue on spatiotemporal optical fields and time-varying optical materials

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Chinese Optics Letters 2023年第12期21卷 1-6页

作者：万辰皓陈伟曹前 School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Optical science is currently witnessing a surge of interest in exploring complex optical fields with intricately tailored space-time *** spatiotemporal optical fields,carefully sculpted,are opening up new facets in both classical and quantum optics studies and paving the way for many novel *** include,but are not limited to,superresolution imaging,nanofabrication,optical manipulation,high-dimensional communication,and quantum information ***,there is a growing fascination surrounding time-varying optical *** dynamic materials,in stark contrast to their time-invariant counterparts,provide an innovative degree of freedom in orchestrating electromagnetic fields and light–matter interactions across time and ***,these time-varying materials offer a promising avenue for generating and tailoring spatiotemporal optical fields.

关键词： materials quantum invariant

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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PCART: Automated Repair of Python API Parameter Compatibility Issues

arXiv

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arXiv 2024年

作者： Zhang, Shuai Xiao, Guanping Wang, Jun Lei, Huashan He, Gangqiang Liu, Yepang Zheng, Zheng The College of Computer Science and Technology The Key Laboratory for Safety-critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The School of Automation Science and Electrical Engineering Beihang University Beijing China

In modern software development, Python third-party libraries play a critical role, especially in fields like deep learning and scientific computing. However, API parameters in these libraries often change during evolution, leading to compatibility issues for client applications reliant on specific versions. Python’s flexible parameter-passing mechanism further complicates this, as different passing methods can result in different API compatibility. Currently, no tool can automatically detect and repair Python API parameter compatibility issues. To fill this gap, we introduce PCART, the first solution to fully automate the process of API extraction, code instrumentation, API mapping establishment, compatibility assessment, repair, and validation. PCART handles various types of Python API parameter compatibility issues, including parameter addition, removal, renaming, reordering, and the conversion of positional to keyword parameters. To evaluate PCART, we construct PCBENCH, a large-scale benchmark comprising 47,478 test cases mutated from 844 parameter-changed APIs across 33 popular Python libraries. Evaluation results demonstrate that PCART is both effective and efficient, significantly outperforming existing tools (MLCatchUp and Relancer) and the large language model ChatGPT (GPT-4o), achieving an F1-score of 96.49% in detecting API parameter compatibility issues and a repair precision of 92.26%. Further evaluation on 30 real-world Python projects from GitHub confirms PCART’s practicality. We believe PCART can significantly reduce the time programmers spend maintaining Python API updates and advance the automation of Python API compatibility issue repair. Copyright © 2024, The Authors. All rights reserved.

关键词： Python

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Sensorless Torque Control of an Electric Vehicle Driven by an Open End Winding Induction Motor: An Experimental Study 2

Sensorless Torque Control of an Electric Vehicle Driven by a...

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2nd International Conference on Energy Transition and Security, ICETS 2023

作者： Younes, Djellouli Ahmed El Mehdi, Ardjoun Sid Houcine, Chafouk Mouloud, Denai Djilali Liabes University Irecom Laboratory Faculty of Electrical Engineering Sidi Bel-Abbes22000 Algeria Normandy University Unirouen Esigelec Irseem Rouen76000 France University of Hertfordshire School of Physics Engineering and Computer Science HatfieldAL10 9AB United Kingdom

ISBN: (纸本)9798350372182

Electric Vehicles (EVs) are anticipated to play a pivotal role in addressing global climate change objectives and offering a solution for improved fuel efficiency. However, enhancing the performance, energy efficiency and cost remain the major challenges for EV technology. Identifying the operating points of the EVs motor drive system is crucial for optimizing its performance. Sensorless electric motor control for EVs is one of the promising research areas for cost reduction and performance improvement. This work's primary objective is to develop, implement and validate experimentally a torque estimator based on Luenberger observer on an EV driven by an open-end winding induction motor (OEWIM) fed by two inverters. The proposed observer structure uses the complete OEWIM mathematical model. The rotor fluxes are estimated using the electrical model whilst the torque is observed using the mechanical model. This observer has been implemented using a board signified for a 1.5 kW OEWIM. The obtained experimental findings demonstrate the efficacy of the method put forward. © 2023 IEEE.

关键词： Torque

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Denoised Internal Models:A Brain-inspired Autoencoder Against Adversarial Attacks

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Machine Intelligence Research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life Sciences and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201602China School of Life Sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

关键词： Brain-inspired learning autoencoder robustness adversarial attack generative model

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Real-Time Implementation of the Luenberger Torque Observer for an Induction Motor 2

Real-Time Implementation of the Luenberger Torque Observer f...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Younes, Djellouli El Mehdi, Ardjoun Sid Ahmed Mouloud, Denai Houcine, Chafouk Djillali Liabes University Irecom Laboratory Faculty of Electrical Engineering Sidi Bel-Abbes22000 Algeria School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Normandy University Unirouen Esigelec Irseem Rouen76000 France

ISBN: (纸本)9798350314243

Currently, the technology of high-performance variable speed drives without mechanical sensors is widely used in the industrial environment. This preference arises from the exorbitant costs and fragility associated with mechanical sensors. This investigation aims to establish, implement, and empirically validate a Luenberger Observer for wheel torque within an electric propulsion framework that employs induction motors. The proposed observer structure focuses on the complete dynamic model of an induction motor. Concerning the rotor fluxes, they are estimated using the electrical model. On the other hand, the torque is observed using the mechanical model. A real-time implementation was performed on a DSpace 1104 board designed for a 1.5 kW induction motor to verify the proposed observer's effectiveness. The experiment results show that the proposed method exhibits satisfactory performance, affirming its effectiveness and robustness and giving valuable insights into its practical applicability. © 2023 IEEE.

关键词： Induction motors

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Decision Transformers for Wireless Communications: A New Paradigm of Resource Management

arXiv

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arXiv 2024年

作者： Zhang, Jie Li, Jun Wang, Zhe Shi, Long Jin, Shi Chen, Wen Poor, H. Vincent School of Electronic and Optical Engineering Nanjing University of Science and Technology China National Mobile Communications Research Laboratory Southeast University China School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Electronic Engineering Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Princeton University United States

As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable environments. In particular, deep reinforcement learning (DRL) is an important tool for addressing stochastic optimization issues of resource allocation. However, DRL has to start each new training process from the beginning once the state and action spaces change, causing low sample efficiency and poor generalization ability. Moreover, each DRL training process may take a large number of epochs to converge, which is unacceptable for time-sensitive scenarios. In this paper, we adopt an alternative AI technology, namely, Decision Transformer (DT), and propose a DT-based adaptive decision architecture for wireless resource management. This architecture innovates through constructing pre-trained models in the cloud and then fine-tuning personalized models at the edges. By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios with different state and action spaces, compared with DRL. We then design DT frameworks for two typical communication scenarios: intelligent reflecting surfaces-aided communications and unmanned aerial vehicle-aided mobile edge computing. Simulations demonstrate that the proposed DT frameworks achieve over 3-6 times speedup in convergence and better performance relative to the classic DRL method, namely, proximal policy optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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