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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6723-6738页

作者： Qin, Lutong Zhang, Lei Li, Chengrun Song, Chaoda Cheng, Dongzhou Wang, Shuoyuan Wu, Hao Song, Aiguo Nanjing Normal University School of Electrical and Automation Engineering Nanjing210023 China Shanghai200240 China Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States University of Macau Department of Computer and Information Science Taipa999078 China Yunnan University School of Information Science and Engineering Kunming650500 China Southeast University Department of Instrument Science and Engineering Nanjing210096 China

Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. © 2020 IEEE.

关键词： Budget control

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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SELF-ENSEMBLE PROTECTION: TRAINING CHECKPOINTS ARE GOOD DATA PROTECTORS 11

SELF-ENSEMBLE PROTECTION: TRAINING CHECKPOINTS ARE GOOD DATA...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Sizhe Yuan, Geng Cheng, Xinwen Gong, Yifan Qin, Minghai Wang, Yanzhi Huang, Xiaolin Department of Automation Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Northeastern University United States

As data becomes increasingly vital, a company would be very cautious about releasing data, because the competitors could use it to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, we could add imperceptible perturbations to it. Since such perturbations aim at hurting the entire training process, they should reflect the vulnerability of DNN training, rather than that of a single model. Based on this new idea, we seek perturbed examples that are always unrecognized (never correctly classified) in training. In this paper, we uncover them by model checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples;(2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures. That is, our amazing performance of ensemble only requires the computation of training one model. By extensive experiments with 9 baselines on 3 datasets and 5 architectures, SEP is verified to be a new state-of-the-art, e.g., our small ∞ = 2/255 perturbations reduce the accuracy of a CIFAR-10 ResNet18 from 94.56% to 14.68%, compared to 41.35% by the best-known method. Code is available at https://***/Sizhe-Chen/SEP. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Obstacle avoidance parking based on improved deep reinforcement learning SAC algorithm

Obstacle avoidance parking based on improved deep reinforcem...

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2024 China automation Congress, CAC 2024

作者： Liu, Ting Liu, Ping Liu, Mingjie Piao, Changhao Chang, Kyunghi College of Automation Chongqing University of Posts and Telecommunications Chongqing China Electrical and Computer Engineering Department Inha University Incheon Korea Republic of

ISBN: (纸本)9798350368604

Recent research uses neural-network-based approaches to generate time-optimized parking trajectories in linear time. However, the generalization of these neural networks in different parking lot scenarios is not fully considered and relies on high-quality data. To address these issues, this paper proposes an improved soft actor critic (SAC)based parking trajectory planning method to achieve fast convergence and high success rate. A trajectory data-based reward function and an experience replay sampling strategy are designed simultaneously improve the learning efficiency. Meanwhile, the Flatten-T Swish Plus (FTSPlus) activation function is firstly introduced into the SAC's neural network structure to enhance the convergence capabilities. The simulation results show that compared with traditional SAC methods, the success rate has been greatly improved and convergence rate has also been improved by more than 60%. Furthermore, it has the good model generalization ability while maintaining collision-free trajectories. © 2024 IEEE.

关键词： Trajectories

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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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Fully integrated Windows framework for source localization with MNE Python and FreeSurfer

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Current Directions in Biomedical engineering 2023年第1期9卷 371-374页

作者： Oppermann, Hannes Wulf, Simon Komosar, Milana Haueisen, Jens Haueisen, Jens Department of Computer Science and Automation Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Department of Neurology Biomagnetic Center University Hospital Jena Jena Germany

There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the '.cnt', eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Python

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Probabilistic Model Checking for IEC 61499: A Manufacturing Application 25

Probabilistic Model Checking for IEC 61499: A Manufacturing ...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Faqrizal, Irman Liakh, Tatiana Xavier, Midhun Salaün, Gwen Vyatkin, Valeriy Univ. Grenoble Alpes Cnrs Grenoble Inp Inria Lig Grenoble France Lulea Tekniska Universitet Department Of Computer Science Computer And Space Engineering Sweden Aalto University Department Of Electrical Engineering And Automation Espoo Finland

ISBN: (纸本)9798350340266

The ever-increasing complexity of industrial control systems generates a demand for reliable development methods. IEC 61499, a recent industrial standard, helps to develop complex distributed systems based on their positive characteristics, namely reusability, reconfigurability, interoperability, and portability. Formal verification techniques, such as model checking, have been proposed to ensure the correctness of these systems during the design time. However, they do not consider the presence of the environment that can impact the application behaviour at runtime. This work combines design time and runtime analyses to apply probabilistic model checking on an IEC-61499-based manufacturing application. We present several probabilistic properties to be checked. The results are visualised graphically to be analysed, which allows one to optimise the system's quantitative features, such as productivity. © 2024 IEEE.

关键词： Formal verification

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Utilizing chaos game representation for enhanced classification of SARS-CoV-2 variants with stacked sparse autoencoders

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Neural Computing and Applications 2024年第31期36卷 19823-19837页

作者： Coutinho, Maria G. F. Câmara, Gabriel B. M. Barbosa, Raquel de M. Fernandes, Marcelo A. C. RN Natal59078-900 Brazil UFRN RN Natal59078-900 Brazil Department of Pharmacy and Pharmaceutical Technology University of Seville Seville41012 Spain Department of Computer and Automation Engineering UFRN RN Natal59078-900 Brazil

Since the beginning of the COVID-19 pandemic, the World Health Organization (WHO) has been tracking SARS-CoV-2 mutations. The SARS-CoV-2 consistently mutated throughout the pandemic, which resulted in many variants. A variant is a viral genome containing one or more genetic code mutations. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. This work proposed an effective viral genome classifier for SARS-CoV-2 variants using the deep neural network based on the stacked sparse autoencoder (SSAE). Aiming to achieve the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input. The dataset based on Chaos Game Representation (CGR) images was generated and applied to the experiments of classification of SARS-CoV-2 variants of concern (VOC). The SSAE technique provided great performance results, achieving classification accuracy of 99.9% for the validation set and 99.8% for the test set. Finally, the results indicated the relevance of using this deep learning technique in genome classification problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Coronavirus

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Analysis of Predictive Current Regulator for High-Speed IPMSM Operating at Low Sampling to Fundamental Frequency Ratios 26

Analysis of Predictive Current Regulator for High-Speed IPMS...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Lin, Shengyu Mon-Nzongo, Daniel L. Ipoum-Ngome, Paul G. Lai, Chunyan Fuzhou University College of Electrical Engineering and Automation Fuzhou China Concordia University Department of Electrical and Computer Engineering Concordia Canada

ISBN: (纸本)9798350317589

At low sampling to fundamental frequency ratios, the time delay introduced by the Pulse Width Modulation (PWM) calculation and the duty cycle update deteriorates the decoupling dynamic between d and q-axis currents. The controllers implemented with One-step Current Prediction (OCP) method can reduce the effects of PWM delay. But, inappropriate tuning of the control parameters will lead to poor dynamic responses and less stability. Therefore, a comprehensive analysis and tuning approach are proposed in this paper, for the PI and the Internal Model Controller (IMC) with the OCP. In addition, an active resistance for the Interior Permanent Magnet Synchronous Motors (IPMSM) is designed and implemented in IMC to enhance the disturbance rejection capabilities. This paper introduces the root locus to select the correct parameter with better dynamic response and stability. With OCP and active resistance, the IMC controller obtains an improved dynamic response and stability at a minimum sampling ratio of 5. Simulation results performed at various conditions have successfully validated this study. © 2023 IEEE.

关键词： Pulse width modulation

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Doped low-density parity-check codes

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Digital Communications and Networks 2024年第1期10卷 217-226页

作者： Yong Li Rui Liu Xianlong Jiao Youqiang Hu Zhen Luo Francis C.M.Lau College of Computer Science Chongqing UniversityChongqing400044China School of Automation Chongqing UniversityChongqing400044China College of Electronic and Information Engineering Southwest UniversityChongqing400715China Future Wireless Networks and IoT Focusing Area Department of Electronic and Information EngineeringHong Kong Polytechnic UniversityHong KongChina

In this paper,we propose a doping approach to lower the error floor of Low-Density Parity-Check(LDPC)*** doping component is a short block code in which the information bits are selected from the coded bits of the dominant trapping sets of the LDPC ***,an algorithm for selecting the information bits of the short code is proposed,and a specific two-stage decoding algorithm is *** results demonstrate that the proposed doped LDPC code achieves up to 2.0 dB gain compared with the original LDPC code at a frame error rate of 10^(-6)Furthermore,the proposed design can lower the error floor of original LDPC Codes.

关键词： LDPC codes Doped LDPC codes Tanner graph Quadratic residue codes Trapping sets

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