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Remaining Useful Life Prediction Method of Lithium-Ion Batteries is Based on Variational Modal Decomposition and Deep Learning Integrated Approach

SSRN

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SSRN 2023年

作者： Wang, Shuai Ma, Hongyan Zhang, Yingda Li, Shengyan He, Wei School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing10044 China Institute of Distributed Energy Storage Safety Big Data Beijing10044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing10044 China

A common method based on variational modal decomposition (VMD) and an integrated depth model is proposed to address the problem that it is difficult to precisely anticipate the remaining useful life (RUL) of lithium-ion batteries (LIBs). Initially, VMD is employed to decompose the LIBs capacity data in multiple scales to obtain the signal's global degradation tendency and local random fluctuation components. Then, the global degradation trend and each fluctuation component are modeled using an echo state network (ESN) and a Bayesian optimized long short-term memory (LSTM) network, respectively. The final LIBs RUL prediction results are obtained by integrating the prediction outcomes. On three public LIBs datasets with distinct degradation characteristics, the performance of the proposed model is tested, and alternative prediction algorithms are compared. The results of the experiments show that the proposed model's maximum average absolute percentage error does not exceed 0.43%. The average relative error not more than 0.5%, indicating great accuracy and stability in prediction. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Unveiling Network Anomalies: A Comparative Study of Real-time Log-Based Detection Approach

Unveiling Network Anomalies: A Comparative Study of Real-tim...

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IEEE India Conference (INDICON)

作者： Deepika Marella Roopa Navya Muthi Buchammagari Avinash Reddy Kaniganti Priyanka Saraswathi Vijender Busi Reddy Nenavath Srinivas Naik Computer Science and Engineering IIIT Naya Raipur Raipur India Data Science and Artificial Intelligence IIIT Naya Raipur Raipur India Advanced Data Processing Research Institute (ADRIN) Secunderabad Secunderabad India Computer Science and Engineering IIITDM Kurnool Kurnool India

In this paper, we address the issue of detecting anomalies in network log files. In laboratory setup, log files are generated by network devices such as routers, switches, firewalls, and servers. These log files contain information about network activities, such as network traffic, device configurations, user activity, and security events. Analyzing log files on computer networks is essential for identifying potential security threats and network issues. Researchers have sought to develop quick and effective methods to identify anomalies in a computer system by examining its log data as a result of this significant utility. In our work, we have used a machine-learning approach called Isolation Forest (iForest) and also examined clustering algorithms, Ordering Points To Identify Cluster Structure, referred to as OPTICS Clustering, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, for detecting anomalies in real-time log datasets. The performance of each model is evaluated and compared. Finally, we conclude by presenting the experimental results that demonstrate the effectiveness of the suggested method and future directions in real-time log-based anomaly detection, highlighting the need for more advanced and efficient methods to deal with the increasing complexity and scale of modern networks.

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Onset Detection of Pulse-Shaped Bioelectrical Signals Using Linear State Space Models

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Current Directions in Biomedical engineering 2022年第2期8卷 101-104页

作者： Waldmann, Frédéric Baeriswyl, Christof Andonie, Raphael Wildhaber, Reto A. Institute for Medical Engineering and Medical Informatics University of Applied Sciences and Arts Northwestern Switzerland Muttenz Switzerland STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Bern University of Applied Sciences Biel Switzerland Hearing Research Laboratory ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Institute for Medical Engineering and Medical Informatics University of Applied Sciences and Arts Northwestern Switzerland Hofackerstrasse 30 Muttenz Switzerland ETH Zurich Sternwartstrasse 7 Zurich Switzerland

Bioelectrical signals are often pulse-shaped with superimposed interference signals. In this context, accurate identification of features such as pulse onsets, peaks, amplitudes, and duration is a frequent problem. In this paper, we present a versatile method of rather low computational complexity to robustly identify such features in real-world signals. For that, we take use of two straight-line models fit to the observations by minimizing a quadratic cost term, and then identify desired features by tweaked likelihood measures. To demonstrate the idea and facilitate access to the method, we provide examples from the field of cardiology. © 2022 The Author(s), published by De Gruyter.

关键词： Feature extraction

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Development of Software for Automated Marking of Vertebral Bodies

Development of Software for Automated Marking of Vertebral B...

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2022 IEEE International Multi-Conference on engineering, Computer and Information Sciences, SIBIRCON 2022

作者： Piletskii, D. Gladkov, A. Yakimenko, A. Novosibirsk State Technical University Faculty of Automation and Computer Engineering Novosibirsk Russia Novosibirsk State Technical University Department of Data Acquisition and Processing Systems Novosibirsk Russia Institute of Computational Mathematics and Mathematical Geophysics SB RAS Laboratory of Geophysical Informatics Novosibirsk Russia

ISBN: (纸本)9781665464802

in order to create a digital twin of the spine to simulate the behavior of the spine after various interventions, the task of automating the marking of the vertebral bodies on radiographs was formulated. The presented work describes the developed approaches to solving the problem of marking the vertebral bodies on radiographs, and also describes algorithms for extracting images of the spine and images of individual vertebrae. For the detection of images of each individual vertebra, the YOLOv5 model was used, and an ensemble of regression models was created to solve the problem of marking the image of each individual vertebra. After a series of experiments, marking errors were estimated at 4.6% using the ratio of coordinates obtained using trained models and correctly marked data. Theories were also put forward about the possible improvement of the created software by dividing the vertebrae into two subclasses in order to reduce the markup error to the desired 0.8%. © 2022 IEEE.

关键词： Computer vision

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin institute

关键词： Stability criteria

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Multikernel:Operating system solution to generalized functional safety

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Security and Safety 2023年第2期2卷 30-43页

作者： Yijing Song Huasheng Dai Jinhu Jiang Weihua Zhang School of Computer Science Fudan UniversityShanghai 200433China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Institute of Big Data Fudan UniversityShanghai 200433China Parallel Processing Institute Fudan UniversityShanghai 200433China

With the trend of digitalization,intelligence,and networking sweeping the world,functional safety and cyber security are increasingly intertwined and overlapped,evolving into the issue of generalized functional *** system reliability technology and network defense technology cannot provide quantifiable design implementation theories and *** the cornerstone of software systems,operating systems in particular are in need of efficient safety *** DHR architecture is a mature and comprehensive solution,and it is necessary to implement an OS-level DHR architecture,for which the multi-kernel operating system is a good *** multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy,heterogeneity,and dynamism on the kernel,so it has the generalized robustness of the DHR *** article analyzes the significance and requirements of OS-level DHR architecture,and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

关键词： Metaverse New infrastructure Security base Highly trusted network Trusted identiry Trusted circulation of data

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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A Novel Reliable Routing protocol for MANETs

A Novel Reliable Routing protocol for MANETs

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Nirmala Devi Lavadya Nageswar Rao Amgoth Vijender Busi Reddy Dept.of Electronics and Communication Engineering University College of Engineering Osmania University Hyderabad India Strategic Electronics Research and Design Center (SLRDC)- Communication Dept Hindustan Aeronautics Limited Hyderabad India Advanced Data Processing Research Institute (ADRIN) Hyderabad India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In MANETs, nodes connect each other to share information. These nodes move from one place to other and co-operate with each other. Apart from the security, reliability is also one of the main concern in MANETs. Since, most of the devices in the MANET are mobile, mobility degrades the performance of routing protocol. We have proposed a Routing protocol for MANETs which takes mobility as a parameter and establishes a relative reliable path to the destination. our proposed routing protocol increased the throughput and packet Delivery ratio in mobility environment.

关键词： Performance evaluation Throughput Routing protocols Ad hoc networks Telecommunications Telecommunication network reliability Security Mobile computing

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