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A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

Changes in the Heart Rate During Sleep With Menstrual Cycle Under Free-Living Conditions

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Changes in the Heart Rate During Sleep With Menstrual Cycle ...

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hazuki Masuda Shima Okada Takahide Tanaka Naruhiro Shiozawa Yusuke Sakaue Masanobu Manno Graduate School of Science and Engineering Ritsumeikan University Shiga Japan Faculty of Sport and Health Science Ritsumeikan University Shiga Japan Department of Robotics Faculty of Science and Engineering Ritsumeikan University Shiga Japan Ritsumeikan Global Innovation Research Organization Ritsumeikan University Shiga Japan Corp. Value Link Technology Tokyo Japan

ISBN: (纸本)9781665492331

In this study, we observed the changes in the heart rate (HR) during sleep with respect to the menstrual cycle under free-living conditions. Seven female university students with normal menstrual cycles were allowed to live freely for one month without controlling their sleeping and waking times. The HR was measured by a wristwatch wearable device during nighttime sleep. Considering the circadian rhythm, the time of the lowest point of the circadian rhythm (min CR time) was estimated using HR during sleep, and the change in HR at min CR time (min HR) with menstrual cycle was evaluated. For comparison, the sublingual temperature was measured immediately after waking up as basal body temperature (BBT). The three subjects whose biological rhythms were disturbed showed no significant difference in BBT between the follicular and luteal phases. On the other hand, regarding min HR, all subjects showed a significant difference between the follicular and luteal phases (P<0.05), and it increased in the luteal phase compared to the follicular phase. Thus, the HR at the lowest point of the circadian rhythm (min HR) may be an important parameter for monitoring the menstrual cycle in women with irregular rhythms.

关键词： Temperature measurement Cardiovascular system Heart beat Wearable computers Autonomic nervous system Biochemistry Circadian rhythm

Planning a haemodialysis process by minimum time control of hybrid systems with sliding motion

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Planning a haemodialysis process by minimum time control of ...

IEEE Conference on Decision and Control

作者： Radosław Pytlak Damian Suski Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781665436601

The aim of the paper is to provide a computational tool for planning a haemodialysis process. It is shown that optimization methods can be used to obtain the most effective treatment focused on removing both urea and phosphorus during the process. Our approach to the planning process is based on a model which takes into account a rebound phenomenon and that results in a hybrid model in which sliding motion is likely to occur. For such a model we construct an optimization problem and a computational method for solving it. The presented approach to optimal control problems with hybrid systems is different from the others in several aspects. First of all, it is assumed that a hybrid system can exhibit sliding modes. Secondly, the system’s motion on the switching surface is described by index 2 differential–algebraic equations and that guarantees accurate tracking of the sliding motion surface. Thirdly, the gradients of the problem’s functionals are evaluated with the help of adjoint equations. The adjoint equations presented in the paper take into account sliding motion and exhibit jump conditions at transition times. The stated adjoint equations are used in a globally convergent algorithm which generates a sequence of controls whose accumulation points satisfy the weak maximum principle for optimal control problems with hybrid systems. The paper presents numerical results of solving haemodialysis planning problem.

关键词： Tracking Sensitivity analysis Computational modeling Process control Optimal control Optimization methods Switches

An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron

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arXiv 2024年

作者： Alazab, Moutaz Khurma, Ruba Abu Castillo, Pedro A. Abu-Salih, Bilal Martı´n, Alejandro Camacho, David Faculty of Artificial Intelligence Al-Balqa Applied University Amman Jordan Computer Science Department Faculty of MEU Research Unit Middle East University Amman Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%. © 2024, CC BY.

关键词： Optimization

Correction to: Design and experimental validation of a nonlinear controller for underactuated surface vessels

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Nonlinear Dynamics 2021年第2期104卷 1451-1454页

作者： Xie, Wei Reis, Joel Cabecinhas, David Silvestre, Carlos Faculty of Science and Technology University of Macau Taipa China Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

The correct Figs. 2, 3, 4, 5, and 6 are shown in the correction article.

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Situational Graphs for Robot Navigation in Structured Indoor Environments

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arXiv 2022年

作者： Bavle, Hriday Sanchez-Lopez, Jose Luis Shaheer, Muhammad Civera, Javier Voos, Holger The Automation and Robotics Research Group Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg The Faculty of Science Technology and Medicine University of Luxembourg Luxembourg I3A Universidad de Zaragoza Spain

Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments. 3D scene graphs are an emerging field of research that propose to represent the environment in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been combined with SLAM techniques to provide robots with situational understanding, further research is still required to effectively deploy them on-board mobile robots. To this end, we present in this paper a novel, real-time, online built Situational Graph (S-Graph), which combines in a single optimizable graph, the representation of the environment with the aforementioned three dimensions, together with the robot pose. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes (1) a robot tracking layer where the robot poses are registered, (2) a metric-semantic layer with features such as planar walls and (3) our novel topological layer constraining the planar walls using higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment. © 2022, CC BY.

关键词： Mobile robots

An Efficient USE-Net Deep Learning Model for Cancer Detection

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International Journal of Intelligent Systems 2023年第1期2023卷

作者： Almutairi, Saad M. Manimurugan, S. Aborokbah, Majed M. Narmatha, C. Ganesan, Subramaniam Karthikeyan, P. Industrial Innovation & Robotics Center Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Electrical and Computer Engineering Oakland University Rochester112345 United States Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for fnding BrCa at an earlier stage and monitoring BrCa during treatment. Te computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. Te main objective of this research is to classify the BrCa images for newly generated images. Te model performs preprocessing, segmentation, feature extraction, and classifcation. In preprocessing, a hybrid median fltering (HMF) is used to eliminate the noise in the images. Te contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Ten, ROI segmentation is performed using the USE-Net deep learning model. Te CafeNet model is used for feature extraction on the segmented images, and fnally, classifcation is made using the improved random forest (IRF) with extreme gradient boosting (XGB). Te model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specifcity, 98.96% precision, and 98.70% f1-score for ultrasound images. Te model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specifcity, 98.82% precision, and 99.05% f1-score for mammogram images. © 2023 John Wiley and Sons Ltd. All rights reserved.

关键词： Feature extraction

Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology

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arXiv 2023年

作者： Li, Yiqing Noack, Bernd R. Wang, Tianyu Cornejo Maceda, Guy Y. Pickering, Ethan Shaqarin, Tamir Tyliszczak, Artur School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China Department of Mechanical Engineering Tafila Technical University Tafila66110 Jordan Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology Czestochowa42-201 Poland

We optimize the jet mixing using large eddy simulations (LES) at a Reynolds number of 3000. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning, and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius (Req) derived from the streamwise velocity in a plane located 8 diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. The plant benefits from a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius Req with a 7-armed blooming jet after around 570 evaluations. The control input requires 2% momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double helix solution. The learning is accelerated and converges to another better optimum by including surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing. © 2023, CC BY-NC-ND.

关键词： Flow patterns