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第43届中国控制会议

作者： Tian-Shu Song Zhen-Peng Chen Hui-Rang Hou Hong-Yue Li Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the scene of a fire is crucial for fire investigation. The electronic nose(e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network(FMG-Net).This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network(SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs(diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization

A Depression Level Classification Model Based on EEG and GCN...

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第43届中国控制会议

作者： Zhongyi Zhang Chenyang Xu Licheng Jin Huirang Hou Qinghao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Decision trees

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A Multi-stage Single-Point Incremental Sheet Forming for Enhanced Homogeneous Thickness Distribution

A Multi-stage Single-Point Incremental Sheet Forming for Enh...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ghadeer Issa Karam Almaghout Alexander Maloletov Alexandr Klimchik Intelligent Robotics Systems Laboratory Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Lincoln Centre for Autonomous Systems (L-CAS) School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a multi-stage approach to enhance homogeneous thickness distribution in single-point incremental sheet forming. Traditional methods often struggle with achieving uniform material thickness, compromising the final part’s structural integrity and performance. Our proposed methodology leverages a V-shape starting stage, systematically designed to address these challenges by improving material flow and distribution throughout the forming process. We employ finite element analysis to validate the efficiency of the proposed approach and compare it with conventional single-stage forming processes. The simulations demonstrate significant improvements in achieving a more uniform thickness distribution across the formed parts. The comparative analysis highlights the potential of the proposed multi-stage approach for industrial applications and enhances material homogeneity. This methodology advances the precision and reliability of formed parts and opens new avenues for research and development in sheet metal forming technologies.

关键词： Geometry Accuracy Shape Simulation Metals Production Manufacturing Reliability Information technology Research and development

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Towards Camera Parameters Invariant Monocular Depth Estimation in autonomous Driving

Towards Camera Parameters Invariant Monocular Depth Estimati...

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European Conference on Mobile Robots (ECMR)

作者： Karlo Koledić Ivan Marković Ivan Petrović Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Zagreb Croatia

Monocular depth estimation is an effective approach to environment perception due to simplicity of the sensor setup and absence of multisensor calibration. Deep learning has enabled accurate depth estimation from a single image by exploiting semantic cues such as the sizes of known objects and positions on the ground plane thereof. However, learning-based methods frequently fail to generalize on images collected with different vehicle-camera setups due to the induced perspective geometry bias. In this work, we propose an approach for camera parameters invariant depth estimation in autonomous driving scenarios. We propose a novel joint parametrization of camera intrinsic and extrinsic parameters specifically designed for autonomous driving. In order to supplement the neural network with information about the camera parameters, we fuse the proposed parametrization and image features via the novel module based on a self-attention mechanism. After thorough experimentation on the effects of camera parameter variation, we show that our approach effectively provides the neural network with useful information, thus increasing accuracy and generalization performance.

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CrazyKhoreia, a robotic perception system for UAV path planning from digital images

TechRxiv

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TechRxiv 2023年

作者： Restrepo-García, Santiago Romero Cano, Victor Robotics and Autonomous Systems Laboratory Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia

Micro air vehicles can be used for a wide range of applications, which include drone light shows for cultural or marketing purposes, or as a replacement of fireworks. However, widespread use of this technology is precluded by a usability gap between choreography design and deployment on the target drones. In order to achieve seamless deployment of micro air vehicle choreographies, computational interfaces that obtain drone trajectories from a more conventional media such as digital images are needed. In this paper, we propose CrazyKhoreia, a low-cost, computationally efficient approach to Unmanned Aerial Vehicles (in short, UAVs) choreography design, which consists of a robotic perception system that takes a digital image, applies boundary tracing to it and obtains a safely, traversable and apparently accurate waypoint matrix as a result. We validate our trajectory generation system through two modes of operation, light painting, where the UAV can either fly through all of the waypoints or multiple UAVs may be arranged to mimic the original image. The generated trajectories on Light Painting mode by physical robots are compared against the ground truth using a data association tool by the Computer Vision Group at TUM and achieved an average RMSE error of 0.4531 metres, while on Multi-Drone Formation the observed positions are estimated from photographs on actual formations, this way the system reached a mean RMSE error of 0.136 metres. © 2023, CC BY.

关键词： Micro air vehicle (MAV)

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GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding

arXiv

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arXiv 2023年

作者： Koledić, Karlo Petrović, Luka Petrović, Ivan Marković, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

Learning-based monocular depth estimation leverages geometric priors present in the training data to enable metric depth perception from a single image, a traditionally ill-posed problem. However, these priors are often specific to a particular domain, leading to limited generalization performance on unseen data. Apart from the well studied environmental domain gap, monocular depth estimation is also sensitive to the domain gap induced by varying camera parameters, an aspect that is often overlooked in current state-of-the-art approaches. This issue is particularly evident in autonomous driving scenarios, where datasets are typically collected with a single vehicle-camera setup, leading to a bias in the training data due to a fixed perspective geometry. In this paper, we challenge this trend and introduce GenDepth, a novel model capable of performing metric depth estimation for arbitrary vehicle-camera setups. To address the lack of data with sufficiently diverse camera parameters, we first create a bespoke synthetic dataset collected with different vehicle-camera systems. Then, we design GenDepth to simultaneously optimize two objectives: (i) equivariance to the camera parameter variations on synthetic data, (ii) transferring the learned equivariance to real-world environmental features using a single real-world dataset with a fixed vehicle-camera system. To achieve this, we propose a novel embedding of camera parameters as the ground plane depth and present a novel architecture that integrates these embeddings with adversarial domain alignment. We validate GenDepth on several autonomous driving datasets, demonstrating its state-of-the-art generalization capability for different vehicle-camera systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Designing a Haptic Interface for Enhanced Non-Verbal Human-Robot Interaction: Integrating Heart and Lung Emotional Feedback 23

Designing a Haptic Interface for Enhanced Non-Verbal Human-R...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Saood, Adnan Liu, Yang Zhang, Heng Tapus, Adriana U2IS Ensta Paris Institut Polytechnique de Paris Paris France Ltci Laboratory Telecom Paris Institut Polytechnique de Paris Paris France Autonomous Systems and Robotics Lab U2IS Ensta Paris Institut Polytechnique de Paris France

ISBN: (纸本)9798350373578

Using non-verbal cues such as breathing and heartbeat signals as media for Human-Robot Interaction (HRI) enables subtle exchanges between humans and robots beyond explicit verbal or visual cues. This study describes the development of a novel pneumatic haptic interface that replicates human breathing and heartbeat animations for a humanoid robot and investigates its application for enhancing non-verbal HRI. The experimental results validate the effectiveness of this haptic device and highlight the significance of non-verbal cues in conveying emotional states. Introducing the haptic interface as a means for delivering involuntary, non-verbal emotional signals significantly enhances human-robot interaction compared to using gestures alone. © 2024 IEEE.

关键词： Human robot interaction

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MOFT: Monocular odometry based on deep depth and careful feature selection and tracking

MOFT: Monocular odometry based on deep depth and careful fea...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Karlo Koledić Igor Cvišić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

autonomous localization in unknown environments is a fundamental problem in many emerging fields and the monocular visual approach offers many advantages, due to being a rich source of information and avoiding comparatively more complicated setups and multisensor calibration. Deep learning opened new venues for monocular odometry yielding not only end-to-end approaches but also hybrid methods combining the well studied geometry with specific deep components. In this paper we propose a monocular odometry that leverages deep depth within a feature based geometrical framework yielding a lightweight frame-to-frame approach with metrically scaled trajectories and state-of-the-art accuracy. The front-end is based on a multihypothesis matcher with perspective correction coupled with deep depth predictions that enables careful feature selection and tracking; especially of ground plane features that are suitable for translation estimation. The back-end is based on point-to-epipolar line minimization for rotation and unit translation estimation, followed by deep depth aided reprojection error minimization for metrically correct translation estimation. Furthermore, we also present a domain shift adaptation approach that allows for generalization over different camera intrinsic and extrinsic setups. The proposed approach is evaluated on the KITTI and KITTI-360 datasets, showing competitive results and in most cases outperforming other state-of-the-art stereo and monocular methods.

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Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method

arXiv

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arXiv 2025年

作者： Zhang, Zhong-Yi Xu, Chen-Yang Zhao, Li-Xuan Hou, Hui-Rang Meng, Qing-Hao Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring, widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depression-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity;the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models. Ablation studies confirmed both modules’ contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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