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CCDepth: A Lightweight Self-Supervised Depth Estimation Network with Enhanced Interpretability 27

CCDepth: A Lightweight Self-Supervised Depth Estimation Netw...

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27th IEEE international conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Xi Xue, Yaru Jia, Shaocheng Pei, Xin Beijing China The University of Hong Kong Department of Civil Engineering Hong Kong Hong Kong Beijing National Research Center for Information Science and Technology Tsinghua University Department of Automation Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798331505929

Self-supervised depth estimation, which solely requires monocular image sequence as input, has become increasingly popular and promising in recent years. Current research primarily focuses on enhancing the prediction accuracy of the models. However, the excessive number of parameters impedes the universal deployment of the model on edge devices. Moreover, the emerging neural networks, being black-box models, are difficult to analyze, leading to challenges in understanding the rationales for performance improvements. To mitigate these issues, this study proposes a novel hybrid self-supervised depth estimation network, CCDepth, comprising convolutional neural networks (CNNs) and the white-box CRATE (Coding RAte reduction TransformEr) network. This novel network uses CNNs and the CRATE modules to extract local and global information in images, respectively, thereby boosting learning efficiency and reducing model size. Furthermore, incorporating the CRATE modules into the network enables a mathematically interpretable process in capturing global features. Extensive experiments on the KITTI dataset indicate that the proposed CCDepth network can achieve performance comparable with those state-of-the-art methods, while the model size has been significantly reduced. In addition, a series of quantitative and qualitative analyses on the inner features in the CCDepth network further confirm the effectiveness of the proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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Review of deep learning based target detection algorithm research 6

Review of deep learning based target detection algorithm res...

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6th international conference on Mechatronics and Intelligent robotics, ICMIR 2022

作者： Ye, Rong Shao, Jianfei Shao, Jianlong Ji, Jianjie Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510656635

With the rapid development of the times, big data has started to affect all aspects of our life, and more and more products in industries are becoming carriers of big data. For example, CNNs with more implicit layers are proposed to meet our needs, although they have a more complex network structure. Compared with traditional machine learning, it has more powerful feature learning and feature representation ability, which can better meet the needs in the era of big data. Since its introduction, deep learning has achieved remarkable results in several large-scale recognition tasks in the field of computer vision. This paper presents a review of target detection methods based on a survey of domestic and international literature on the subject. Firstly, the current state of research in the field of target detection is introduced. Next, the development and rise of deep learning and convolutional neural networks are briefly introduced, and the basic structure of convolutional neural networks, convolutional feature extraction, and pooling operations are outlined. The analysis and discussion of deep learning-based target detection algorithms are focused, and the current shortcomings are pointed out. Finally, the applications of deep learning-based target detection and the future development directions are summarized. © 2022 SPIE.

关键词： Object detection

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Method for Determining the Digital Twin Structure of Distributed Open Sources

Method for Determining the Digital Twin Structure of Distrib...

引用

2022 international Russian automation conference, RusAutoCon 2022

作者： Antonov, V.V. Voyakovskaya, Y.S. Suvorova, V.A. Usatu Faculty of Computer Science and Robotics Ufa Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

For the material of this article, open sources are publicly available information posted on the Internet for repeated and free use in the form of machine-readable systematized data, in a format that allows their automated processing. Any of these sources is considered as a structure with iterative nesting of content, formalized by a number of stable rules. The mathematical description of the rules for the formulation of open source content objects constitutes a knowledge base containing statistics of various properties and mappings that bring such content closer to its digital counterpart, regardless of its nature. The digital twin, in this case, can be represented as a set of local systems with feedback subsystems connected by categorical relations at the level of functorial dependence. The conducted research has established the possibility of forming the structures of the digital twin of an open source, which makes it possible to increase the importance of the analysis of the qualifications of open sources. Turning to the same type of content, it becomes necessary to formalize the structures of connections between its objects, which can be described in the language of graph theory and as virtual objects of category theory. The formalized open source assumes preliminary iterations on the graph structure of the main content objects, as well as objects related to them. The subject area is an open source, which has some property of stable connections between the objects of research. © 2022 IEEE.

关键词： Iterative methods

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Human-Robot Collaborative Multi-Agent Path Planning using Monte Carlo Tree Search and Social Reward Sources

Human-Robot Collaborative Multi-Agent Path Planning using Mo...

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IEEE international conference on robotics and automation (ICRA)

作者： Dalmasso, Marc Garrell, Anais Enrique Dominguez, Jose Jimenez, Pablo Sanfeliu, Alberto Inst Robot & Informat Ind CSIC UPC Llorens Artigas 4-6 Barcelona 08028 Spain

ISBN: (纸本)9781728190778

The collaboration between humans and robots in an object search task requires the achievement of shared plans obtained from communicating and negotiating. In this work, we assume that the robot computes, as a first step, a multiagent plan for both itself and the human. Then, both plans are submitted to human scrutiny, who either agrees or modifies it forcing the robot to adapt its own restrictions or preferences. This process is repeated along the search task as many times as required by the human. Our planner is based on a decentralized variant of Monte Carlo Tree Search (MCTS), with one robot and one human as agents. Moreover, our algorithm allows the robot and the human to optimize their own actions by maintaining a probability distribution over the plans in a joint-action space. The method allows an objective function definition over action sequences, it assumes intermittent communication, it is anytime and suitable for on-line replanning. To test it, we have developed a human-robot communication mobile phone interface. Validation is provided by real-life search experiments of a Parcheesi token in an urban space, including also an acceptability study.

关键词： Monte Carlo methods Collaboration Human-robot interaction Search problems Linear programming Mobile handsets Probability distribution

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Knowledge Concept Recommendation Model for MOOCs with Local Sub-graph Embedding

Knowledge Concept Recommendation Model for MOOCs with Local ...

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automation, robotics and Computer Engineering (ICARCE), international conference on

作者： ChengCheng Ju Yi Zhu ChenYu Wang Department of Computer Science and Technology Jiangsu Normal University Xuzhou China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665475495

Massive open online courses (MOOCs) play a crucial role in modern education by providing users with largescale open online learning platforms. Substantial research has been conducted to reduce user learning blindness and improve user experience, particularly in personalized course recommendations based on graph neural networks. However, these efforts have focused primarily on fixed or homogeneous graphs, are vulnerable to data sparsity problems, and are difficult to scale. This study proposes to overcome this limitation using graph convolution on local subgraphs combined with an extended matrix factorization model. First, the proposed method decomposes the heterogeneous graph into multiple meta-path-based subgraphs and combines random wandering sampling methods to capture complex semantic relationships between entities while sampling nodes' influence-rich neighborhoods. Next, the attention mechanism adaptively fuses the contextual information of different subgraphs for a more comprehensive construction of user preferences. Experiments on publicly available MOOCs datasets reveal that the proposed model outperforms other benchmark models and is highly scalable while alleviating the data sparsity problem.

关键词： Knowledge engineering Adaptation models Electronic learning Computational modeling Semantics Sampling methods Data models

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A Dual-Direction Convolution Mixed-Attention Network for Facial Expression Recognition

A Dual-Direction Convolution Mixed-Attention Network for Fac...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Yang Li Yukun Shao Wenkang Liu Department of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Facial expression recognition has remained a focal point due to its vital applications, such as medical diagnosis and human-computer interaction. However, the complexity of human emotion leads to a wide diversity of external expressions. Small inter-class differences and large intra-class differences are also significant challenges for effectively identifying facial expressions. In this paper, we establish a Dual-Direction Convolution Mixed-Attention Network (DDC-MAN) to enhance the recognition effect of CNNs. The DDC-MAN framework consists of a CNN backbone, a dual-direction convolution (DDC) module, a mixed-attention dual-direction feature fusion (MADDFF) module, and a multi-head spatial-channel attention (MHSCA) module. Specifically, our model first employs ResNet18 as the backbone for extracting facial features. The DDC module is then incorporated to capture facial texture information from two directions more effectively. Additionally, the MADDFF module is proposed to find key areas from both single and mixed attention across width and height directions. Finally, the MHSCA module is introduced to perform spatial and channel attention based on these key areas. Experimental results demonstrate that our DDC-MAN approach yields impressive improvements over state-of-the-art methods on the AffectNet and RAF-DB datasets.

关键词： Human computer interaction Control engineering Convolution Service robots Face recognition Feature extraction Convolutional neural networks Medical diagnosis Data mining Facial features

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GoNoGo: An Efficient LLM-Based Multi-agent System for Streamlining Automotive Software Release Decision-Making 36th

GoNoGo: An Efficient LLM-Based Multi-agent System for Stream...

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36th international conference on Testing Software and Systems

作者： Khoee, Arsham Gholamzadeh Yu, Yinan Feldt, Robert Freimanis, Andris Rhodin, Patrick Andersson Parthasarathy, Dhasarathy Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden Volvo Grp Gothenburg Sweden

ISBN: (纸本)9783031808883;9783031808890

Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their labor-intensive nature. Large Language models (LLMs) present a promising solution to these challenges. However, their application generally demands multiple rounds of human-driven prompt engineering, which limits their practical deployment, particularly for industrial end-users who need reliable and efficient results. In this paper, we propose GoNoGo, an LLM agent system designed to streamline automotive software deployment while meeting both functional requirements and practical industrial constraints. Unlike previous systems, GoNoGo is specifically tailored to address domain-specific and risk-sensitive systems. We evaluate GoNoGo's performance across different task difficulties using zero-shot and few-shot examples taken from industrial practice. Our results show that GoNoGo achieves a 100% success rate for tasks up to Level 2 difficulty with 3-shot examples, and maintains high performance even for more complex tasks. We find that GoNoGo effectively automates decision-making for simpler tasks, significantly reducing the need for manual intervention. In summary, GoNoGo represents an efficient and user-friendly LLM-based solution currently employed in our industrial partner's company to assist with software release decision-making, supporting more informed and timely decisions in the release process for risk-sensitive vehicle systems.

关键词： LLMs LLM-based Multi-agent Software Release Assistant Table Analysis automation Risk-sensitive Systems

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15th EAI international conference on automation and Control in Theory and Practice, ARTEP 2023

15th EAI International Conference on Automation and Control ...

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15th EAI international conference on automation and Control in Theory and Practice, ARTEP 2023

ISBN: (纸本)9783031319662

The proceedings contain 27 papers. The special focus in this conference is on . The topics include: Theoretical Basis of Application of the Parameter of Dielectric Permeability of Hydrocarbon Feedstock during its Processing;description of the Basic Tools of Augmented Reality in the Design of Computer models in Unity;preparation of an Assembly in Virtual Reality;FDM Printing and Parameter Setting in the KISSlicer Program;application of Composite Materials Natural Fibers in Automotive Industry – Short Review;Increasing the Sustainability of Manufacturing Processes in the Conditions of SMEs Enterprises by Predicting, Their Information Intensity;a View on the Digitization of Small and Medium-Sized Enterprises;process Modularity Impact on Manufacturing Lead Time in Mass Customization;testing and Verification of the Proposed Method for the Assembly Process Based on the Human–Robot Collaboration;obstacle-Resistant Wireless Strain Gauge Complex for Automated Monitoring of the Steel Structures Condition;using Mathematical Modelling for Experimental Obtaining of Initial Data when Creating Digital Twins of Complex Technical Products;Calibration of Panasonic TM-2000 Welding Robot Using Simulation Software;modelling of Technical Condition Control of Heavy Loaded Gears Teeth;automated Subsystem for Cutting Modes Calculations;Improving the Performance of Grey-Box Model of 3-DOF Compliant Robotic Arm with Fluidic Muscles;development of a Virtual Bench for Simulation and Research of a Mobile Robot Motor;monte Carlo Simulation in a Digital Calibration Certificate;Control of Temperature Field in the Secondary Cooling Zone of Continuous Casting Process Based on Robust Internal Model Control (IMC) Approach;comparison of Surface Roughness Measured by Contact and Noncontact methods.

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Leveraging IoT for Enhanced Industrial automation: Innovations and Applications

Leveraging IoT for Enhanced Industrial Automation: Innovatio...

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Advances in Computation, Communication and Information Technology (ICAICCIT), international conference on

作者： Hiranmaye Sarpana Chandu

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The IoT is a relatively new and exciting field in computer science that is expanding rapidly worldwide. Background: IoT has seen substantial growth due to advancements in wireless networking, enhanced sensors, and IoT platforms, as well as the convergence of cloud and edge computing. Objective: The paper aims to assess the innovations in IoT, evaluate their applications in industrial automation, and identify key challenges and opportunities for further research. methods: The review covers recent advancements, such as the application of Federated Learning for enhancing IoT security, the use of large-scale blockchains for data integrity, and the integration of digital twins and containerisation in robotics. Key areas of industrial application, including predictive maintenance, supply chain management, and real-time environmental control, are explored. Results: The findings highlight significant improvements in cost savings, reliability, and safety through IoT in industrial automation. However, challenges such as network generalization, real-world applicability, and efficient resource usage remain. Conclusion: More effective and dependable systems might be the result of future research that tackles these issues and increases the influence of IoT technology in industrial automation.

关键词： Wireless communication Wireless sensor networks Technological innovation automation Supply chain management Service robots Supply chains Sensors Predictive maintenance Edge computing

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Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain models

Real-Time Adaptive Safety-Critical Control with Gaussian Pro...

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IEEE international conference on robotics and automation (ICRA)

作者： Yu Zhang Long Wen Xiangtong Yao Zhenshan Bing Linghuan Kong Wei He Alois Knoll Department of Informatics Technical University of Munich Germany Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model’s online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from newly samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both simulation platform and a real-world 7-DOF robot.

关键词： Adaptation models Adaptive systems Uncertainty Computational modeling Gaussian processes Real-time systems Safety

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