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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68829 条记录，以下是691-700 订阅

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A Robust Adaptive Terminal Sliding Mode Control for Lane-keeping in Road Vehicles

A Robust Adaptive Terminal Sliding Mode Control for Lane-kee...

引用

2023 international conference on Advanced Mechatronic Systems, ICAMechS 2023

作者： Ge, Zhikang Wang, Zhuo Bai, Xiaoping Wang, Xiaoxiong Man, Zhihong Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Swinburne University of Technology Faculty of Science Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350307474

In this paper, a novel adaptive terminal sliding-mode control (ATSMC) method is developed for road vehicles with uncertain dynamics. It is shown that the designed adaptive laws can recursively update the controller parameters based on the estimation values of the unknown parameter bound in the Lyapunov sense, and then the lateral position error can be driven to zero in a finite time. In comparison to the conventional sliding-mode-based vehicle lateral control method, the proposed ATSMC not only ensures fast error convergence, but also eliminates the requirement for known bounded information in controller design. Simulation results are presented to demonstrate the good performance of the proposed ATSMC scheme. © 2023 IEEE.

关键词： Sliding mode control

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Simulation and analysis of path planning algorithm for campus patrol robot based on ant colony algorithm

Simulation and analysis of path planning algorithm for campu...

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international conference on computer Vision, Robotics, and automation Engineering, CRAE 2024

作者： Liu, Yanwei Yao, Xue Yunnan Land and Resources Vocational College Kunming China Education Robotics and Innovative Application Engineering Research Center of Yunnan Provincial Department of Education Kunming China School of Materials Science and Engineering Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510682283

As a bionic optimization algorithm, ant colony algorithm searches for the optimal path by simulating the foraging behavior of ants. It has the advantages of strong global search ability and easy implementation, and can be used to solve the path planning problem of patrol robot in campus environment. This paper first constructs the MATLAB simulation model of campus patrol environment, and then designs the path planning method based on ant colony algorithm. The effectiveness and practicability of the algorithm are verified by simulation analysis. The simulation results show that the ant colony algorithm can effectively plan the optimal path for the patrol robot, improve the patrol efficiency and reduce the patrol time. This research provides an efficient and intelligent solution for campus security patrol, and has important practical value. © 2024 SPIE.

关键词： MATLAB

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Transfer learning with deep models for small food datasets

Transfer learning with deep models for small food datasets

引用

2023 international conference on automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Cai, Longzheng Tang, Longmei Lim, Shuyun School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Faculty of Business and Technology Unitar International University Selangor Petaling Jaya47301 Malaysia

ISBN: (纸本)9781510667662

Deep Convolutional Neural Networks (DCNNs) have been used in food image classification, segmentation, gradient identification and many other applications. There are many small segments in food industry, and it is very expensive and time-consuming to build a large training dataset for each of them. Transfer learning can play a role in this aspect by reusing knowledge learned from large datasets, like ImageNet, to target tasks where only small datasets are available. In this work, we conduct an empirical study on the transfer learning capability of DCNNs pre-trained with ImageNet on four small food datasets. We find that models perform better on ImageNet dataset also have better transfer performance for small target datasets;In two transfer learning approaches, fine-turning performs better than feature extraction;Data augmentation helps in transfer learning. © 2023 SPIE.

关键词： Convolutional neural networks

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Attribute Feature Fusion Network for Pedestrian Detection and Re-identification 5

Attribute Feature Fusion Network for Pedestrian Detection an...

引用

5th international conference on Robotics and computer Vision, ICRCV 2023

作者： Jiang, Yu Liu, Qian Liu, Meng-Ting School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing China

ISBN: (纸本)9798350326369

Person re-identification is presented to retrieve specific pedestrian targets across monitoring devices. In this paper, we propose an attribute feature fusion network (AFFNet) for pedestrian detection and re-identification, which integrates pedestrian detection, person re-identification and attribute recognition into a deep network for end-to-end multi-task learning. In AFFNet, we design the identity and attribute branches to separately learn the identity and attribute fusion features of pedestrians. The identity features focus on the overall pedestrian appearance, while the attribute fusion features pay attention to local regions of the pedestrian. These two types of features are concatenated for the person re-identification task, which complement each other to provide a more comprehensive description of pedestrians. Experimental results on two commonly used person re-identification datasets including DukeMTMC-reID and Market-1501 demonstrate the effectiveness of proposed AFFNet. © 2023 IEEE.

关键词： Object detection

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Measurement of Truck Carriage Size based on Lidar 4

Measurement of Truck Carriage Size based on Lidar

引用

4th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2023

作者： Wu, Hongen Wei, Hailiang Wang, Yanzhuang Wang, Pengju Zhang, Kewei Shandong University of Science and Technology College of Mechanical and Electronic Engineering Shandong Qingdao266590 China

ISBN: (纸本)9781510666412

In order to solve the problem of measuring the size of the truck carriage and positioning the vehicle, a lidar-based non-contact dimensional measurement device for truck carriage is designed. The measuring device is composed of a vertical two-dimensional laser radar, a four-degree-of-freedom manipulator, a sliding rail and an industrial computer. The point cloud information of the length and width of the carriage is obtained by the laser radar. The linear features are extracted by the Probabilistic Hough Transform Algorithm, and then the outline segments of the carriage are extracted according to the prior knowledge, so as to measure the length, width and height of the carriage and position the vehicle. The measurement result of the carriage model shows that the device is characterized by small measurement error, high measurement efficiency and simple structure and so on. © 2023 SPIE.

关键词： Optical radar

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Gender Recognition using Deep Learning Methodology 2

Gender Recognition using Deep Learning Methodology

引用

2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Prajapati, Avantika Patel, Nagendra Singh, Upendra Department of Computer science and engineering Rewa Institute of Technology M.P. Rewa India Department of Information Technology Shri G. S. Institute of Technology and Science Indore India

ISBN: (纸本)9798350340235

Identifying a person's gender from their facial features is a prominent subject in computer vision. While humans can intuitively perform this task, it poses substantial challenges for machines. The research study associated with this article details a gender classification method anchored in face recognition feature vectors. Initially, input images undergo face recognition and preprocessing, leading to a standardized face format. Subsequently, the data is processed by the face recognition model to extract feature vectors, encapsulating facial attributes within a specific feature domain. Ultimately, machine learning techniques are deployed to classify these derived vectors. This research introduces a sophisticated gender classification system harnessing VGG Face, Deep Belief Networks, and shifted filter responses. Models such as CNN, VGG 16, Resnet50, Inception v3, and EfficientNet were explored, with Resnet 152 emerging as the most effective. Remarkably, the Resnet 152 models exhibit a 9% enhancement over leading competitors, and they also demonstrate superior resilience to anomalies compared to previous iterations. © 2023 IEEE.

关键词： CNN Convolutional Neural Systems EfficientNet FERET Gender classification Inception v3 LFW Resnet50 VGG 16

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Mathematical Model of Asset and Liability Management in the Presence of Normative and Internal Constraints 5

Mathematical Model of Asset and Liability Management in the ...

引用

5th international conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Aleksey Pilnik, Nikolay FRC CSC of RAS Dept. of Mathematical Modelling of Economic Systems MIPT Moscow Russia FRC CSC RAS Dept. of Mathematical Modelling of Economic Systems Moscow Russia Moscow Institute of Physics and Technology MIPT-School of Applied Mathematics and Computer Science Moscow Russia

ISBN: (纸本)9798350309126

In this paper we present the mathematical formulation of the problem of planning the Asset Liability Management if the Presence of Normative and Internal Constraints. As a nonlinear optimal control problem, it has specifics due to the state constraints. The maximum principle involves verification of regularity property in order to apply computational methods. We reformulate the problem as the problem with convex objective function and linear, but mixed system of constraints. This enables application of optimization methods to compute the optimal controls, and study the dependence of controls on the parameters that describe the external environments and the management objective. © 2023 IEEE.

关键词： Optimal control systems

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A Study on Occlusion Removal technology Using Integral Imaging with Semantic Segmentation and Predictive Labeling 23

A Study on Occlusion Removal Technology Using Integral Imagi...

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23rd international conference on Control, automation and Systems, ICCAS 2023

作者： Kim, Hyun-Woo Huh, Jong-Hoon Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan Kyushu Institute of Technology Department of Physics and Information Technology Fukuoka Iizuka820-8502 Japan

ISBN: (纸本)9788993215274

In this paper, we propose an improved occluded object visualization method using semantic segmentation and predictive labeling based on integral imaging (InIm) technology. InIm is a passive 3D visualization technique that can generate 3D information through elemental images with different perspectives of information about an object and, it can be used to remove occlusion. However, occlusion pixels in elemental images can degrade the quality of reconstructed images. Our proposed method can remove occlusion and visualize the object of interest. In addition, we propose a predictive labeling method for reducing processing time, which can generate accurate 3D object information without occlusion. Therefore, when the proposed method is commercialized, it is expected to contribute to increasing the safety of autonomous driving technology. © 2023 ICROS.

关键词： Semantic Segmentation

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Learning Design Strategies for Optimizing User Behaviour Towards automation: Architecting Quality Interactions from Concept to Prototype 5th

Learning Design Strategies for Optimizing User Behaviour Tow...

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5th international conference on HCI in Mobility, Transport and Automotive Systems (MobiTAS) Held as Part of the 25th international conference on Human-computer Interaction (HCII)

作者： Mbelekani, Naomi Y. Bengler, Klaus TUM Sch Engn & Design Chair Ergon Boltzmannstr 15 D-85748 Garching Germany

ISBN: (纸本)9783031356773;9783031356780

Automated vehicles are equipped with systems for task operation and furnished with complex sensor-based systems for detecting and avoiding crashes. However, because technology is unruly and prone to error due to its low maturity level, the human is required to be an active member and collaborator in the operation of the vehicle. As a result, users are required to be constantly vigilant for instances where automation may fail and request to intervene. Yet, notably, some users have a tendency to perceive automation as a holy grail and partake in undesirable user behaviours linked to misuse, over trust, and even high-level complacency, etc. Consequently, industry faces safety criticalities, resulting in either low to high-level risk taking behaviours. Thus, focus should be on taming users' knowledge to fit the level of automated driving, trucking, flying, farming systems' capabilities and limitations by investing in exceptionally ergonomic-inspired strategies that promote desirable user behaviours, such as the intended use of automation and interaction with its human-machine interfaces (HMIs) over the sequence of time. As a result, N = 20 air and ground vehicle industry experts views on training to use and learning strategies for optimizing safety and risk-free human-automation interaction and use (HAI/U) were considered. The paper devises ergonomically enthused learning design strategies in support of deprogramming-risky behaviours and reprogramming-safe taking behaviours towards automation, by bearing in mind long-term effects.

关键词： Automated vehicles Learning design strategies Long-term effects Safe and risk-free behaviour automation-induced effects Experts views

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud Computing (CC) is a massive breakthrough in Information technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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