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Robust Object Recognition in Open Environments Based on Causal Inference

Robust Object Recognition in Open Environments Based on Caus...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Ruonan Liu Shuai Wu Ping Kong Wei Xie Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Engineering Hainan University Hainan China Department of Automation Shanghai Jiaotong University Shanghai China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

the rapid development of artificial intelligence technology has drawn widespread attention to the problem of target recognition in open environments. Traditional target recognition methods are easily affected by environmental changes and interference noise, leading to insufficient recognition accuracy and robustness. this paper proposes a robust target recognition method based on causal inference, aiming to improve the robust performance of image recognition models in open environments. the method first extracts features from images, analyzes the impact of noise features such as the environment in image features, and derives the essential feature properties of the target. then, the causal relationship is introduced into the target recognition, and by analyzing the relationship between the environment, the target, and the observation data, the features of the target are combined with the observation data to achieve accurate and stable recognition of the target. the proposed method has achieved good target recognition effects on multiple open datasets, and the experimental results show that the algorithm can effectively solve the problem of poor robustness of the target recognition model in open environments, providing strong support for practical applications. through the application of this method, the stability and robustness of the target recognition model can be significantly improved, which has important practical application value.

关键词： Accuracy Image recognition automation Target recognition Noise Interference Feature extraction Robustness Stability analysis Robots

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A Field-Weakening Scheme with Predictive Current Error for PMSM Modulated Model Predictive Control

A Field-Weakening Scheme with Predictive Current Error for P...

引用

international conference on Electrical Machines and Systems (ICEMS)

作者： Qinghua Dong Yong Yu Bo Wang Minghe Tian Dianguo Xu School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

In this paper, a field-weakening scheme on modulated model predictive control (M2PC) using predictive current error (PCE) margin is proposed for permanent magnet synchronous motor (PMSM). Compared with traditional finite-control-set MPC, M2PC has small steady-state current harmonics benefitting from the synthesis of multiple vectors. However, due to the limit of inverter output capacity and the discreteness of M2PC, the operation range is difficult to extend the field-weakening region and ensure dynamic performance. To address this problem, a predictive current error (PCE) margin with a weight factor is designed to improve the dynamic performance in field-weakening operation, in which the PCE margin is derived from the voltage mapping. the experimental results are eventually conducted for validating the proposed methods.

关键词： Predictive models Permanent magnet motors Harmonic analysis Permanent magnets Inverters Trajectory Steady-state

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Domain Adaptation for Degraded Remote Scene Classification

Domain Adaptation for Degraded Remote Scene Classification

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jianfei Yang Hailin Chen Yuecong Xu Ziji Shi Ruikang Luo Lihua Xie Rong Su School of Electical and Electronic Engineering Nanyang Technological University Singapore Spasee Technology Pte. Ltd. Singapore

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Remote scene classification serves a vital role in many applications. However, satellite images are often blurred and degraded due to aerosol scattering under fog, haze, and other weather conditions, reducing the image contrast and color fidelity. State-of-the-art remote sensing classification models building upon convolutional neural networks (CNNs) are mostly trained on annotated datasets of clear satellite images. When applied to blurred images, they will suffer a great degradation in performance. To address this problem, we adopt the domain adaptation algorithm TADA and propose Transferable Attention enhanced Adversarial Adaptation Network (TA 3 N), which utilizes annotated data in clear images by applying knowledge transferring from clear image domain to blurred image domain. Our TA 3 N first integrates spatial attention to focus on salient areas which are discriminative and transferable. In addition, domain discriminator and adversarial training via gradient reversal layer are used to minimize the discrepancies in extracted features from clear and degraded domains. We synthesize degraded remote scene classification dataset SSI based on FoHIS model. Experiments on degraded SSI showed that TA 3 N significantly outperforms baseline and other state-of-the-art domain adaptation methods.

关键词： Feature extraction Remote sensing Satellites Task analysis Adaptation models Training Meteorology

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Interactive Context-Comparative Model for Text Matching

Interactive Context-Comparative Model for Text Matching

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Zhuozhang Zou Wei Yang Longlong Pang Chen Liang Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Most of the existing text matching models are based on the interactive network and use cross-attention mechanism to capture the semantic links between two texts, and have made outstanding achievements. However, the current methods generally do not pay enough attention to the context information of the text and ignore the importance of comparing these features. To solve this problem, this paper proposes an interactive context-comparative model for text matching (ICCM). First, the Bi-directional Long Short-Term Memory network is used to encode the word vector. then the key information of related words between different texts is aligned through the cross-attention mechanism, and the key information in their contexts is focused through the context-attention mechanism. the two kinds of information are compared using comparison functions, and the convolutional neural network is used to aggregate the features after splicing. Finally, the results of text matching are predicted. Testing on LCQMC dataset shows that the accuracy rate is 85.5%. the experimental result and analysis prove that the proposed model is superior to the existing main text matching methods and has certain effectiveness.

关键词： Measurement Computational modeling Splicing Semantics Predictive models Encoding Convolutional neural networks

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Linear Parameter Varying Model Predictive-Based Controller for Trajectory Tracking of High-Fidelity Autonomous Vehicle

Linear Parameter Varying Model Predictive-Based Controller f...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Kanani Kulwa-Juma Kanani B K Tiwari Tae-wan Kim Department of Naval Architecture and Ocean Engineering Seoul National University Seoul South Korea Department of Advance Computer Science and Engineering Vignan University Guntur India

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the tracking control of an unmanned autonomous vehicle (UA V) is one of the key factors determining the performance and effectiveness of autonomous navigation. A controller based on Linear Parameter Varying - Model Predictive Control (LPV - MPC) is designed to track the desired trajectory of the UAV. the Linear Parameter Varying (LPV) modeling and control strategy allows the representation of the UAV nonlinear complex model in a linear-like manner which can be used in a linear Model Predictive Control (MPC). the main advantage of the LPV -MPC technique is its ability to improve tracking solutions very close to nonlinear MPC but with significantly low computational costs, enabling LPV-MPC to be used for real-time operations. the simulation results of this paper show that the designed LPV-MPC performs well in tracking the desired trajectory through each waypoint. Furthermore, experiments with different numerical integration methods have been investigated, especially Euler's method, and the Runge-Kutta method. the results showed that the Runge-Kutta method performed better with smaller horizon periods, with minimum tracking errors and shorter elapsed time.

关键词： Trajectory tracking Simulation Urban areas Predictive models Autonomous aerial vehicles Real-time systems Numerical models

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Use of Yolo Algorithm for Traffic Sign Detection in Autonomous Vehicles and Improvement Using Data Replication methods

Use of Yolo Algorithm for Traffic Sign Detection in Autonomo...

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Serkan Budak Firat Bozkaya Mustafa Yasin Akmaz Şükrücan Tiğlioğlu Cansel Boynukara Okan Kazanci Zülal Hilal Yildiz Budak Akif Durdu Cemil Sungur Electrical Electronics Engineering Konya Technical University Konya Türkiye Computer Engineering Konya Technical University Konya Türkiye Electrical Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Türkiye

Autonomous vehicles use many technologies and methods to detect and act on surrounding objects. the most common among these technologies is an algorithm called YOLO (You Only Look Once). this algorithm quickly detects objects in an image and classifies these objects accurately. this study examines the use of the YOLO algorithm for signage detection in autonomous vehicles and how this algorithm can be improved. First of all, the basic principles and working mechanisms of the YOLO algorithm are explained. then, it is explained in detail how this algorithm can be used for plate detection in autonomous vehicles. Various models were trained using the YOLO algorithm and the data set created with real data, and the trained models were tested on real-time systems. Finally, suggestions for the improvement of the YOLO algorithm are presented and how this algorithm can be improved further in the future is discussed.

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Human-Led Robotic Transportation of Elastic Objects with Adaptive Control

Human-Led Robotic Transportation of Elastic Objects with Ada...

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international conference on robotics and automation Engineering (ICRAE)

作者： Zhenxi Cui Jiewen Lai Bo Lu Yi Guo Henry K. Chu School of Computing and Engineering University of West London London United Kingdom Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China School of Mechanical and Electrical Engineering Soochow University Suzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken New Jersey United States Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

the perception and transportation of soft objects are critical tasks in daily life, and recent sensor advancements enable robots to safely perform these tasks in human-shared environments. this study advances elastic linear object transportation by introducing a collaborative framework where a human leads, providing task-specific instructions to guide the robot. Unlike conventional methods focused on fixed object manipulation, our approach dynamically controls and converges feature points of the elastic object into desired configurations during transportation. We developed an online model estimation technique using a least-squares optimization algorithm, with an exponential forgetting mechanism to adaptively update the model under disturbances from sensors and human interaction. Validated in experiments with a 6-DOF robot and depth camera, our method demonstrated robustness across varied scenarios, achieving faster convergence and reduced positional fluctuation compared to conventional gradient descent approaches.

关键词： Deformable models Adaptation models Shape Robot kinematics Transportation Estimation Robot sensing systems Stability analysis Robots thermal stability

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Comparative Analysis of Listwise Reranking with Large Language models in Limited-Resource Language Contexts

Comparative Analysis of Listwise Reranking with Large Langua...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yanxin Shen Lun Wang Chuanqi Shi Shaoshuai Du Yiyi Tao Yixian Shen Hang Zhang Simon Fraser University Canada Duke University North Carolina USA University of California San Diego California USA University of Amsterdam Amsterdam Netherlands Johns Hopkins University Maryland USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Large Language models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. this study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages. We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingual contexts. Results indicate that these LLMs significantly outperform traditional baseline methods such as BM25-DT in most evaluation metrics, particularly in nDCG@10 and MRR@100. these findings highlight the potential of LLMs in enhancing reranking tasks for low-resource languages and offer insights into cost-effective solutions.

关键词： Measurement Translation Costs Large language models Robots Context modeling

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Vision for Cognitive Systems: A New Compound Concept Connecting Natural Scenes with Cognitive models

Vision for Cognitive Systems: A New Compound Concept Connect...

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IEEE international conference on Industrial Informatics (INDIN)

作者： Peter Michael Goebel Markus Vincze Vienna University슠of슠Technology Austria Automation and Control Institute Vision for Robotics Laboratory University of Technology Vienna Austria

Vision, as a key perceptional capability for cognitive systems relates to rather difficult problems -such as visual object recognition, representation, categorization, and scene understanding. State-of-the-art solutions, using object appearance based models, already reached certain maturity. they achieve excellent recognition performance and provide learning structures that are subsequently utilized for object recognition and tracking. However, in context of object topology understanding for cognitive tasks, these methods cannot be directly compared with human performance, because it is obvious that appearance based methods do not contribute to understanding of structures in 3D. Research findings from infant psychology and animal investigation give evidence for using hierarchical models of object representation, based on image primitives e.g. such as edges, corners, shading or homogeneity of object colors. It is the objective of this paper to present an approach based on both, findings from biological studies and cognitive science, as enablers for autonomous cognitive investigation of natural scenes and their understanding. We present the architecture of a compound cognitive framework and its first behavioral level with the implementation of a vision model of the mammalian striate visual cortex in five layers. the proposed implementation is exemplified with an object similar to the Necker cube.

关键词： Machine vision Joining processes Layout Object recognition Biological system modeling Topology Humans Animal structures Pediatrics Psychology

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the Comprehensive Evaluation Method for the Visibility Ability of ATCT Based on BP Network

The Comprehensive Evaluation Method for The Visibility Abili...

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

作者： Bo Wang Xiaofei Peng Jiaquan Ye Gang Su Chenyu Liu The Second Research Institute of Civil Aviation Administration of China Chengdu China Civil Aviation Flight University of China Guanghan China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Airport Traffic Control Tower (ATCT) is one of the most important facilities at civil aviation airports. the location and height of ATCT will directly affect the controller's observation and judgment. In order to evaluate and compare the visibility capability of different tower construction schemes, a comprehensive evaluation method is set up. this method includes building an index system of obligatory and preference indicators, forming calculation methods for each indicator, and establishing a comprehensive evaluation model. In the evaluation model, two networks have been constructed based on Coefficient Strategy (CS) and Level Strategy (LS) respectively. Selecting a certain airport in East China as a case study, CS-BP and LS-BP network models of 5-10-1 in topology have been set up after 12000 typical samples for training respectively. the research results show that LS-BP network has the smaller Mean Squared Error (MSE) and greater regression coefficient compared to CS-BP network. And the results of CS-BP were similar to Traditional Strategy (TS). therefore, selecting LS-BP network to evaluate calculated indicators of given schemes and considering that scheme 3 was best. the entire method can serve as a reference for decision-making by air traffic control unit.

关键词： Training Service robots Network topology Poles and towers Evaluation models Lighting Traffic control Airports Topology Indexes

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