Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough fo...
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ISBN:
(纸本)9798350399462
Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough for its acceptance on the market. Recently, much research has focused on scenario-based methods. However, the number of possible driving scenarios to test is in principle infinite. In this paper, we formalize a learning-based optimization framework to generate corner test-cases, where we take into account the operational design domain. We examine the approach on the case of a feedback control system for automated driving, for which we suggest the design of the objective function expressing the criticality of scenarios. Numerical tests on two logical scenarios of the case study demonstrate that the approach can identify critical scenarios within a limited number of closed-loop experiments.
Leveraging recent developments in black-box riskaware verification, we provide three algorithms that generate probabilistic guarantees on (1) optimality of solutions, (2) recursive feasibility, and (3) maximum control...
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ISBN:
(纸本)9781665491907
Leveraging recent developments in black-box riskaware verification, we provide three algorithms that generate probabilistic guarantees on (1) optimality of solutions, (2) recursive feasibility, and (3) maximum controller runtimes for general nonlinear safety-critical finite-time optimal controllers. These methods forego the usual (perhaps) restrictive assumptions required for typical theoretical guarantees, e.g. terminal set calculation for recursive feasibility in Nonlinear Model Predictive control, or convexification of optimal controllers to ensure optimality. Furthermore, we show that these methods can directly be applied to hardware systems to generate controller guarantees on their respective systems.
Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations...
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ISBN:
(纸本)9798350377712;9798350377705
Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The highlevel utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://***/WYCUniverStar/SCMP.
In industrial positioning systems where rapid response and high-precision are crucial, minor model inaccuracies due to unknown dynamics and identification errors in controller design significantly impede achieving des...
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ISBN:
(纸本)9798350355376;9798350355369
In industrial positioning systems where rapid response and high-precision are crucial, minor model inaccuracies due to unknown dynamics and identification errors in controller design significantly impede achieving desired positioning accuracy. This paper introduces and evaluates a novel, direct data-driven control-based additive feedforward (FF) compensation method, aimed at enhancing precision in positioning while streamlining the design process. The purpose of this additive FF compensation is to attenuate undesirable error responses resulting from modeling errors in the existing model-based FF design. The proposed method enhances control performance by utilizing data-driven prediction of positioning response and optimizing the predicted response. Moreover, this work presents a newly developed design theory for the additive FF controller and highlights its design efficiency. The effectiveness of the proposed approach is substantiated through comprehensive experiments with a galvano scanner in printed circuit board laser drilling applications, demonstrating significant improvements in positioning accuracy and response time.
Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper proposes a monocular event-inertial odom...
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ISBN:
(纸本)9798350377712;9798350377705
Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper proposes a monocular event-inertial odometry incorporating an adaptive decay kernel-based time surface with polarity-aware tracking. We utilize an adaptive decay-based Time Surface to extract texture information from asynchronous events, which adapts to the dynamic characteristics of the event stream and enhances the representation of environmental textures. However, polarity-weighted time surfaces suffer from event polarity shifts during changes in motion direction. To mitigate its adverse effects on feature tracking, we optimize the feature tracking by incorporating an additional polarity-inverted time surface to enhance the robustness. Comparative analysis with visual-inertial and event-inertial odometry methods shows that our approach outperforms state-of-the-art techniques, with competitive results across various datasets.
Distributed systems are critical infrastructures for many enterprises and organizations, yet they also present challenges in fault diagnosis. To improve the efficiency and accuracy of fault diagnosis in distributed sy...
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ISBN:
(纸本)9798350352634;9798350352627
Distributed systems are critical infrastructures for many enterprises and organizations, yet they also present challenges in fault diagnosis. To improve the efficiency and accuracy of fault diagnosis in distributed systems, this paper proposes a fault diagnosis model based on the RF-LGBM algorithm. The model first utilizes LGBM for initial diagnosis and then employs RF to reclassify the two fault types with lower accuracy. The results indicate that the RF-LGBM model achieves an AUC greater than 0.96, effectively diagnosing faults in distributed systems.
The emergence of software-defined vehicles (SDVs) introduces significant challenges in dynamically deploying services with diverse criticality semantics. To address this issue, we present a framework for the dynamic m...
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ISBN:
(纸本)9798350386066;9798350386059
The emergence of software-defined vehicles (SDVs) introduces significant challenges in dynamically deploying services with diverse criticality semantics. To address this issue, we present a framework for the dynamic mapping of mixed-criticality applications (MCAs) onto a mixed-criticality runtime system (MCR) with probabilistic guarantees. We model an SDV service, such as a Docker container, as an MCA and provide an MCR based on a finite-state machine. We present an approach that maps the criticality levels of an MCA to those of the MCR, tracks available resources in the MCR, converts the resource demands of an MCA, and performs admission control to ensure the MCR remains schedulable. This framework enables the reliable and prioritized execution of critical SDV functions while appropriately managing less critical tasks.
The proceedings contain 78 papers. The special focus in this conference is on Hybrid intelligentsystems. The topics include: Text Classification Using Convolution Neural Networks with FastText Embedding;deep Convolut...
ISBN:
(纸本)9783030730499
The proceedings contain 78 papers. The special focus in this conference is on Hybrid intelligentsystems. The topics include: Text Classification Using Convolution Neural Networks with FastText Embedding;deep Convolutional Neural Network Based on Wavelet Transform for Super Image Resolution;optimizing Instance Selection Strategies in Interactive Machine Learning: An Application to Fraud Detection;building IoT Analytics and Machine Learning with Open Source Software for Prediction of Environmental Data;ANFIS-Based Inverse Kinematics and Forward Dynamics of 3 DOF Serial Manipulator;design and Development of intelligent Pesticide Spraying System for Agricultural Robot;solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrid Bi-Level Optimization Model;Caching Mechanisms Evolution in CCN Architectures;SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews;auto-Encoder Based Wavelet and Extreme Learning Machine for Face Recognition;characterizing Antennas’ Radiation Pattern Using Bernoulli Lemniscates;analysis of Metaheuristics Feature Selection Algorithm for Classification;hybrid Genetic Algorithms to Solve the Traveling Salesman Problem;hybridization of Adaboost with Random Forest for Real-Time Prediction of Online Shoppers’ Purchasing Intention;change Detection in Satellite Imagery: A Multi-label Approach Using Convolutional Neural Network;inverse Neural control of a Magnetic Levitation System: Experimental Results;COVID-19 Detection Using Deep Learning;an Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies;an Effective Heart Disease Prediction Model Based on Machine Learning Techniques;optimal Design of Fuzzy controllers Using the Multiverse Optimizer;sparse Wavelet Auto-encoder for Covid-19 Cases Identification;a New Bi-objective Classic Transportation Model Considering Social Justice;emotion Recognition Using Multimodalities.
With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. The system is now able to lever...
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The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flow...
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