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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Farzad Rezazadeh Emad Olfatbakhsh Andreas Kroll Department of Measurement & Control University of Kassel Kassel Germany Mercedes-Benz Tech Innovation GmbH Ulm Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

Data-driven modeling is essential for predicting the behavior of complex industrial systems, but such systems often challenge machine learning models due to their intricacies. Ensemble learning enhances predictive performance by combining multiple base learners (BLs), where diversity among BLs is a critical factor. Existing diversity measures typically rely on general statistical metrics not tailored for ensemble learning and do not address the challenge of determining an appropriate ensemble size. This study introduces the Weighted Sign Disagreement Metric (WSDM) and its extension, which are methods specifically designed for measuring diversity among BLs in stacking ensemble regression tasks. Both methods incorporate the concept of sign diversity by considering the signs of BL predictions relative to target values. These methods introduce an adjustable hyperparameter to balance the trade-off between accuracy and diversity among BLs. The extension of WSDM specifically addresses the challenge of determining an appropriate ensemble size, enhancing the flexibility and effectiveness of ensemble design. Experimental results on six synthetic datasets demonstrate that WSDM and its extension significantly improve predictive accuracy and generalization compared to traditional diversity measures. By addressing the deficiencies of existing methods and exploring the trade-off between accuracy and diversity, these methods offer a robust framework for heterogeneous stacking ensemble regression.

关键词： Data-driven modeling Weight measurement Accuracy Diversity reception Stacking Diversity methods Predictive models Size measurement Ensemble learning Synthetic data

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ieee Joint 19th International symposium on computational intelligence and Informatics and 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics, CINTI-MACRo 2019 - Proceedings

IEEE Joint 19th International Symposium on Computational Int...

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19th ieee Joint International symposium on computational intelligence and Informatics and 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics, CINTI-MACRo 2019 - Proceedings

ISBN: (纸本)9781728156255

The proceedings contain 36 papers. The topics discussed include: mixed-order Sugeno model to predict the resultant force in the milling process for honeycomb sandwich;stoichiometry control of the two gas reactive sputtering process;demand side management electric energy consumption data processing architectures within internet of things context;development of an LQ regulator for longitudinal vehicle control of an automated vehicle;parking lot exploration strategy;a semi-automated generation of entity relationship diagram based on morphosyntactic tagging from the requirements written in a Serbian natural language;data analytics for health-care risk predictions based on ensemble classifiers and subjective projection;and data analytics for health-care risk predictions based on ensemble classifiers and subjective projection.

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Safety-Driven AMR End-to-End Navigation Framework Based on Sparse Sensor Human Behavior Prediction

Safety-Driven AMR End-to-End Navigation Framework Based on S...

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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Fuhua Jia Kai Yang Junlin Xiao Tuo Hu Xiaoying Yang Adam Rushworth Heng Yu Tianxiang Cui Department of Mechanical Materials and Manufacturing University of Nottingham Ningbo China Ningbo China Control System Laboratory University of Nottingham Ningbo China Ningbo China Ningbo Innovation Center Zhejiang University School of Computer Science University of Nottingham Ningbo China Ningbo China

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

Autonomous Mobile Robots (AMRs) have garnered significant attention and widespread application due to their operational efficiency. However, in human-centered environments, such as warehouses, manufacturing facilities, and public spaces, AMRs face the dual challenge of navigating autonomously while perceiving and adapting to human movements to ensure safety. This work proposes a novel safety-driven end-to-end navigation framework for AMRs that processes navigation commands and sparse data collected by onboard 2D LiDAR and IMU sensors to directly control the robot's velocity. The framework comprises two key modules: a human estimation module and a trajectory generation module. The human estimation module employs an LSTM-based architecture to estimate human positions and movement trends from sparse sensor data, generating a dynamic obstacle map. Based on these predictions, a genetic algorithm -based trajectory generation module creates safe-driven dynamic trajectories subject to dynamic constraints. This approach enables accurate forecasting of pedestrian movements, allowing the AMR to effectively avoid dynamic human obstacles, thereby enhancing its safety and practicality in real-world environments. Our contributions are threefold: dynamic human detection and prediction from sparse onboard sensor data, safety-driven dynamic trajectory planning, and extensive real-world experiments demonstrating feasibility and robustness of the proposed framework.

关键词： Navigation Market research Robustness Real-time systems Trajectory Sensors Safety Mobile robots Long short term memory Genetic algorithms

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ieee Joint 22nd International symposium on computational intelligence and Informatics and 8th International Conference on Recent Achievements in Mechatronics, automation, Computer Science and Robotics, CINTI-MACRo 2022 - Proceedings

IEEE Joint 22nd International Symposium on Computational Int...

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Joint 22nd ieee International symposium on computational intelligence and Informatics and 8th ieee International Conference on Recent Achievements in Mechatronics, automation, Computer Science and Robotics, CINTI-MACRo 2022

ISBN: (纸本)9798350398823

The proceedings contain 64 papers. The topics discussed include: noise-based logic versus quantum supremacy;modeling cerebro-vascular autoregulation after postural change perturbations;a review on emotion based harmful speech detection using machine learning;comparison of automatic question generation techniques;transparent slide detection and gripper design for slide transport by robotic arm;proposing a new model for estimation of oil rate passing through wellhead chokes in an Iranian heavy oil field;real-time multi-user 3D visualization software in medicine;evaluation of the use of an intelligent system in the calibration of a refined car-following model;inertial sensor-based movement classification with dimension reduction based on feature aggregation;and on the simulation of lower order control strategies for higher order systems.

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TinyML for Computation-aware Transformer-based Anomaly Detection in Internal Combustion Systems

TinyML for Computation-aware Transformer-based Anomaly Detec...

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International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Iman Sharifirad Jalil Boudjadar Peter Gorm Larsen Aarhus University Aarhus Denmark

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This paper proposes a highly accurate, computation-efficient anomaly detection for an industrial internal combustion engine using a Transformer model. The proposed anomaly detection model enables identifying anomaly occurrences and quan-tifying how far the abnormality deviates from a normal state. This leads to informed decisions for efficient mitigation in the control loop of the combustion engine. To achieve computation-awareness while maintaining considerable accuracy, we have applied Pruning, a Tiny ML technique, to reduce the computation cost of the proposed Transformer model. The capacity analysis of the proposed model shows that, while trained on anomalies from a few combustion cylinders, our model can recognize and quantify anomalies from the entire set of cylinders. Furthermore, the analysis results demonstrated that Pruning reduced the computation cost by 8.4% and memory usage by up to 39% while the model's prediction performance barely decreased. Our transformer-based model to identify and quantify anomalies is deployable on the resource-constrained platform of the use case.

关键词： Analytical models Accuracy Costs computational modeling Tiny machine learning Transformers Combustion Real-time systems Data models Anomaly detection

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Procedural Content Generator for Generating a 3D Graphical Representation of the River Model

Procedural Content Generator for Generating a 3D Graphical R...

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International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Alžbetá Sklenáriková Branislav Madoš Norbert Ádám Heidar Khorshidiyeh Peter Poprík Lukáš Tornaščík Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Ko išce Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This work deals with procedural content generation, specializing in content generation in the fields of computer graphics and computer games. Even more precisely, it deals with the procedural generation of three-dimensional polygonal surface models of rivers. In the introductory part, the paper provides an overview of related procedural content generation principles using noise generating functions and more complex simulation techniques. Then, it presents a within this work designed algorithm for the procedural generation of a river model by creating its bed using the designed concept of control tiles, which allow controlling the direction and shape of the river. The generator's goal is to provide a tool for creating river models that can be integrated into the environment of 3D computer games.

关键词： Video games Solid modeling Procedural generation Three-dimensional displays Shape computational modeling Computer graphics Generators Real-time systems Rivers

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data

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ieee Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital Department of Pathology Wuhan University Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 ieee.

关键词： Gene expression

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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ieee Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 ieee.

关键词： Memristors

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3D Self-triggered-organized Communication Topology Based UAV Swarm Consensus System with Distributed Extended State Observer

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ieee Transactions on Network Science and Engineering 2025年

作者： Xiao, Hanzhen Yang, Yang Yu, Dengxiu Lai, Guanyu Chen, C. L. Philip Guangdong University of Technology School of Automation Guangzhou China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Pazhou Laboratory Guangzhou510335 China

In this work, to address the communication topology alteration caused by unit failure or loss in the multi-UAV cooperation control system, a 3D self-triggered-organized topology (3D-STOT) based UAV swarm consensus system with distributed extended state observer (DESO) is developed. First, we propose a novel 3D self-organized topology (3D-SOT) generation method that automatically constructs adequate topology networks from swarm position information without manual intervention, with a computational complexity of O(n4). This network maintains connectivity even when a certain number of UAV failures or losses occur. Next, a 3D-STOT updating strategy is introduced to automatically update the topology based on real-time location under predefined trigger conditions. This updating strategy enables the 3D-SOT generating method to flexibly adapt to swarms of varying scales and significantly enhances the swarm system's ability to handle unexpected situations. Additionally, DESO not only supports real-time 3D-STOT updates but also assists the double-loop Sliding Mode controller (SMC) in achieving consensus formation. Finally, the effectiveness of the proposed method is verified by simulation experiments. © 2013 ieee.

关键词： Sliding mode control

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Proceedings of the 2007 ieee International symposium on computational intelligence in Robotics and automation, CIRA 2007

Proceedings of the 2007 IEEE International Symposium on Comp...

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2007 ieee International symposium on computational intelligence in Robotics and automation, CIRA 2007

ISBN: (纸本)1424407907

The proceedings contain 96 papers. The topics discussed include: bio-inspired model of robot spatial cognition: topological place recognition and target learning;autonomous configuration of transportation network by artificial pheromone system;optimization of source identification algorithm derived from moth-inspired plume tracing strategies;neural fields for controlling formation of multiple robots;an iterative fuzzy segmentation algorithm for recognizing an odor source in near shore ocean environments;a sensor calibration methodology for evidence theoretic unmanned ground vehicle localization;facial identity and expression recognition by using active appearance model with efficient second order minimization and neural networks;investigation of performance of distributed complex systems using information-theoretic means and genetic algorithms;and vision aided stabilization and the development of a quad-rotor micro UAV.

关键词： Cognitive systems

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