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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Kai-Tai Song Shih-Wei Chiu Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

In this paper, we present a hand gesture-based robot path generation system using mixed reality (MR) for interactive robot programming. A hand-gesture recognition scheme is proposed to recognize specific gestures for path generation and modification. A user can intuitively plan a virtual path by hand gestures in the actual working space and send the planned path to the robot controller to execute through the MR glasses. A human-machine interface is designed to allow a user to assign the required task and send control commands to the robot. To ensure the robot can accurately move along the planned path, a calibration procedure is designed to establish the relationship between the MR system and robot navigation system. The proposed method has been realized and verified using a TM5 cobot and HoloLens II. The experimental results show that the robot navigation error is within 2mm using the developed MR-based robot programming system.

关键词： Presses Navigation Mixed reality Virtual environments Glass Manipulators Path planning

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Modeling and Simulation of Two Axes Gimbal Using Fuzzy control

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computers, Materials & Continua 2022年第7期72卷 93-107页

作者： Ayman A.Aly Mohamed O.Elhabib Bassem F.Felemban B.Saleh Dac-Nhuong Le Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Control and Mechatronics Division School of Electrical EngineeringUTMJohor Bahru81300Malaysia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and *** main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling *** paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/***,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB *** performance of both controllers was evaluated and tested for different types of input *** simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.

关键词： Gimbal system self-tuning fuzzy proportional-integral-derivative(PID)control cross coupling

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Adversarial Training with Maximal Coding Rate Reduction

Adversarial Training with Maximal Coding Rate Reduction

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Asilomar Conference on Signals, Systems & computers

作者： Hsiang-Yu Chu Hongbo Zhao Markus Flierl Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples, which is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, we propose in this paper two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction (MCR 2 ). We evaluate the performance of different adversarial training methods by comparing the clean accuracy and adversarial accuracy. It is shown that adversarial training with the MCR 2 loss function yields a more robust network than the traditional adversarial training method. In our experiments, adversarial accuracies are improved by up to 10%. The two loss functions are discussed by using a model.

关键词： Training Deep learning Hands Accuracy Image coding Computational modeling Robustness Data models Encoding Convolutional neural networks

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Predictive Cruise control for Connected Autonomous Vehicles Considering Communication Delay in Mixed Traffic

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IFAC-PapersOnLine 2024年第29期58卷 172-177页

作者： Xinyu Li Jin Zhang Jiaqi Xue Lin Qi Wenbin Tang Xiaohong Jiao Institute of Electrical Engineering Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei Province China

The stochastic behaviors of human-driven vehicles (HDVs) and the communication delay of Vehicle-to-Everything (V2X) technique present challenges to the advancement of connected autonomous vehicles (CAVs) in mixed traffic comprising CAVs, uncertain numbers of HDVs and connected HDVs (CHDVs). Consequently, a predictive cruise control strategy based on V2X communication is proposed for CAVs in a mixed platoon. Firstly, a long-short-term memory (LSTM) network combined with a conditional linear Gaussian (CLG) model is adopted for speed prediction of the preceding vehicle considering the communication delay. Subsequently, the CAV's speed planning is conducted for passing continuous signalized intersections without stopping. Then, the planned speed is tracked in energy-efficient, safe, and comfortable manners under a stochastic model predictive control (SMPC) framework. The simulation results validate the effectiveness and advantages of the proposed strategy.

关键词： Connected autonomous vehicles Mixed traffic Communication delay Predictive cruise control

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Multi-view stereo based on adaptive expansion propagation

Multi-view stereo based on adaptive expansion propagation

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computer Information Science and Application Technology (CISAT), International Conference on

作者： Zixuan Nie Haohai Fu School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Through computer vision and image processing techniques, a set of images from a scene can be reconstructed in 3D to recover a 3D model of the scene, in which dense reconstruction is a crucial part, and most existing algorithms cannot effectively recover weak texture regions in dense reconstruction. In response to this issue, this article proposes a hypothesis propagation strategy based on adaptive extension. In the hypothesis propagation stage of PatchMatch MVS, an adaptive mechanism for region propagation is introduced, and an update counter is set to regulate the expansion of the sampling area. This ensures that there are enough excellent hypotheses for sampling in this algorithm iteration, and the quality of sampling hypotheses is not limited by fixed sampling pixel positions, thereby improving the probability of excellent hypotheses being sampled in the sampling space. It is demonstrated in the ETH3D dataset that our method reconstructs models with higher integrity, and fewer voids and smoother surfaces in the reconstruction of weakly textured regions.

关键词： Point cloud compression Solid modeling Adaptation models Surface reconstruction Information science Three-dimensional displays Computational modeling Surface texture Image reconstruction Optimization

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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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Modeling the Efficiency of an Edge Coupler with a Si3N4-based Taper 25

Modeling the Efficiency of an Edge Coupler with a Si3N4-base...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Barbin, Evgenij S. Nesterenko, Tamara G. Koleda, Aleksej N. Myrzakhmetov, Ayan Kulinich, Ivan V. Mokhovikov, Denis M. Laboratory of microsystems engineering Tomsk State University of Control Systems and Radioelectronics Laboratory of Radiophotonics V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia Laboratory of microsystems engineering Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Photonic Integrated Circuits Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Laboratory of Intelligent Computer Systems V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia

ISBN: (纸本)9798350389234

The article studies the efficiency of optical radiation input/output between a photonic integrated circuit (PIC) and an edge-coupled tapered silicon nitride waveguide with dimensions 350 nm x 850 nm at a wavelength of 1550 nm. The work determines the losses by radiation for transverse electric and transverse magnetic modes at different taper width and input mode diameter for direct (edge width more than 850 nm) and inverted tapers (edge width less than 850 nm). The results show that the admissible width of the inverted taper edge lies within 150–300 nm with losses by input/output of up to 2 dB and low sensitivity to optical polarization. For the direct taper, the highest input efficiency (losses amounted to 3-6 dB) belongs to the optical fibers with a beam waist diameter of 2–3 μm and taper edge width of 2–3 μm. For both types of edge couplers, the effect of the waist diameter of lensed fiber is more important: the spread should be within 150 nm. © 2024 IEEE.

关键词： Silicon nitride

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Keeping Eyes on the Road: Understanding Driver Attention and Its Role in Safe Driving 9

Keeping Eyes on the Road: Understanding Driver Attention and...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Fiani, Francesca Ponzi, Valerio Russo, Samuele Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy

Monitoring the driver’s attention is an important task to maintain the driver’s safety. The estimation of the driver’s gaze direction can help us to evaluate if the drivers are not focusing their attention on the street. For an evaluation of this type, comparing the inside view and outside scenery of the vehicle is essential, therefore we decided to create a specific dataset for this task. In this work, we realize a machine-learning-oriented approach to driver’s attention evaluation using a coupled visual perception system. By analyzing the road and the driver’s gaze simultaneously it is possible to understand if the driver is looking at the traffic signs detected. We evaluate if a determined Region Of Interest (ROI) contains a road sign or not through YOLOv8. © 2023 Copyright for this paper by its authors.

关键词： Machine learning

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3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-controlled GWO-AOA

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computer Systems Science & engineering 2023年第6期45卷 2483-2503页

作者： K.Sreelakshmy Himanshu Gupta Om Prakash Verma Kapil Kumar Abdelhamied A.Ateya Naglaa F.Soliman Department of Instrumentation and Control Engineering Dr.B.R.Ambedkar National Institute of Technology JalandharPunjab144027India Department of Industrial and Production Engineering Dr.B.R.Ambedkar National Institute of Technology JalandharPunjab144027India Department of Electronics and Communication Engineering Zagazig UniversityZagazigSharqia44519Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous *** increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past ***,not a single optimization algorithms can solve all kind of optimization problem ***,there is dire need to integrate metaheuristic for general *** address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional *** performance of the developed QGA has been compared against the various *** simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

关键词： Archimedes optimisation algorithm grey wolf optimisation path planning reinforcement learning unmanned aerial vehicles

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Advancing Visual Decoding in EEG: Enhancing Spatial Density in Surface EEG for Decoding Color Perception 3

Advancing Visual Decoding in EEG: Enhancing Spatial Density ...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Schreiner, Leonhard Sieghartsleitner, Sebastian La Rosa, Matteo Tanackovic, Slobodan Pretl, Harald Colamarino, Emma Guger, Christoph Institute for Integrated Circuits Johannes Kepler University G.tec medical engineering GmbH Linz Austria Institute of Computational Perception Johannes Kepler University G.tec medical engineering GmbH Linz Austria Sapienza University of Rome Rome Italy G.tec medical engineering GmbH Schiedlberg Austria Institute for Integrated Circuits Johannes Kepler University Linz Austria Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350378009

Investigating the early visual cortex's sensitivity to fundamental visual characteristics such as orientation, contrast, spatial frequency, and colors is a broad field in neural engineering. Based on previous studies showing improved results with higher EEG electrode density, this work aims to advance our understanding of how the brain processes visual information by using ultra-high-density EEG (uHD EEG) surface scalp recordings to capture more detailed spatial and temporal information. In this study, the focus lies on decoding color and black-and-white stimuli by densely covering the occipital region of three healthy subjects with 512 channels, using flexible surface electrode grids. Visual evoked potentials (VEPs) were analyzed to investigate the temporal dynamics of neural activity, and topographical maps were generated on 3D reconstructed head models of all subjects for enhanced spatial visualization. Additionally, a 4-class classification model was employed to assess the discriminative power of extracted visual evoked potentials, focusing on different colors (red, green, blue) and black-and-white stimuli. The results indicate consistent VEP timing and amplitudes across all subjects, with discernible differences observed among colors and black-and-white stimuli. Additionally, distinct focal areas of activation for each stimulus type within the occipital region of the subjects have been identified. Classification results revealed a grand average classification accuracy of 87.2% for all subjects in the 4-class problem, achieved through averaging ten trials of VEPs. © 2024 IEEE.

关键词： Color vision

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