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A Fully Sparse Detector with Cascade Heads for LiDAR-based 3D Object Detection 13

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3...

引用

13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Junnan Zhang, Gang Hu, Xiaolin Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (纸本)9798331516147

LiDAR-based 3D object detection is a critical component of perception systems in autonomous driving. Existing detectors typically rely on dense feature maps for 3D object prediction. However, as the perception range expands, the computational overhead associated with dense feature maps increases quadratically, creating significant challenges for scaling to long-range detection scenarios. To address this issue, fully sparse detectors have recently been proposed, but they often suffer from suboptimal performance or excessively complex architectures. In this paper, we present CHNet, a fully sparse detector designed to overcome these limitations by introducing a cascade head module, which consists of two sequential heads. By generating coarse object predictions, creating new feature points based on centers of the predicted objects, and aggregating features of the entire object to these new center points, CHNet enhances the qualities of object feature representations, achieving higher detection performance. Notably, CHNet effectively addresses the center feature missing problem in LiDAR-based 3D object detection by adding at most a hundred new feature points, offering a potential avenue for future research. On the widely used Waymo Open and Argoverse2 datasets, CHNet achieves impressive results without any additional embellishments. © 2025 ieee.

关键词： Object recognition

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A Handle-like Antenna for Narrow Band Internet of Things

A Handle-like Antenna for Narrow Band Internet of Things

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2022 International Applied computational Electromagnetics Society symposium, ACES-China 2022

作者： Zhang, Peng Zhang, Lei Zhang, Cunfeng Suzhou Chien-Shiung Institute of Technology School of Artificial Intelligence Suzhou China Changzhou University College of Applied Technology Changzhou China

In this paper, a handle-like antenna for narrowband internet of things (NB-IoT) is presented. The proposed antenna holds two roles, which means it works as a structural handle while transmitting signals simultaneously... 详细信息

ISBN: (纸本)9781665452366

关键词： Internet of things

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Time-frequency varying tissue impedance properties from noxious stimulation protocols 20

Time-frequency varying tissue impedance properties from noxi...

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20th ieee Jubilee World symposium on Applied Machine intelligence and Informatics, SAMI 2022

作者： Ghita, Mihaela Ghita, Maria Copot, Dana Birs, Isabela De Keyser, Robin Ionescu, Clara M. Dysc Research Lab Ghent University Eedt D&C Flanders Make Ghent9052 Belgium Antwerp University Eedt D&C Flanders Make Dysc Research Lab Ghent University Faculty of Medicine and Health Sciences Belgium Ghent University Dysc Research Lab Ghent9052 Belgium Technical University of Cluj-Napoca Department of Automation Romania Technical University of Cluj-Napoca Eedt D&c Flanders Make Department of Automation Romania

ISBN: (纸本)9781665497046

Tissue impedance can be an evaluation factor for noxious stimulation in humans. It has been shown in several studies that bioimpedance can be used as a direct evaluation tool of pain. In this study, we evaluated the changes in perception of different stimuli (mechanical and thermal) applied to subjects in a safe laboratory environment. The measurements are performed with the in-house developed pain monitoring device ANSPEC-PRO, employing impedance evaluation as a function of time and frequency. In this paper the following hypotheses have been investigated: i) the time-frequency response is nonlinear and independent of stimuli type, ii) there are differences in energy between mechanical and thermal-induced pain as indicated by impedance changes. © 2022 ieee.

关键词： Performance evaluation Time-frequency analysis Protocols Pain Mechanical factors Time measurement Frequency measurement

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A Distributed Multi-User Access control Middleware for Critical Applications

A Distributed Multi-User Access Control Middleware for Criti...

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ieee symposium Series on computational intelligence (SSCI)

作者： Alexander Williams Arunava Roy Dipankar Dasgupta Center for Information Assurance University of Memphis

We present a novel access control middleware for distributed multi-user review. The system uses a fuzzy inference system trained on real world access control rules to evaluate and select reviewers as an extension to a more traditional access control system. The method is intended for high security need specific requests, as a supplement to regular access control methods. In this way, it models a multi-person access system common in mechanical controls like missile launches, bank vault opening, and other high criticality domains. The proposed method improves security by increasing the number of compromised users needed to perform an attack, taking advantage of situational awareness of peer users in a system. We evaluate the proposed system with an example implementation based on a real-world organization, and show that the system can be used to effectively implement a secure resource access control system. Our work contributes to the growing body of research into fuzzy-logic access control, ML in access control, and multi-user authentication systems.

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Learning control Policies for Variable Objectives from Offline Data

Learning Control Policies for Variable Objectives from Offli...

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ieee symposium Series on computational intelligence (SSCI)

作者： Marc Weber Phillip Swazinna Daniel Hein Steffen Udluft Volkmar Sterzing Siemens AG Technology Munich Germany

Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is not available. In this paper, we introduce a conceptual extension for model-based policy search methods, called variable objective policy (VOP). With this approach, policies are trained to generalize efficiently over a variety of objectives, which parameterize the reward function. We demonstrate that by altering the objectives passed as input to the policy, users gain the freedom to adjust its behavior or re-balance optimization targets at runtime, without need for collecting additional observation batches or re-training.

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Parameter-Adaptive Paired Offspring Generation for Constrained Large-Scale Multiobjective Optimization Algorithm

Parameter-Adaptive Paired Offspring Generation for Constrain...

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ieee symposium Series on computational intelligence (SSCI)

作者： Haiyue Zhu Qingda Chen Jinliang Ding Xingyi Zhang Hongfeng Wang The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Artificial Intelligence Anhui University Hefei China

There are significant challenges in designing optimization algorithms for constrained large-scale multiobjective optimization problems due to numerous decision variables and constraints. For example, the decision space size exponentially grows with the number of decision variables, and constraints restrict the feasible range, increasing the complexity of the search space. To solve these problems, this paper presents a constrained large-scale multiobjective optimization algorithm based on adaptive paired offspring generation (aPOCEA). Specifically, an adaptive parameter adjustment strategy is proposed to determine the number of solutions in each subpopulation and balance the exploration and exploitation ability of the algorithm, enhancing the convergence speed of aPOCEA. Meanwhile, we propose a parent selection strategy to select high-quality parent solutions, increasing the probability of generating high-quality offspring solutions. Experimental results on ten benchmarks, each with two to three objectives, multiple constraints, and hundreds of decision variables, demonstrate that aPOCEA outperforms other representative optimization algorithms.

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Adaptive control of a Nonlinear System Avoiding State Estimation 20

Adaptive Control of a Nonlinear System Avoiding State Estima...

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20th ieee Jubilee World symposium on Applied Machine intelligence and Informatics, SAMI 2022

作者： Varga, Bence Horvath, Richard Tar, Jozsef K. Óbuda University Doct. School of Appl. Inf. and Appl. Math. Banki Donat Faculty of Mechanical and Safety Engineering Budapest Hungary Óbuda University Bánki Donát Faculty of Mechanical and Safety Engineering Budapest Hungary

ISBN: (纸本)9781665497046

In general a controlled system suffers from imprecise dynamic model which makes accurate trajectory tracking very difficult. Although the dynamic model can be fine tuned e.g., by using soft computing methods, but in order to make precise trajectory tracking some kind of adaptive or robust controller design is required. Traditional Model Predictive or Lyapunov function based controller designs require state estimation for the full set of state variables. However, in many cases it is not possible due to sensor issues. In this case the controlled system can be excited with special functions and the response must be observed and analyzed, which is a very difficult process. To avoid such problems in this paper a Fixed Point Iteration-based adaptive control design is presented in which a simplified affine initial system model is applied which can be a general solution for various control problems. The simulation investigations confirm the expectations with regard to the use of simple affine inverse model for the given physical system. © 2022 ieee.

关键词： Adaptation models Trajectory tracking computational modeling Process control Predictive models control systems State estimation

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Instrumentation and In-mould Data Acquisition System for Injection Moulding Process 4

Instrumentation and In-mould Data Acquisition System for Inj...

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4th International Conference on Artificial intelligence, Robotics and control, AIRC 2023

作者： Bernardo, Rodrigo M. C. Zhiltsova, Tatiana Oliveira, Monica S. A. Neto, Victor Caseiro, Joao Laranjeira, Jorge Andrade-Campos, Antonio Ferreira, Jorge A. F. University of Aveiro Mechanical Engineering Department Aveiro Portugal University of Aveiro TEMA-Centre for Mechanical Engineering and Automation Aveiro Portugal LASI-Intelligent Systems Associate Laboratory Guimaraes4800-058 Portugal CENTIMFE-Technological Centre for the Mouldmaking Special Tooling and Plastic Industries Marinha Grande Portugal Moldit Industria de Moldes S.A. Loureiro Oliveira de Azemeis Portugal

ISBN: (纸本)9798350348248

Developing new tools for industry that lead to a sustainable development is essential. This paper briefly presents the challenges the injection moulding industry faces in manufacturing plastic products and the requirement for moulds to possess specific characteristics to withstand the high pressures and thermal loads imposed on them. Over-sizing moulds to meet these requirements results in elevated production, operation, and logistics costs. To address this challenge, an in-mould monitoring architecture is proposed to measure fundamental mould variables, such as pressure, temperature, vibrations, strain, and displacement. The sensors and the computational architecture required for data acquisition, pre-processing, and storage are described. It also presents a user-friendly front-end architecture example and a user interface (UI) developed using Python. Although preliminary, the results presented here provide evidence of the system's validity. The monitoring system proposed in this paper, may provide a valuable tool to develop novel models and control strategies for the injection moulding industry that could help to reduce mould oversizing, leading to significant economic and environmental benefits. © 2023 ieee.

关键词： Injection molding

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Adaptive Stabilization control for Second-order Strict Feedback System with Quantized Input via Fully Actuated System Method 8

Adaptive Stabilization Control for Second-order Strict Feedb...

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8th International symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Wu, Xueqi Yuan, Junhao Sun, Wei Liaocheng University The School of Mathematics Science Liaocheng China

ISBN: (纸本)9798350380286

This paper focuses on investigating the issue of adaptive fuzzy stabilization control for a specific class of uncertain second-order strict feedback system with quantized input via fully actuated system method. In order to deal with second-order systems directly and efficiently, we take the fully actuated system method. It is unnecessary to convert the second-order system into the first-order system, thereby significantly reducing the computational load. To effectively overcome the challenges posed by quantized input, the decomposition of hysteresis quantizer is used. It can effectively suppress high-frequency noise and deal with power reduction, and has good dynamic response characteristics. Moreover, it can greatly reduce the cost and is suitable for many low-cost, high-performance real-time control systems. To deal with the uncertainty in the system, fuzzy logic system technology is adopted, and the calculation amount is reduced by tunning function to avoid the occurrence of over-parametrization phenomenon and make the controller have good stability. The proposed quantization controller ensures that all signals in the closed-loop system remain bounded, thereby achieving stabilization control. Finally, the effectiveness and feasibility of the proposed method are further demonstrated through simulations on the single-link robot arm model. This paper offers a viable solution to the control issues in systems with uncertainty and quantized input. © 2024 ieee.

关键词： Robotic arms

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RGB-THERMAL BASED PEDESTRIAN DETECTION WITH SINGLE-MODAL AUGMENTATION AND ROI POOLING MULTISCALE FUSION

RGB-THERMAL BASED PEDESTRIAN DETECTION WITH SINGLE-MODAL AUG...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Xiang, Jiajun Gou, Shuiping Li, Ruimin Zheng, Zhihui Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Shaanxi Peoples R China Beijing Aerosp Automat Control Inst Beijing 100039 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

RGB-Thermal based pedestrian detection has received more extensive attention due to the provided detailed information and thermal sensitivity of pedestrians. In this paper, a single-modal feature augmentation network (SMA-Net) is proposed. Firstly, two single-modal branches are trained separately to optimize the feature extraction of each branch in addition to the training of pedestrian detection based on fused features. Secondly, a lightweight ROI pooling multiscale fusion module (PMSF) is proposed to obtain more fine-grained and abundant features, in which pooling features of different scales are integrated by adaptively weighting. Finally, a generative constraint strategy is designed to constrain fusion by minimizing the loss function between the generated fusion image and RGB-Thermal pairs. Experimental result on the challenging dataset KAIST demonstrates that the proposed SMA-Net achieves great performance in terms of accuracy and computational efficiency.

关键词： Pedestrian detection single-modal augmentation ROI pooling multiscale fusion generative constraint multispectral datasets

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