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Enhancing doctor-patient communication in surgical explanations: Designing effective facial expressions and gestures for animated physician characters

Enhancing doctor-patient communication in surgical explanati...

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作者： Kim, Hwang Youn Ali, Ghazanfar Hwang, Jae-In AI-Robotics University of Science and Technology Daejeon Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of

Paying close attention to facial expressions, gestures, and communication techniques is essential when creating animated physician characters that are realistic and captivating when describing surgical procedures. This paper emphasizes the integration of appropriate emotions, co-speech gestures when medical experts explain the medical procedure, and designing animated characters. We can achieve healthy doctor-patient relationships and improvement of patients' understanding by depicting these components truthfully. We suggest two critical approaches to developing virtual medical experts by incorporating these elements. First, doctors can generate the contents of the surgical procedure with a virtual doctor. Second, patients can listen to the surgical procedure described by the virtual doctor and ask if they have any questions. Our system helps patients by considering their psychology and adding medical professionals' opinions. These improvements ensure the animated virtual agent is comforting, reassuring, and emotionally supportive. Through a user study, we evaluated our hypothesis and gained insight into improvements. © 2024 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons Ltd.

关键词： Design

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Path Planning of Autonomous Underwater Vehicle Departing from Port Based on Improved Deep Q-Network 6

Path Planning of Autonomous Underwater Vehicle Departing fro...

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Li, Chen Er, Meng Joo Liu, Tianhe Jiang, Guohong Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798350371253

This paper presents a path planning scheme for autonomous underwater vehicle (AUV) in port environment based on improved Deep Q-Network (DQN) algorithm. First, port map is gridded and binarized, and port area is separated from other areas according to port shape. Secondly, in order to enable the agent to leave port area, the range of agent exploration randomness is increased in port area. Finally, to solve the problem that the traditional DQN algorithm is difficult to make the agent find the target point after leaving port area quickly, the reward function of DQN is improved. Simulation results verify the effectiveness and efficiency of the proposed scheme. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Path Following Control of Underactuated AUV Based on Improved Model Predictive Control 6

Path Following Control of Underactuated AUV Based on Improve...

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Wang, Siqi Er, Meng Joo Liu, Tianhe Gong, Huibin Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798350371253

In this paper, an improved model predictive control (MPC) method using line-of-sight (LOS) for precise path following control is proposed. First, simplify the motion model of the underactuation five degree of freedom (DOF) autonomous underwater vehicle (AUV), Yunfan-04. The LOS guidance method provides precise heading instructions based on the line of sight position between the AUV and target. The MPC algorithm is used to generate an optimal control strategy based on the dynamic model of AUV to control the following motion of AUV. In order to optimize the path following performance, the problem formula is transformed into an optimization problem with constraints, and the optimal control input is solved. Simulation experiments validate our approach. The LOS and MPC-based method outper-forms traditional methods in accuracy and robustness, achieving stable and precise path following. This work provides valuable insights for developing AUV control systems. © 2023 IEEE.

关键词： Model predictive control

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A Modular Training Approach for Multi-Robot Cooperative Search in Dynamic Environments 10

A Modular Training Approach for Multi-Robot Cooperative Sear...

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10th Indian Control Conference, ICC 2024

作者： Dasgaonkar, Yogesh Sharma, Radhe Shyam Samant, Raj Nayan Centre for Artificial Intelligence and Robotics Indian Institute of technology Mandi175005 India Nvidia Graphics Private Limited Maharashtra Mumbai400093 India

ISBN: (纸本)9798331517212

In a multi-robot system, two or more robots are trained to cooperate in their actions to complete a given task faster and with a higher probability of success than in a single-robot system. State-Of-The-Art (SOTA) cooperative multi-robot training methods are of two types: (a) Algorithmic: Greedy, Genetic, and (b) Machine Learning (ML): Regression, Reinforcement Learning (RL), Deep RL (DRL), and Inverse RL (IRL). Existing literature shows they perform well when the environmental conditions are well-known and static, for instance, in a car body part assembling task. To test the performance of the SOTA under dynamic conditions, we simulate a 3D fire disaster environment in a real-world urban city building with humans trapped inside it. We model the fire dynamics such that the intensity and spread rate of fire, smoke, and blocked paths increase non-linearly. The trapped humans have a high risk of losing their lives if not searched within a deadline. We state this problem as the Multi-robot Cooperative Search in Dynamic Environments (MCSDE) problem. When we applied the SOTA approaches to train multi-robots to search for human lives in this simulated environment, they failed to search for all human lives within a deadline and lacked computational efficiency. To address the issue of multi-robot training in MCSDE, we present a modular training approach. In this approach, we train each robot locally based on their local perceptions. Our efforts in this research are to uncover these perceptions’ geometric locality properties. When fused with another robot’s perception, each perception reveals the true behaviour of the environment’s dynamic. Each robot becomes self-aware of global dynamics by training from its local perceptions. The proposed modular approach requires just one-fourth of the computation than the RL or DL-based training approaches and performs far superior to the algorithmic approaches. © 2024 IEEE.

关键词： Multipurpose robots

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EventAlign: LiDAR-Event Camera Calibration with Event Alignment Loss 27

EventAlign: LiDAR-Event Camera Calibration with Event Alignm...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Dong, Pengfei Hai, Renwei Shen, Yanqing Zhu, Ziyu Chen, Shitao Xin, Jingmin Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Shaanxi Xi'an710049 China Shunan Academy of Artificial Intelligence Zhejiang Ningbo China

ISBN: (纸本)9798331505929

In recent years, event cameras, as a type of bio-inspired sensor, have become increasingly important in the fields of robotics and autonomous driving. Given that events inherently lack depth information, a multi-sensor solution is applied to combine events and LiDAR scans, and its performance highly depends on precise calibration between different sensors. However, due to the distinctive representation of event cameras, which only capture dynamic objects without texture structure, most previous calibration methods for event cameras have complex setups and controlled lighting conditions. In this paper, we propose a novel extrinsic calibration method by simulating events with LiDAR, which does not require any equipment beyond the sensors themselves. Specifically, we first extract LiDAR events from the continuous raw point clouds. Then a clustering algorithm is applied to refine this event data and identify motion centroids. Finally, a two-step optimization process is proposed: an initial rough calibration based on these centroids and a subsequent fine calibration achieved through nearest neighbor matching between LiDAR events and camera events. The experimental results validate that our algorithm achieves high accuracy and robustness in simulations and real-world experiments. © 2024 IEEE.

关键词： Nearest neighbor search

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Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

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Advances in Manufacturing 2024年第2期12卷 227-251页

作者： Si-Xiao Gao Hui Liu Jun Ota Key Laboratory of Traffic Safety on Track of Ministry of Education Institute of Artificial Intelligence and Robotics(IAIR)School of Traffic and Transportation EngineeringCentral South UniversityChangsha410075People’s Republic of China Research into Artifacts Center for Engineering School of EngineeringThe University of TokyoTokyoJapan

Currently,simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing *** allocation problems have been solved previously to satisfy economic requirements;however,owing to the progress of green manufacturing,energy conservation and environmental protection have become increasingly ***,an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems,subject to the total buffer capacity,total service rate,and predefined energy *** energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression *** former algorithm searches for Pareto-optimal solutions of sufficient *** latter algorithm builds prediction models to rapidly calculate the throughput,energy consumption,and energy *** examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported ***,the prediction models can rapidly evaluate manufacturing systems with sufficient *** study benefits the multi-objective optimization of green manufacturing systems.

关键词： Energy-efficient allocation Multi-objective optimization Energy efficiency Energy consumption Machine learning

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SparallaxMPC: High-Speed Model Predictive Control Solver with High Sparsity and Parallelism

SparallaxMPC: High-Speed Model Predictive Control Solver wit...

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2024 China Automation Congress, CAC 2024

作者： Lei, ZhengXing Deng, Xiaodong Liu, Longjun Jiang, Caigui National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (纸本)9798350368604

Model Predictive Control (MPC) is one of the most popular methods used in control problem. However, MPC is computationally demanding with limited use of sparse and parallel implementations. We present SparallaxMPC, a high-speed MPC solver with parallel and sparse representation. Our approach is based on sparse matrix representation and leverages the structure of the MPC problem for efficiency. We demonstrate the effectiveness of SparallaxMPC by benchmarking it against the state-of-the-art solver CasADi, achieving more than a 3x acceleration. © 2024 IEEE.

关键词： Predictive control systems

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Practical Nonlinear Model Predictive Control for Piezoelectric Actuators Based on an Echo State Network 42

Practical Nonlinear Model Predictive Control for Piezoelectr...

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42nd Chinese Control Conference, CCC 2023

作者： Zhao, Fei Wu, Yinan Liu, Huawang Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Tianjin China

ISBN: (纸本)9789887581543

The piezoelectric actuator (PEA) is widely used to realize high precise motion of the micro-systems. However, the inherent nonlinear property such as hysteresis, creep, thermal drift and vibration impairs the overall performance and leads to the system instability. In this paper, a practical nonlinear model predictive control (PNMPC) approach based on an echo state network is proposed for the displacement tracking of PEAs. The proposed method has the advantage of omitting the common building process of an inversion model for hysteresis compensation, which avoids the problem of model inversion and the inversion imprecision. Specifically, an echo state network (ESN) is adopted to model the nonlinear dynamics of the PEA system. On this basis, the PNMPC method is designed via utilizing the linearized ESN model, which reduces the computational complexity of the optimization problem caused by nonlinear model predictive control. Finally, the tracking performance of the proposed controller is verified through experimental results. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Piezoelectric actuators

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An LLM-Assisted Framework for Synthetic Power Network Graph Generation 7

An LLM-Assisted Framework for Synthetic Power Network Graph ...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Song, Yulin Yan, Han Sun, Chenxi Huang, Jianwei The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798350366945

Due to privacy and security concerns surrounding actual power system structure data, many studies have focused on generating simulated power grids and providing corresponding datasets. However, existing methods and datasets often struggle with effective visualization and lack diverse power grid scenarios. To address these challenges, we propose a large language model-assisted workflow for small-scale synthetic power grid generation, verification, and visualization. Moreover, by taking into account the variety of node types and connection relationships inherent in power networks, our approach aims to enhance understanding of structural characteristics through detailed feature representation. We leverage the large language model to enrich the generated power grid structure data, uncovering potential internal structures. Furthermore, we employ advanced visualization techniques to generate power structure images, facilitating analysis by human experts and supporting potential learning tasks. Our results demonstrate that the generated power grid structures align well with the properties of real-world networks, and the visual outputs effectively highlight the intricacies of these structures. The code and examples are available at https://***/SYLLABLE604/PG-generator. © 2024 IEEE.

关键词： data generation Large language model power system visualization

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D2S: Towards Efficient Sparse 3D Object Detection via Dense to Sparse Knowledge Distillation

D2S: Towards Efficient Sparse 3D Object Detection via Dense ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yuqi Liu, Longjun Gao, Yingke Zhang, Haonan Li, Haoteng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Beijing Institute of Control Engineering China Academy of Space Technology Beijing China

ISBN: (纸本)9798350368741

LiDAR-based 3D object detection is widely used in high-level autonomous driving schemes. However, the cumbersome modules in most 3D detectors lead to substantial computational overhead. Despite knowledge distillation (KD) is an effective approach for compressing models, previous methods cannot be extended to the dense-to-sparse paradigm. To this end, we propose a simple yet effective Dense to Sparse Knowledge Distillation (D2S) framework for accelerating 3D detectors. Firstly, to compensate for the difference in predicted location between dense and sparse detectors, we introduce a lightweight feature diffusion (FeaD) module for spreading important features. Secondly, to achieve high performance, we propose a dual-stream distillation scheme to transfer knowledge. In this scheme, we align both of the feature and category prediction between distillation pairs at important positions. Extensive experiments on KITTI and Waymo Open Dataset demonstrate the effectiveness of our method. For example, on KITTI dataset, the sparse detector we obtained surpasses VoxelNeXt with around 2.0× fewer parameters and 1.6× fewer FLOPs. © 2025 IEEE.

关键词： Feature Diffusion Knowledge Distillation Model Compression Sparse 3D Object Detection

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