Article
Engineering, Biomedical
Matthew R. Short, Daniel Ludvig, Emek Baris Kucuktabak, Yue Wen, Lorenzo Vianello, Eric J. Perreault, Levi Hargrove, Kevin Lynch, Jose L. Pons
Summary: This study investigated the effects of dyadic interaction during an ankle tracking task. The results showed that increasing connection stiffness improved tracking performance, with a greater advantage for the worse partner in difficult connection conditions. Through modeling analysis, it was found that this improvement was likely due to a cancellation of tracking errors between partners.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Mianlun Zheng, Danyong Zhao, Jernej Barbic
Summary: Haptics is crucial in training users for assembling mechanical components, with the need for efficient collision detection and six-DoF haptic rendering acknowledged. This study explores how to enhance haptic rendering for maximizing virtual assembly training efficiency, proposing various visual and haptic guidance strategies. Results suggest that combining haptic rendering with visual animation-based guidance is most effective, while continuous forces, nudging, anti-forces and motion indicator cues were found to be less effective.
IEEE TRANSACTIONS ON HAPTICS
(2021)
Article
Computer Science, Information Systems
Hojun Cha, Amit Bhardwaj, Seungmoon Choi
Summary: This paper proposes an improved data-driven method for modeling viscoelastic deformable objects, with greatly improved computational efficiency. By using fractional derivatives and regression forests for data-interpolation models, accurate modeling with reduced costs is achieved.
Article
Computer Science, Software Engineering
Florian Berton, Fabien Grzeskowiak, Alexandre Bonneau, Alberto Jovane, Marco Aggravi, Ludovic Hoyet, Anne-Helene Olivier, Claudio Pacchierotti, Julien Pettre
Summary: This article explores the use of wearable haptics in virtual crowd navigation and finds that providing haptic feedback improves the realism of interaction and encourages participants to actively avoid collisions. However, there is a significant after-effect in users' behavior when haptic rendering is disabled again, although haptic feedback does not have a significant impact on users' sense of presence and embodiment.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Engineering, Electrical & Electronic
Tatiana Duyun, Ivan Duyun, Petr Kabalyants, Larisa Rybak
Summary: This paper presents modern research in developing a model and real prototype of an automotive driving simulator based on the Gough-Stewart platform. The geometric parameters were optimized using the PSO algorithm, and virtual prototypes were created using MSC Adams. The paper showcases a prototype of the driving simulator and specialized equipment with an integrated system of virtual 3D models of real terrain.
Article
Computer Science, Cybernetics
Sergio Portoles Diez, Emmanuel Vander Poorten, Dominiek Reynaerts, Yasuyoshi Yokokohji
Summary: This paper introduces a new technology for simulating surface exploration tasks in virtual reality, focusing on the 'in-contact sliding' phase. By utilizing two novel control approaches, the realistic feel of sliding over a virtual surface has been achieved. The user experiments confirmed the feasibility and the effectiveness of the new rendering schemes, indicating that combining both rendering methods could provide an even more realistic experience.
IEEE TRANSACTIONS ON HAPTICS
(2021)
Article
Computer Science, Information Systems
Yucheng Li, Fei Wang, Liangze Tao, Juan Wu
Summary: This paper proposes a multi-modal haptic rendering method based on a genetic algorithm (GA) to generate force and vibration stimuli for haptic actuators according to user's target hardness and roughness. The method utilizes a neural network to establish the mapping from objective stimuli features to perception intensities and uses a GA-based fitness function to transform the perception model into the force/vibration control model. The experiments show that multi-modal haptic rendering is more realistic than single-mode rendering.
Article
Engineering, Mechanical
Xinbing Ding, Mats Isaksson
Summary: This paper proposes a method for designing decoupled and spatially isotropic Stewart platforms with user-selected values of diagonal elements. By establishing a parametric model and deriving the expression of the Cartesian stiffness matrix, conditions for decoupling and spatial isotropy are quantitatively derived. Several novel architectures with three horizontal links were derived using this approach.
MECHANISM AND MACHINE THEORY
(2023)
Article
Computer Science, Information Systems
Vijay Kumar Pediredla, Karthik Chandrasekaran, Srikar Annamraju, Asokan Thondiyath
Summary: This study developed a novel haptic device with three degrees of freedom that can render high-fidelity touch sensations simultaneously by combining different movements to simulate multiple sensations. Experimental results demonstrate its accuracy in reproducing various modalities of haptic feedback of the virtual/remote environment.
Article
Computer Science, Software Engineering
Inrak Choi, Yiwei Zhao, Eric J. Gonzalez, Sean Follmer
Summary: This article investigates the effects of active transient vibration and visuo-haptic illusion on enhancing the perceived softness of haptic proxy objects, conducting three user studies to understand their individual and combined effects. The studies found that visuo-haptic illusion has a greater influence on perceived softness compared to active transient vibration when interacting with haptic proxy objects.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Guanhua Li, Weidong Zhu, Huiyue Dong, Yinglin Ke
Summary: In this paper, the importance of stiffness modeling in robotic machining is introduced, and an improved index based on the Frobenius condition number is proposed and theoretically proved to be superior in preventing manipulator ill-conditions. Contrast experiments show that the improved index ensures positioning accuracy in robotic machining while effectively avoiding robot ill-conditions.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Software Engineering
Andre Zenner, Kristin Ullmann, Antonio Krueger
Summary: The study proposes the combination of Dynamic Passive Haptic Feedback (DPHF) and Haptic Retargeting techniques, demonstrating through experiments that this combination can better address the challenges of similarity and colocation in immersive haptic experiences.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2021)
Article
Computer Science, Software Engineering
Filipe Barbosa, Daniel Mendes, Rui Rodrigues
Summary: In this study, a novel haptic device called Shape-a-getti is proposed, which can quickly change between different shapes of virtual objects by rotating poles. The user evaluation results show that despite the difficulty in distinguishing between objects with very similar shapes, participants were still able to successfully identify virtual objects with different shapes rendered by the device.
COMPUTERS & GRAPHICS-UK
(2023)
Article
Chemistry, Analytical
Hong Ren, Lin Lin, Yanjie Wang, Xin Dong
Summary: In this paper, a new robust 6-DoF pose estimation algorithm under hybrid constraints is proposed to improve the accuracy and stability of the two-stage pose estimation algorithm using heatmap in occluded object pose estimation. The proposed algorithm achieves better accuracy rates compared to other state-of-the-art algorithms and significantly reduces the mean estimation error while improving the stability of pose estimation. It is also a performant and efficient method.
Article
Chemistry, Multidisciplinary
Xuan Huang, Lingbao Kong, Guangxi Dong
Summary: This paper focuses on a 6-DOF robotic manipulator, establishing forward and inverse kinematics models and proposing a new modeling method as well as error compensation model, which are shown to effectively improve the working performance and accuracy of the manipulator.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
