Article
Robotics
Mohammad Samin Yasar, Tariq Iqbal
Summary: A novel sequence learning approach is proposed in this work, which learns a robust representation of human motion and predicts intent, working for various settings and improving human motion understanding.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Geology
Qinjun Qiu, Yongjian Tan, Kai Ma, Miao Tian, Zhong Xie, Liufeng Tao
Summary: Geological maps contain important geological knowledge and accurately recognizing symbols in these maps is crucial for understanding and analyzing them. This paper proposes a three-stage framework based on deep learning to automatically recognize symbols in geological maps, including dataset construction, CRNN model training, and geo-symbol index construction.
ORE GEOLOGY REVIEWS
(2023)
Article
Computer Science, Artificial Intelligence
Zhongli Wang, Guohui Tian
Summary: This article proposes a task-oriented robot cognitive manipulation planning method using affordance segmentation and logic reasoning, which can provide robots with semantic reasoning skills to manipulate appropriate parts of an object according to different tasks. The method utilizes a convolutional neural network based on the attention mechanism to obtain object affordance and constructs object/task ontologies for the management of objects and tasks. By establishing object-task affordances through causal probability logic, the method can reason manipulation regions' configuration for the intended task with the help of the Dempster-Shafer theory. Experimental results demonstrate that this method effectively improves the cognitive manipulation ability of robots and enhances their performance in various tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jingda Guo, Dominic Carrillo, Qi Chen, Qing Yang, Song Fu, Hongsheng Lu, Rui Guo
Summary: Cooperative perception is a novel approach to improve driving safety by overcoming the sensing limitation of a single automated vehicle. Existing solutions for cooperative perception use feature maps generated by CNN models, but their large size makes transmission difficult. This study proposes Slim-FCP, a new method that significantly reduces the transmission data size by using a channelwise feature encoder to remove irrelevant features and an intelligent channel selection strategy. Evaluation results show that Slim-FCP reduces transmission data size by 75% compared to the best state-of-the-art solution, with minimal loss in object detection recall.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Mei Liu, Bo Peng, Mingsheng Shang
Summary: Research on intention recognition of lower limb rehabilitation robot requires consideration of normal movement intentions before improving models to recognize patient intentions. A projected recurrent neural network (PRNN) model has been proposed to address convergence speed limitations of traditional RNN models, showing successful application in experimental intention recognition of lower limb movements.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Hsiu-Yuan Wang, Jian-Hong Wang, Jie Zhang, Hsing-Wei Tai
Summary: The aim of this study is to investigate the factors influencing Pokemon-Go robot users' intention to patronize hospitality firms using virtual monsters.
INFORMATION SYSTEMS FRONTIERS
(2021)
Article
Transportation
Chenxi Chen, Qing Tang, Xianbiao Hu, Zhitong Huang
Summary: Infrastructure-based sensors are a potential solution to support the adoption of connected and automated vehicle technologies in the early stages. These sensors can significantly enhance the driving context understanding of connected vehicles with lower levels of automation and overcome occlusion and limited sensor range issues. This manuscript proposes a cooperative perception modeling framework that addresses the key technical challenge of time delay in the perception process, using a CTRV model, delay compensation and fusion module, and an UKF algorithm for improved object tracking accuracy considering communication time delay. Simulation experiments show satisfactory results.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Pediatrics
Elisabeth Ammer, Laura Sophie Mandt, Isabelle Christine Silbersdorff, Fritz Kahl, York Hagmayer
Summary: Compared to other countries, robot-assisted pediatric surgery is not widely practiced in Germany. This study analyzed parents' intention to choose robot-assisted or laparoscopic surgery for their children, finding that parents were more inclined towards laparoscopic surgery. The perception of more benefits, assumed positive attitude from the social environment, and reduced anxiety increased the intention. The type of surgery influenced intentions through the assumed attitude of the social environment.
Article
Chemistry, Analytical
Archana Semwal, Melvin Ming Jun Lee, Daniela Sanchez, Sui Leng Teo, Bo Wang, Rajesh Elara Mohan
Summary: This article presents a method for flexible robotic development based on Cebrenus Rechenburgi, which determines appropriate locomotion modes through object-of-interest perception. The authors trained a locomotion mode recognition framework with a self-collected dataset and validated its effectiveness and accuracy through experiments. The results show that the framework can successfully determine the robot's locomotion modes during complex pathways.
Article
Computer Science, Interdisciplinary Applications
Changchun Liu, Zequn Zhang, Dunbing Tang, Qingwei Nie, Linqi Zhang, Jiaye Song
Summary: This paper proposes a mixed perception-based human-robot collaborative maintenance approach with three-hierarchy structures to address the problems in human-robot collaboration maintenance. The approach includes a perception module for recognizing human safety and maintenance request, a decision-making module for executing robotized maintenance tasks, and an augmented reality-assisted interaction interface for personnel. Comparative experiments in a machining workshop demonstrate the competitive performance of the proposed approach compared with other state-of-the-art methods.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Automation & Control Systems
Xiaoshan Gao, Liang Yan, Gang Wang, Chris Gerada
Summary: This study proposes a hybrid recurrent neural network architecture for intention recognition in collaborative assembly tasks. By improving the activation functions of LSTM and Bi-LSTM networks, and utilizing them in the hybrid architecture, the prediction performance of intention recognition can be effectively improved.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Construction & Building Technology
Jingyuan Wang, Shining Ma, Yue Liu, Yongtian Wang, Weitao Song
Summary: Augmented reality technology presents virtual objects in the real world to assist depth perception. Previous research focused on the impact of virtual object properties, while this study investigates the influence of ambient luminance, virtual object luminance, and shading model on depth perception. Results show that a higher luminance contrast between the real scene and the virtual object leads to decreased accuracy in depth perception in AR.
Article
Engineering, Industrial
Ahmed Eslam Salman, Magdy Raouf Roman
Summary: The study proposes a human-robot interaction framework to enable remote communication between operators and robots in a simple and intuitive way. The purpose is to reduce stress on operators, increase accuracy, and reduce task completion time. The proposed system is specifically designed for use in radioactive isotope production factories.
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION
(2023)
Article
Robotics
Nadav D. Kahanowich, Avishai Sintov
Summary: The research proposed a method using wearable force-myography device to classify objects grasped by humans, improving accuracy through training classifiers and increasing certainty by real-time iterative method. The study demonstrates high accuracy of the method and its ability to enhance the performance of trained classifiers.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Neurosciences
Noor Seijdel, Jessica Loke, Ron van de Klundert, Matthew van der Meer, Eva Quispel, Simon van Gaal, Edward H. F. de Haan, H. Steven Scholte
Summary: The study found that recurrent computations are crucial for figure-ground segmentation of objects embedded in complex scenes. Behavioral results, EEG measurements, and deep convolutional neural network performance all support the notion that recurrent processing is essential for recognizing objects in complex backgrounds.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Computer Science, Information Systems
Shaoyong Zhang, Na Li, Chenchen Qiu, Zhibin Yu, Haiyong Zheng, Bing Zheng
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Computer Science, Software Engineering
Haoxu Zhang, Chenchen Qiu, Chao Wang, Bin Wei, Zhibin Yu, Haiyong Zheng, Juan Li
Summary: This paper introduces a new method for generating 3D objects based on generative adversarial networks (GANs), utilizing multiple generators and discriminators to enhance learning complex distributions. The model employs spectral normalization technology to ensure stable training and generate high-quality and realistic 3D objects. Additionally, the system is capable of recovering incomplete 3D objects and outperforms baseline models in object quality.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Information Systems
Hao Ding, Bin Wei, Zhaorui Gu, Zhibin Yu, Haiyong Zheng, Bing Zheng, Juan Li
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Chao Wang, Wenjie Niu, Yufeng Jiang, Haiyong Zheng, Zhibin Yu, Zhaorui Gu, Bing Zheng
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2020)
Article
Computer Science, Information Systems
Yan Zhao, Ziqiang Zheng, Chao Wang, Zhaorui Gu, Min Fu, Zhibin Yu, Haiyong Zheng, Nan Wang, Bing Zheng
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Chemistry, Analytical
Qingyun Li, Zhibin Yu, Yubo Wang, Haiyong Zheng
Article
Computer Science, Artificial Intelligence
Yubo Wang, Zhibin Yu, Tatinati Sivanagaraja, Kalyana C. Veluvolu
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Hardware & Architecture
Ziqiang Zheng, Hongzhi Liu, Fan Yang, Xingyu Zheng, Zhibin Yu, Shaoda Zhang
Summary: This study introduces an innovative framework for photo-to-caricature translation, using a representation-guided scheme to mimic the caricature style, and introducing a feature-pyramid adversarial network to improve image synthesis quality. Experimental results demonstrate the excellent imitation capabilities of the proposed method across various caricature datasets.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Chemistry, Analytical
Qi Zhao, Zhichao Xin, Zhibin Yu, Bing Zheng
Summary: Estimation of underwater depth maps is crucial in underwater vision research, presenting challenges such as lack of paired data and dynamic underwater environments. Researchers have developed a novel framework combining image translation and depth map estimation techniques, utilizing a coarse-to-fine network for precise depth map estimation. The method efficiently addresses the issues in underwater image synthesis and depth map estimation, providing diverse underwater images and accurate depth map estimation results.
Article
Computer Science, Artificial Intelligence
Ziqiang Zheng, Zhibin Yu, Yang Wu, Haiyong Zheng, Bing Zheng, Minho Lee
Summary: This paper introduces a method to address the imbalanced learning problem through cross-species image-to-image translation, and proposes a novel, simple, and effective structure of Multi-Branch Discriminator (MBD) based on Generative Adversarial Networks (GANs). The effectiveness of the MBD is demonstrated through both theoretical analysis and empirical evaluation, showing remarkable performance in various cross-species image translation tasks.
Article
Computer Science, Information Systems
Ziqiang Zheng, Zhibin Yu, Haiyong Zheng, Yang Yang, Heng Tao Shen
Summary: The paper proposes an effective multi-adversarial framework based on part-global learning for one-shot cross-domain image-to-image translation. Extensive experiments show that the proposed approach achieves impressive results on imbalanced image domains and outperforms existing methods in one-shot image-to-image translation.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Information Systems
Ruyue Han, Yang Guan, Zhibin Yu, Peng Liu, Haiyong Zheng
Article
Computer Science, Information Systems
Xinliang Zhang, Huimin Zeng, Xiang Liu, Zhibin Yu, Haiyong Zheng, Bing Zheng
Article
Computer Science, Information Systems
Huimin Zeng, Xinliang Zhang, Zhibin Yu, Yubo Wang
Article
Computer Science, Information Systems
Zhensheng Shi, Liangjie Cao, Cheng Guan, Haiyong Zheng, Zhaorui Gu, Zhibin Yu, Bing Zheng
Article
Computer Science, Artificial Intelligence
Hamdan Abdellatef, Lina J. Karam
Summary: This paper proposes performing the learning and inference processes in the compressed domain to reduce computational complexity and improve speed of neural networks. Experimental results show that modified ResNet-50 in the compressed domain is 70% faster than traditional spatial-based ResNet-50 while maintaining similar accuracy. Additionally, a preprocessing step with partial encoding is suggested to improve resilience to distortions caused by low-quality encoded images. Training a network with highly compressed data can achieve good classification accuracy with significantly reduced storage requirements.
Article
Computer Science, Artificial Intelligence
Victor R. Barradas, Yasuharu Koike, Nicolas Schweighofer
Summary: Inverse models are essential for human motor learning as they map desired actions to motor commands. The shape of the error surface and the distribution of targets in a task play a crucial role in determining the speed of learning.
Article
Computer Science, Artificial Intelligence
Ting Zhou, Hanshu Yan, Jingfeng Zhang, Lei Liu, Bo Han
Summary: We propose a defense strategy that reduces the success rate of data poisoning attacks in downstream tasks by pre-training a robust foundation model.
Article
Computer Science, Artificial Intelligence
Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao
Summary: In this paper, the convergence rate of AdaSAM in the stochastic non-convex setting is analyzed. Theoretical proof shows that AdaSAM has a linear speedup property and decouples the stochastic gradient steps with the adaptive learning rate and perturbed gradient. Experimental results demonstrate that AdaSAM outperforms other optimizers in terms of performance.
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Computer Science, Artificial Intelligence
Miaohui Zhang, Kaifang Li, Jianxin Ma, Xile Wang
Summary: This paper proposes an unsupervised person re-identification (Re-ID) method that uses two asymmetric networks to generate pseudo-labels for each other by clustering and updates and optimizes the pseudo-labels through alternate training. It also designs similarity compensation and similarity suppression based on the camera ID of pedestrian images to optimize the similarity measure. Extensive experiments show that the proposed method achieves superior performance compared to state-of-the-art unsupervised person re-identification methods.
Article
Computer Science, Artificial Intelligence
Florian Bacho, Dominique Chu
Summary: This paper proposes a new approach called the Forward Direct Feedback Alignment algorithm for supervised learning in deep neural networks. By combining activity-perturbed forward gradients, direct feedback alignment, and momentum, this method achieves better performance and convergence speed compared to other local alternatives to backpropagation.
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Summary: Visual images observed by humans can be reconstructed from brain activity, and the visualization of arbitrary natural images from mental imagery has been achieved through an improved method. This study provides a unique tool for directly investigating the subjective contents of the brain.
Article
Computer Science, Artificial Intelligence
Huanjie Tao, Qianyue Duan
Summary: In this paper, a hierarchical attention network with progressive feature fusion is proposed for facial expression recognition (FER), addressing the challenges posed by pose variation, occlusions, and illumination variation. The model achieves enhanced performance by aggregating diverse features and progressively enhancing discriminative features.
Article
Computer Science, Artificial Intelligence
Zhenyi Wang, Pengfei Yang, Linwei Hu, Bowen Zhang, Chengmin Lin, Wenkai Lv, Quan Wang
Summary: In the face of the complex landscape of deep learning, we propose a novel subgraph-level performance prediction method called SLAPP, which combines graph and operator features through an innovative graph neural network called EAGAT, providing accurate performance predictions. In addition, we introduce a mixed loss design with dynamic weight adjustment to improve predictive accuracy.
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Teng Cheng, Lei Sun, Junning Zhang, Jinling Wang, Zhanyang Wei
Summary: This study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, called Fast Spectrum-Size Self-Training network (FSSNet). By utilizing start-stop points to build the signal model, this method successfully solves the difficulty of existing deep learning methods in detecting discontinuous signals and achieves satisfactory detection speed.
Article
Computer Science, Artificial Intelligence
Wenming Wu, Xiaoke Ma, Quan Wang, Maoguo Gong, Quanxue Gao
Summary: The layer-specific modules in multi-layer networks are critical for understanding the structure and function of the system. However, existing methods fail to accurately characterize and balance the connectivity and specificity of these modules. To address this issue, a joint learning graph clustering algorithm (DRDF) is proposed, which learns the deep representation and discriminative features of the multi-layer network, and balances the connectivity and specificity of the layer-specific modules through joint learning.
Article
Computer Science, Artificial Intelligence
Guanghui Yue, Guibin Zhuo, Weiqing Yan, Tianwei Zhou, Chang Tang, Peng Yang, Tianfu Wang
Summary: This paper proposes a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. BUNet utilizes a pyramid vision transformer encoder to learn multi-scale features and incorporates a boundary exploration module (BEM) and a boundary uncertainty aware module (BUM) to handle boundary areas. Experimental results demonstrate that BUNet outperforms other methods in terms of performance and generalization ability.