Article
Engineering, Industrial
Yongjing Wang, Feiying Lan, Jiayi Liu, Jun Huang, Shizhong Su, Chunqian Ji, Duc Truong Pham, Wenjun Xu, Quan Liu, Zude Zhou
Summary: Remanufacturing involves rebuilding a product using a combination of reused, repaired, and new parts, with disassembly as a key and labor-intensive process. Robotic disassembly is an attractive alternative but still requires manual planning due to the complexity of end-of-life products. The proposed method in this paper allows machines to plan disassembly by generating and manipulating specific matrices, providing a flexible and effective approach for dealing with complex mechanical structures.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Panqi Wu, Huanhe Wang, Bailin Li, Wenlong Fu, Jie Ren, Qiang He
Summary: The study proposes a Simplified Discrete Gravitational Search Algorithm (SDGSA) for optimizing the maintenance process of hydropower equipment (HE). Performance comparison experiments show that SDGSA outperforms other algorithms in solving maintenance tasks with different complexity.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Engineering, Industrial
S. K. Ong, M. M. L. Chang, A. Y. C. Nee
Summary: Disassembly sequence planning (DSP) is a challenging research area that has attracted significant attention from researchers worldwide. New solutions are continuously proposed to tackle the complexities of disassembling products, with advancements in computing and introduction of new concepts like virtual reality. Survey papers in the past 12 years have focused on product representation models, sequencing algorithms, and methodology validation in DSP research.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Soran Parsa, Mozafar Saadat
Summary: The research proposes a disassembly planning method based on human-robot collaboration, utilizing human flexibility and robot precision to handle complex disassembly tasks and improve process efficiency. Components are targeted based on remanufacturability parameters, and optimization is done using a mathematical model and genetic algorithm.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Natalia Hartono, F. Javier Ramirez, D. T. Pham
Summary: Remanufacturing is crucial for a circular economy as it helps reduce landfill waste and preserve natural resources, benefiting the environment. This study proposes a model for planning the sequence of steps for robotic disassembly, using the Bees Algorithm to optimize profitability, energy savings, and greenhouse gas emission reductions.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Software Engineering
Arun Rehal, Dibakar Sen
Summary: This paper presents a scheme for efficient disassembly sequence planning based on part accessibility. It introduces the concept of shell structures for analyzing the accessibility of parts and explores their applications in maintenance planning and end-of-life processing. A grid-based method is proposed for constructing the shell structure. The results demonstrate the effectiveness of the proposed methods in assessing accessibility and updating shell structures.
COMPUTER-AIDED DESIGN
(2023)
Article
Computer Science, Software Engineering
Brandon J. J. Matthews, Bruce H. H. Thomas, G. Stewart Von Itzstein, Ross T. T. Smith
Summary: This article presents adaptive reset techniques for haptic retargeting systems in hybrid virtual reality interfaces aligned with physical interfaces. A modified Point technique is introduced to guide users towards the next interaction, minimizing the remaining distance to the target upon reset completion. These techniques consider angular and translational gain and can omit reset when within an acceptable range, enabling uninterrupted retargeting and reducing task completion time, travel distance, and user errors.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Engineering, Mechanical
Hongfei Guo, Linsheng Zhang, Yaping Ren, Leilei Meng, Zhongwei Zhou, Jianqing Li
Summary: This paper studies a disassembly planning problem with operation attributes and proposes a disassembly planning method based on artificial bee colony algorithm. Experimental results show that the proposed method performs well in improving disassembly efficiency and reducing costs.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Xiaonuo Dongye, Dongdong Weng, Haiyan Jiang, Lulu Feng
Summary: This paper proposes a modular haptic agent (MHA) prototype system, which enables the tactile simulation and encountered-type haptic interaction of common virtual pet agents through modular design and haptic mapping. The MHA system with haptic interaction is actively initiated by the agents according to the user's intention, providing a better experience of human-agent interaction. Research demonstrates that the MHA system has more advantages in terms of realism, interactivity, attraction, and raising user emotions. Overall, MHA is a system that can build multiple companion agents, provide active interaction and has the potential to quickly build diverse haptic agents for an intelligent and comfortable virtual world.
Article
Automation & Control Systems
Yuanjun Laili, Xiang Li, Yongjing Wang, Lei Ren, Xiaokang Wang
Summary: The key step in remanufacturing is disassembling the returned product, but challenges arise due to uncertainties in the condition of the cores. This research focuses on developing flexible sequencing of robotic disassembly with online recovery by incorporating backup actions. Through modeling the time and success rate of backup actions, a dual-objective optimization model for robotic disassembly sequence planning is established using a dual-selection multiobjective evolutionary algorithm.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Review
Automation & Control Systems
Xiwang Guo, MengChu Zhou, Abdullah Abusorrah, Fahad Alsokhiry, Khaled Sedraoui
Summary: It is crucial to efficiently disassemble obsolete products to avoid environmental pollution and maximize economic benefits. This paper surveys the state of the art of disassembly sequence planning, aiming to provide guidance for researchers and decision makers in optimal planning solutions. The progress in disassembly sequencing planning is discussed in various aspects, stimulating further engagement in research and development in the Industry 4.0 era.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Interdisciplinary Applications
Amal Allagui, Imen Belhadj, Regis Plateaux, Moncef Hammadi, Olivia Penas, Nizar Aifaoui
Summary: The paper presents a new approach based on the Reinforcement Learning algorithm to optimize Disassembly Sequence Planning. The approach aims to optimize five disassembly parameters or goals and is applied to a demonstrative example.
COMPUTERS IN INDUSTRY
(2023)
Article
Multidisciplinary Sciences
Cheng Zhang, Amir Mohammad Fathollahi-Fard, Jianyong Li, Guangdong Tian, Tongzhu Zhang
Summary: Product disassembly and recycling are crucial in green design, with disassembly sequence planning being a key problem that can be solved using a new algorithm involving disassembly hybrid graphs and disassembly constraint matrices, along with an improved social engineering optimizer method.
Article
Engineering, Industrial
Yuanjun Laili, Fei Ye, Yongjing Wang, Lin Zhang
Summary: This paper discusses the use of a probability matrix to represent uncertain interference and proposes a multi-threshold planning scheme to generate optimal disassembly sequence plans. The research demonstrates that this approach is effective in handling disassembly problems under complex end-of-life conditions.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Robotics
Mine Sarac, Tae Myung Huh, Hojung Choi, Mark R. Cutkosky, Massimiliano Di Luca, Allison M. Okamura
Summary: The aim of this study was to provide effective interaction with virtual objects using haptic devices worn near the wrist. The results showed that participants performed better with normal displacements compared to shear displacements during virtual manipulation. A calibration method was needed to find the point of equality between normal and shear stimuli.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Immunology
Lifan Xu, Qizhao Huang, Haoqiang Wang, Yaxing Hao, Qiang Bai, Jianjun Hu, Yiding Li, Pengcheng Wang, Xiangyu Chen, Ran He, Bingshou Li, Xia Yang, Tingting Zhao, Yanyan Zhang, Yifei Wang, Juanjuan Ou, Houjie Liang, Yuzhang Wu, Xinyuan Zhou, Lilin Ye
Article
Immunology
Bingshou Li, Zhirong Li, Pengcheng Wang, Qizhao Huang, Lifan Xu, Ran He, Lilin Ye, Qiang Bai
Article
Immunology
Yaxing Hao, Yifei Wang, Xiaobing Liu, Xia Yang, Pengcheng Wang, Qin Tian, Qiang Bai, Xiangyu Chen, Zhirong Li, Jialin Wu, Zhunyi Xie, Xinyuan Zhou, Yuyang Zhou, Zhinan Yin, Yuzhang Wu, Lilin Ye
FRONTIERS IN IMMUNOLOGY
(2018)
Article
Immunology
Sophie El Abbas, Coraline Radermecker, Qiang Bai, Charline Beguin, Joey Schyns, Margot Meunier, Dimitri Pirottin, Christophe J. Desmet, Marie-Alice Meuwis, Tatiana Art, Edouard Louis, See-Ying Tam, Mindy Tsai, Fabrice Bureau, Stephen J. Galli, Thomas Marichal
MUCOSAL IMMUNOLOGY
(2020)
Article
Multidisciplinary Sciences
Joey Schyns, Qiang Bai, Cecilia Ruscitti, Coraline Radermecker, Sebastiaan De Schepper, Svetoslav Chakarov, Frederic Farnir, Dimitri Pirottin, Florent Ginhoux, Guy Boeckxstaens, Fabrice Bureau, Thomas Marichal
NATURE COMMUNICATIONS
(2019)
Article
Immunology
Pierre Lemaitre, Qiang Bai, Celine Legrand, Alain Chariot, Pierre Close, Fabrice Bureau, Christophe J. Desmet
Summary: The study revealed that specific epitranscriptomic tRNA modifications, particularly Elp3, play a crucial role in T cell biology by influencing T cell cycle entry and promoting optimal TFH responses. Overactivation of the stress-responsive transcription factor Atf4 in Elp3-deficient T cells leads to impaired T cell function, while targeting Atf4 or Chop rescues TFH responses in Elp3-deficient T cells.
JOURNAL OF IMMUNOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Maude Liegeois, Qiang Bai, Laurence Fievez, Dimitri Pirottin, Celine Legrand, Julien Guiot, Florence Schleich, Jean-Louis Corhay, Renaud Louis, Thomas Marichal, Fabrice Bureau
Summary: This study explores the heterogeneity of human alveolar macrophages (AMs) isolated from different groups, finding distinct subpopulations of AMs in BAL fluid based on autofluorescence levels. Single-cell RNA sequencing analyses reveal transcriptionally distinct clusters of classical and monocyte-derived AM enriched in smokers with and without COPD. Signs of gene signatures related to detoxification, oxidative stress, and proinflammatory responses are observed in smoking-associated clusters, indicating potential implications for COPD initiation or progression.
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY
(2022)
Article
Immunology
Xiao-Huan Liu, Jin-Ting Zhou, Chun-xia Yan, Cheng Cheng, Jing-Na Fan, Jing Xu, Qiangsun Zheng, Qiang Bai, Zongfang Li, Shengbin Li, Xiaoming Li
Summary: The deficiency of ApoA4 leads to the reprogramming of liver immune cells and promotes the increase of specific subsets, exacerbating hepatic inflammation in NAFLD. These findings may provide new therapeutic targets for the treatment of NAFLD.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Immunology
Domien Vanneste, Qiang Bai, Shakir Hasan, Wen Peng, Dimitri Pirottin, Joey Schyns, Pauline Marechal, Cecilia Ruscitti, Margot Meunier, Zhaoyuan Liu, Celine Legrand, Laurence Fievez, Florent Ginhoux, Coraline Radermecker, Fabrice Bureau, Thomas Marichal
Summary: Using a mouse model, researchers found that engrafted Ly6C(+) classical monocytes proliferate locally in a Csf1 receptor-dependent manner before differentiating into lung interstitial macrophages.
Article
Geology
Lei YongLiang, Dai JiaWen, Bai Qiang, Wang KaiXing, Sun LiQiang, Liu XiaoDong, Yu ChiDa, He ShiWei
Summary: The Silurian rhyolite in the Haidewula region of the East Kunlun Orogenic Belt was formed at 426Ma and belongs to the high-K calc-alkaline to shoshonite series. It was generated by high-temperature, middle-crust pressure melting of Ordovician felsic igneous rocks. The formation of the rhyolite may have been influenced by post-collisional lithospheric delamination during the Silurian Roderlo epoch.
ACTA PETROLOGICA SINICA
(2021)
Article
Biochemistry & Molecular Biology
Yanyan Zhang, Baohua Li, Qiang Bai, Pengcheng Wang, Gang Wei, Zhirong Li, Li Hu, Qin Tian, Jing Zhou, Qizhao Huang, Zhiming Wang, Shuai Yue, Jialin Wu, Liuqing Yang, Xinyuan Zhou, Lubin Jiang, Ting Ni, Lilin Ye, Yuzhang Wu
Summary: The study identified a key lncRNA-Snhg1 in CD8 T cells that promotes memory formation and inhibits effector cell differentiation. Snhg1 interacts with Vps13D and regulates IL-7 signaling to play a crucial role in memory CD8 cell generation. The Snhg1-Vps13D-IL-7R-TCF1 axis is central in memory CD8 establishment and a potential target for enhancing vaccination effects against ongoing pandemics.
SIGNAL TRANSDUCTION AND TARGETED THERAPY
(2021)
Meeting Abstract
Immunology
Yanyan Zhang, Qiang Bai, Ting Ni, Liuqing Yang, Lubin Jiang, Lilin Ye, Yuzhang Wu
JOURNAL OF IMMUNOLOGY
(2020)
Meeting Abstract
Immunology
J. Schyns, Q. Bai, C. Ruscitti, C. Radermecker, S. De Schepper, S. Chakarov, D. Pirottin, F. Ginhoux, G. Boeckxstaens, F. Bureau, T. Marichal
EUROPEAN JOURNAL OF IMMUNOLOGY
(2019)
Article
Immunology
Pengcheng Wang, Youping Wang, Luoyingzi Xie, Minglu Xiao, Jialin Wu, Lifan Xu, Qiang Bai, Yaxing Hao, Qizhao Huang, Xiangyu Chen, Ran He, Baohua Li, Sen Yang, Yaokai Chen, Yuzhang Wu, Lilin Ye
FRONTIERS IN IMMUNOLOGY
(2019)
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.
