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arXiv 2024年

作者： Feng, Yi Guo, Zizhan Chen, Qijun Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Unsupervised monocular depth estimation frameworks have shown promising performance in autonomous driving. However, existing solutions primarily rely on a simple convolutional neural network for ego-motion recovery, which struggles to estimate precise camera poses in dynamic, complicated real-world scenarios. These inaccurately estimated camera poses can inevitably deteriorate the photometric reconstruction and mislead the depth estimation networks with wrong supervisory signals. In this article, we introduce SCIPaD, a novel approach that incorporates spatial clues for unsupervised depth-pose joint learning. Specifically, a confidence-aware feature flow estimator is proposed to acquire 2D feature positional translations and their associated confidence levels. Meanwhile, we introduce a positional clue aggregator, which integrates pseudo 3D point clouds from DepthNet and 2D feature flows into homogeneous positional representations. Finally, a hierarchical positional embedding injector is proposed to selectively inject spatial clues into semantic features for robust camera pose decoding. Extensive experiments and analyses demonstrate the superior performance of our model compared to other state-of-the-art methods. Remarkably, SCIPaD achieves a reduction of 22.2% in average translation error and 34.8% in average angular error for camera pose estimation task on the KITTI Odometry dataset. Our source code is available at https://***/SCIPaD. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Online, Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching

arXiv

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arXiv 2024年

作者： Huang, Zhiwei Zhang, Yikang Chen, Qijun Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in intelligent vehicles. Offline, target-based approaches have long been the preferred choice in this field. However, they often demonstrate poor adaptability to real-world environments. This is largely because extrinsic parameters may change significantly due to moderate shocks or during extended operations in environments with vibrations. In contrast, online, target-free approaches provide greater adaptability yet typically lack robustness, primarily due to the challenges in cross-modal feature matching. Therefore, in this article, we unleash the full potential of large vision models (LVMs), which are emerging as a significant trend in the fields of computer vision and robotics, especially for embodied artificial intelligence, to achieve robust and accurate online, target-free LCEC across a variety of challenging scenarios. Our main contributions are threefold: we introduce a novel framework known as MIAS-LCEC, provide an open-source versatile calibration toolbox with an interactive visualization interface, and publish three real-world datasets captured from various indoor and outdoor environments. The cornerstone of our framework and toolbox is the cross-modal mask matching (C3M) algorithm, developed based on a state-of-the-art (SoTA) LVM and capable of generating sufficient and reliable matches. Extensive experiments conducted on these real-world datasets demonstrate the robustness of our approach and its superior performance compared to SoTA methods, particularly for the solid-state LiDARs with super-wide fields of view. Our toolbox and datasets are publicly available at ***/MIAS-LCEC. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Three-dimensional reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface

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Journal of the Optical Society of America A: Optics and Image science, and Vision 2023年第11期40卷 2096-2107页

作者： Wang, Yue Han, Xueyou Rui, Jing Zhang, Hailan Yin, Lei Zhang, Xuefeng Wang, Xiangjun School of Computer Science and Technology Anhui University of Technology Maanshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Maanshan243032 China School of Electrical and Information Engineering Anhui University of Technology Maanshan China The 53th Research Institute of China Electronics Technology Group Corporation Tianjin China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin China Key Laboratory ofMOEMS of the Ministry of Education Tianjin University Tianjin China

Stereo vision technology based on line structured light can effectively solve the problem of a three-dimensional (3D) reconstruction of a smooth surface. A method for 3D reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface is presented in this paper. The subpixel coordinates of the light strip centers of the line structured light are obtained by the Steger algorithm while the binocular module moves along the guide rail, and the polar constraint is used to achieve the matching of the extracted light strip centers. As a result, the3Dcoordinates of the light strip centers in each location can be calculated because of the known interior and external parameters of the binocular module. To obtain the 3D point cloud data of the entire surface, a relative pose optimization method with respect to the initial frame is proposed, which accurately estimates the pose of the cameras in each location with respect to that in the initial location and unifies the 3D coordinates of the light strip centers in each location to the datum coordinates. The relative pose optimization method first estimates the rough values by using the direct linear transform method, and then iteratively calculates the refined solutions based on the principle of minimizing the re-projection errors. Simulation data and substantial experimental results validate the effectiveness of our method. Our method is compared to the direct linear transform method and the frame-by-frame transfer method, and the root mean square error (RMSE) of the distance from 3D point cloud to fitted plane is used to evaluate the 3D reconstruction accuracy. The repeatability experiment shows that the RMSE fromour method is as lowas 0.83mm. © 2023 Optica Publishing Group.

关键词： Location

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An Improved Model for Sap Flow Prediction Based on Linear Trend Decomposition 19th

An Improved Model for Sap Flow Prediction Based on Linear Tr...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Li, Bo Li, Yane Feng, Hailin Wu, Bin Zhu, Qiang Weng, Xiang Ruan, Yaoping College of Mathematics and Computer Science Zhejiang A&F University 666 Wusu Street Hangzhou311300 China Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province Hangzhou311300 China China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment Hangzhou311300 China College of Optical Mechanical and Electrical Engineering Zhejiang A&F University Hangzhou311300 China

ISBN: (纸本)9783031651229

Tree transpiration plays an important role in environmental change, which mainly assessed by sap flow. It is great significance to predict sap flow accurately for estimation of water consumption, management of water resource and making of sustainable development strategy for geohydrological balance under climate change. In this study, an improved prediction model was built with Dilnear algorithm using historical environment factors to assess short-term sap flow. Specifically, a public dataset including 17568 records was used. One record have one sap flow value and 9 environment factors which measured from January 1, 2016 to December 31, 2016 at 30 min interval in Canada for Tsuga canadensis (L.) Carriere. After data processed with missing value padding and normalization, seven factors was extracted with fusion of Pearson correlation and Grey correlation method. Then, sap flow prediction model was established with Dlinear algorithm. For comparison and analysis, five other deep learning networks of CNN, GRU, LSTM, Transformer and Informer were used to built sap flow prediction model respectively. Results shown that Dlinear based model has better performance than other models established in this paper. The coefficient of determination (R2), the Mean Absolute Error (MSE) and the Mean Square Error (MAE) achieved 0.9568, 0.0282 and 0.0017 for Dlinear based model. R2 of Dlinear based model is of 20.37%, 14.9% and 21.93% higher than CNN, GRU and LSTM based model respectively. In addition, we also analyzed length of look-back window and prediction window. Results shown that when length of look-back window was set as 720, Dlinear based model has better performance with R2 of 0.9568, which higher of 2.62%, 7.75%, 18.95% and 35.46% than length of 336, 192, 96, and 48. When the prediction window length was set as 96, Dlinear based model has higher of R2 and smaller of MSE and MAE than length of 240, 336 and 720. At the same time, we compared performance of models established wit

关键词： Prediction models

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Van der Waals integration inch-scale 2D MoSe_(2) layers on Si for highly-sensitive broadband photodetection and imaging

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Nano Research 2023年第8期16卷 11422-11429页

作者： Yupiao Wu Shuo-En Wu Jinjin Hei Longhui Zeng Pei Lin Zhifeng Shi Qingming Chen Xinjian Li Xuechao Yu Di Wu School of Physics and Microelectronics and Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA School of Microelectronics Science and Technology Sun Yat-Sen UniversityZhuhai 519082China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China

As one of the most promising materials for two-dimensional transition metal chalcogenides(2D TMDs),molybdenum diselenide(MoSe_(2))has great potential in photodetectors due to its excellent properties like tunable bandgap,high carrier mobility,and excellent air *** 2D MoSe_(2)-based photodetectors have been reported to exhibit admired performance,the large-area 2D MoSe_(2)layers are difficult to be achieved via conventional synthesis methods,which severely impedes its future ***,we present the controllable growth of large-area 2D MoSe_(2)layers over 3.5-inch with excellent homogeneity by a simple post-selenization ***,a high-quality n-MoSe_(2)/p-Si van der Waals(vdW)heterojunction device is in-situ fabricated by directly growing 2D n-MoSe_(2)layers on the patterned p-Si substrate,which shows a self-driven broadband photoresponse ranging from ultraviolet to mid-wave infrared with an impressive responsivity of 720.5 mA·W^(−1),a high specific detectivity of 10^(13) Jones,and a fast response time to follow nanosecond pulsed optical *** addition,thanks to the inch-level 2D MoSe_(2)layers,a 4×4 integrated heterojunction device array is achieved,which has demonstrated good uniformity and satisfying imaging *** large-area 2D MoSe_(2)layer and its heterojunction device array have great promise for high-performance photodetection and imaging applications in integrated optoelectronic systems.

关键词： molybdenum diselenide large-area synthesis broadband photodetector integrated device array van der Waals(vdW)heterojunction

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RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVED MULTI-OPTIMIZATION ALGORITHM AND SUPPORT VECTOR REGRESSION 14

RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVE...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Tao, Yulong Wang, Jun Wang, Kai School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Intelligent Technology for Chemical Process Industry Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Robotics and Artificial Intelligence Manufacturing Innovation Institute Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781837242672

The lithium-ion battery is increasingly critical in the fields of electric vehicles and sustainable energy. Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential to mitigate risks and minimize potential losses. This paper introduces a novel fusion algorithm, FPSOGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) with an enhanced Circle chaos strategy and a random adaptive weighting strategy. The FPSOGWO algorithm, integrated with Support Vector Regression (FPSOGWO-SVR), was utilized to predict the RUL of health factors in lithium batteries. Compared to other optimization algorithms, FPSOGWO exhibits superior optimization speed and value, effectively overcoming issues of local optimality. Specifically, when benchmarked against the Whale Optimization Algorithm-SVR (WOA-SVR), the R-Square values of FPSOGWO-SVR showed improvements of 15.62%, 1.28%, 5.67%, and 10.65% across four different types of batteries. Experimental results demonstrate that FPSOGWO-SVR provides more accurate RUL predictions for lithium batteries, highlighting its potential for broader application in battery health management systems. © The Institution of Engineering & technology 2024.

关键词： Support vector regression

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A Lightweight Method to Survey with Protecting Privacy yet Maintaining Accuracy 18th

A Lightweight Method to Survey with Protecting Privacy yet...

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18th International Conference on Wireless Artificial intelligent Computing systems and Applications, WASA 2024

作者： Wang, Yuxuan Di, Xinyu Liu, Ningbo Zhang, Xianchao Ren, Wei School of Computer Science China University of Geosciences Wuhan China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada International College Chongqing University of Technology Chongqing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China Ministry of Education Chengdu China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China

ISBN: (纸本)9783031714665

In a survey, a lot of questions and answers are listed and participators will select one of the answers for each question. Sometimes, participators may not be willing to select the preferred one item in the answers because the item may damage the privacy of themselves. If this question is not answered, then it reveals that the question or the answers are sensitive, which also damages the privacy. Besides, each participator may have distinct privacy concerns even for the same questions and answers, thus the privacy-aware questions and answers cannot be determined and be removed in advance. Thus, the dilemma is how to select the answers for privacy-aware questions yet maintain the utility of the answering results in the survey. In this paper, we propose a general method to protect the various privacy in the investigation without the knowledge of privacy to be protected in advance. The main idea is that participators can randomly select answers if the privacy is the concern for themselves in the answers for this question. Our method is lightweight so as to be convenient in practices. We extensively analyze the accuracy of survey results to justify that our method can protect the privacy yet maintain the utility of the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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IoT for Sustainable Hospitality: A Systematic Review of Opportunities and Challenges for the Hospitality Industry Revolution 19

IoT for Sustainable Hospitality: A Systematic Review of Oppo...

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19th Annual International Conference on Distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Poullas, Marios S. Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cell and Developmental Biology University College London London United Kingdom Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350346497

technology has influenced the hospitality industry, and the Internet of Things (IoT) has become a focus for sustainability, revenue growth, and problem-solving. This systematic review examined the opportunities and challenges of implementing IoT technology for a sustainable hospitality. Through a thorough analysis of 28 studies, we found that IoT technology has great potential to address sustainability issues in the hospitality industry. However, there are still significant challenges that need to be addressed for the successful implementation of IoT technology in the hospitality industry. Overall, this study highlights the potential of IoT technology to revolutionize the hospitality industry and make it more sustainable. Further research is needed to address the challenges of IoT implementation in the hospitality industry and to evaluate the effectiveness of IoT technology in achieving sustainability goals. © 2023 IEEE.

关键词： Energy efficiency

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Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Semantics-Aware Avatar Locomotion Adaption for Indoor Cross-Scene AR Telepresence

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Li, Yi-Jun Yang, Hao-Zhong Shu, Wen-Tong Wang, Miao Tsinghua University Department of Computer Science and Technology Beijing100084 China Beihang University China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Zhongguancun Laboratory Beijing100094 China

Geographically dispersed users often rely on virtual avatars as intermediaries to facilitate interactive communication and collaboration. However, existing methods for augmented reality (AR) telepresence applications exhibit limitations, including restricted movement within confined sub-areas, lack of smooth transitions, and the necessity for manually establishing object mapping between dissimilar environments. We present a novel interactive AR framework for virtual avatar locomotion adaption while preserving semantic coherence across dissimilar indoor scenes. Initially, we conduct a preliminary user study to identify key attributes influencing preferred avatar movement. These attributes are quantified as features, and a dataset of user annotations on avatar movements is created. Based on the user interaction and scene configurations, we employ a deep reinforcement learning neural network to guide the avatar to the ideal position while maximizing semantic coherence. We validate our proposed framework through simulations and user studies by implementing an AR-based 3D telepresence prototype, demonstrating the efficacy of our framework in conveying user intentions across dissimilar environments, enabling natural and immersive 3D telepresence interactions. © 1995-2012 IEEE.

关键词： Reinforcement learning

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