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arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Enhancing security in OpenID connect Single Sign-On (SSO) systems: a comprehensive vulnerability analysis and mitigation approach

Enhancing security in OpenID connect Single Sign-On (SSO) sy...

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4th International Conference on Distributed Sensing and Intelligent systems (ICDSIS 2023)

作者： M. Al-Shabi A. Bennour M. Al-Sarem O. I. Khalaf R. Sikder S. Algburi Department of management Information system Taibah University Medina KSA ``LAMIS" Laboratory Echahid Chikh Larbi Tebessi University Tebessa Algeria College of Computer Science and Engineering Taibah University Medina KSA Al-Nahrain University Jadriya Baghdad Iraq Information Technology University of the Cumberlands USA Al-Kitab University College of Engineering Techniques kirkuk Iraq

ISBN: (数字)9781837240258

Single Sign-On (SSO) systems have become popular for simplifying the login process by allowing users to authenticate with a single identity provider (IDP). However, the widespread use of these systems raises concerns about user privacy, as IDPs like Google or Facebook can gather extensive information about user behavior on the web. This poses a significant challenge for privacy-conscious users who wish to limit the disclosure of their web access behavior to third-party organizations. In this paper, we present a comprehensive and large-scale practical study focused on the OpenID Connect protocol, a widely used SSO protocol. Our analysis delves into the various stages of the protocol's operation, highlighting its security flaws and vulnerabilities. Additionally, we conduct a systematic examination of the privacy implications associated with user access to SSO systems. We provide a detailed account of how easily user information can be accessed, shedding light on the potential risks.

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LIMOT: A Tightly-Coupled system for LiDAR-Inertial Odometry and Multi-Object Tracking

arXiv

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arXiv 2023年

作者： Zhu, Zhongyang Zhao, Junqiao Huang, Kai Tian, Xuebo Lin, Jiaye Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China The School of Surveying and Geo-Informatics Tongji University Shanghai China

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial odometry system that is capable of accurately estimating the poses of both ego-vehicle and objects. We propose a trajectory-based dynamic feature filtering method, which filters out features belonging to moving objects by leveraging tracking results before scan-matching. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on the KITTI tracking dataset and self-collected dataset show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other baseline methods. Our open-source implementation is available at https://***/tiev-tongji/LIMOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Development of AUV-based system for acoustic tracking of diving sperm whales

Development of AUV-based system for acoustic tracking of div...

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OCEANS

作者： T. Ura R. Bahl M. Sakata J. Kojima T. Fukuchi J. Ura K. Mori T. Nakatani Y. Nose H. Sugimatsu Underwater Technology Research Center Institute of Industrial Science University of Tokyo Meguro Tokyo Japan KDD Research and Development Laboratories Inc. Kamifukuoka Saitama Japan System Giken Corporation Chigasaki Kanagawa Japan Department of Computer Engineering and Network Engineering Waseda University Shinjuku Tokyo Japan Ogasawara Whale Watching Association Chichijima Ogasawara Tokyo Japan

Sperm whales are known to dive to a depth of over two thousand meters. They emit loud impulsive broadband sounds called "clicks" during their diving. To investigate the sperm whale behavior, we attempt to apply the autonomous underwater vehicle (AUV) as an observation platform for sperm whales. AUV recognizes sperm whales individually and tracks them by listening to their clicks. As an initial stage for these objectives, we set up an experiment to test our observation system using two boats without an AUV. The observation system consists of two sets of 4-hydrophone arrays. Each hydrophone array works as a short baseline (SBL) system. The SBL system detects sperm whale clicks and calculates the orientation to sperm whales. Two relative directions from the two SBL arrays separated over a long base line (LBL) tell us the position of whale by triangulation. In the middle of August 2003, we carried out an experiment of the test system off Chichijima Island in Ogasawara. We deployed two boats each with a hydrophone array. An acoustic link between the SBL arrays was established to obtain the LBL length. During the experiments over five days we encountered at least fourteen sperm whales and succeeded in tracking some sperm whales by our observation system. Later in September 2004, we will perform another experiment using an AUV that works as one of the SBL systems. We expect this AUV-based observation system will give us a better understanding of sperm whale diving behavior and eco-system

关键词： Whales Sonar equipment Boats Underwater tracking system testing Acoustical engineering Underwater vehicles Acoustic arrays Humans Sea surface

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Air Embolism Protecting system for Safe Intravenous Therapy

Air Embolism Protecting System for Safe Intravenous Therapy

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IEEE Symposium on Humanities, science and engineering Research

作者： Kamran Manzoor Waleed Ejaz Najam ul Hasan Mustafa Arif Seok Lee Hyung Seok Kim Department of Information and Communication Engineering Sejong University Seoul Republic of Korea Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea

ISBN: (纸本)9781467313117

Intravenous (IV) therapy has been included as an integral part of nurse's professional practice. Although it is an effective curing technique, it has some complications. Air embolism is one of such complications which may cause death of patients in some cases. In this paper, we have proposed an efficient, economic and reliable air embolism protecting system to save precious lives in case of air bubble insertion. The proposed system monitors the IV tube and when an air bubble even in fraction of millimeter gets introduced into the tube, detects it, indicates emergency and blocks the fluid flow to protect entrance of the air bubble into the human body. The proposed system has been evaluated on various IV solutions;results are very promising as the accuracy is more than or equal to 99% in every case thus supporting the implementation of the solution. Therefore, the system can be used for any type of IV solution.

关键词： Intravenous therapy air embolism infrared bubble detector flow blocker

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Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition

arXiv

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arXiv 2023年

作者： Zhao, Junqiao Zhang, Fenglin Cai, Yingfeng Tian, Gengxuan Mu, Wenjie Ye, Chen Feng, Tiantian Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between frame sequences or extracting sequence descriptors for direct retrieval. However, the former is usually based on the assumption of constant velocity, which is difficult to hold in practice, and is computationally expensive and subject to sequence length. Although the latter overcomes these problems, existing sequence descriptors are constructed by aggregating features of multiple frames only, without interaction on temporal information, and thus cannot obtain descriptors with spatio-temporal discrimination. In this paper, we propose a sequence descriptor that effectively incorporates spatio-temporal information. Specifically, spatial attention within the same frame is utilized to learn spatial feature patterns, while attention in corresponding local regions of different frames is utilized to learn the persistence or change of features over time. We use a sliding window to control the temporal range of attention and use relative positional encoding to construct sequential relationships between different features. This allows our descriptors to capture the intrinsic dynamics in a sequence of frames. Comprehensive experiments on challenging benchmark datasets show that the proposed approach outperforms recent state-of-the-art methods. The code is available at https://***/tiev-tongji/Spatio-Temporal-SeqVPR. Copyright © 2023, The Authors. All rights reserved.

关键词： Query processing

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OPTIMIZATION OF PLASTIC INJECTION MOLDING PROCESS VIA TAGUCHI'S PARAMETER DESIGN METHOD, BPNN, AND DFP

OPTIMIZATION OF PLASTIC INJECTION MOLDING PROCESS VIA TAGUCH...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： W.C.CHEN M.W.WANG G.L.FU C.T.CHEN Graduate School of Industrial Engineering and System Management Chung Hua University No.707 Sec.2 Department of Mechanical Engineering National Kaohsiung University of Aapplied Sciences415 Chien Ku Graduate Institute of Technology Management Chung Hua University No.707 Sec.2 Wu Fu Rd. Hsinchu Department of Computer Science and Information Engineering Ta Hwa Institute of Technology 1 Tahwa

This research integrates Taguchi's parameter design method, back-propagation neural networks (BPNN), and Davidon-Fletcher-Powell (DFP) method to optimize the process parameter settings of plastic injection molding. Taguchi's parameter design method is used to arrange an orthogonal array experiment and to reduce the number of set-test cycles. Injection Time, Velocity Pressure Switch Position, Packing Pressure, and Injection Velocity are selected as process control parameters and product weight is selected as the single product quality. The experimental data of Taguchi's parameter design method are used for effectively training and testing BPNN. Then, BPNN is combined with DFP to search out the final optimal process parameter settings. Finally, a confirmation experiment is performed to confirm the effectiveness of the final optimal process parameter settings. The experimental results show that the proposed effective process parameter optimization approach can avoid shortcomings inherent in the application of trial-and-error processes or the conventional Taguchi parameter design method. Furthermore, the proposed approach can effectively assist engineers in determining optimal initial process parameter settings and achieving competitive advantages on product quality and costs.

关键词： Plastic injection molding Back-propagation neural networks Taguchi's parameter design Davidon-Fletcher-Powell method

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Unsupervised joint learning of depth, optical flow, ego-motion from video

arXiv

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arXiv 2021年

作者： Li, Jianfeng Zhao, Junqiao Song, Shuangfu Feng, Tiantian The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geoinfomatics Tongji University Shanghai China Insititute of Intelligent Vehicle Tongji University Shanghai China

Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robot's visual perception. We use a joint selfsupervised method to estimate the three geometric elements. Depth network, optical flow network and camera motion network are independent of each other but are jointly optimized during training phase. Compared with independent training, joint training can make full use of the geometric relationship between geometric elements and provide dynamic and static information of the scene. In this paper, we improve the joint self-supervision method from three aspects: network structure, dynamic object segmentation, and geometric constraints. In terms of network structure, we apply the attention mechanism to the camera motion network, which helps to take advantage of the similarity of camera movement between frames. And according to attention mechanism in Transformer, we propose a plug-and-play convolutional attention module. In terms of dynamic object, according to the different influences of dynamic objects in the optical flow self-supervised framework and the depth-pose self-supervised framework, we propose a threshold algorithm to detect dynamic regions, and mask that in the loss function respectively. In terms of geometric constraints, we use traditional methods to estimate the fundamental matrix from the corresponding points to constrain the camera motion network. We demonstrate the effectiveness of our method on the KITTI dataset. Compared with other joint self-supervised methods, our method achieves state-of-the-art performance in the estimation of pose and optical flow, and the depth estimation has also achieved competitive *** Codes 65Dxx © 2021, CC BY-NC-ND.

关键词： Geometry

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StressFADS: Learning Latent Autonomic Factors of Stress in the Context of Trauma Recall and Neuromodulation

StressFADS: Learning Latent Autonomic Factors of Stress in t...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Asim H. Gazi Michael Chan Hao-Lun Hsu J. Douglas Bremner Christopher J. Rozell Omer T. Inan School of Electrical and Computer Engineering (ECE) Georgia Institute of Technology (GT) Atlanta GA USA School of Engineering and Applied Sciences Harvard University Cambridge MA USA Coulter Department of Biomedical Engineering (CDBME) GT Atlanta GA USA CDBME GT Atlanta GA USA Department of Computer Science Duke University Durham NC USA Department of Radiology and Imaging Sciences and the Department of Psychiatry and Behavioral Sciences Emory University School of Medicine Atlanta GA USA Atlanta Veterans Affairs Health Care System Decatur GA USA School of ECE GT Atlanta GA USA

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Physiological markers of stress and neuromodulation (e.g., heart rate variability) are often inconsistent when it comes to quantifying changes in autonomic nervous system function. This inconsistency is explained by the autonomic nervous system's output varying across organ systems, as well as limitations in what each marker quantifies. In this work, we present an unsupervised learning approach we term StressFADS: Stress Factor Analysis via Dynamical systems. StressFADS overcomes single marker inconsistencies by learning underlying dynamics that are shared across physiological markers of stress. StressFADS's encoder summarizes a time window of physiological markers and initializes a recurrent neural network (RNN) with this summary. This RNN is autonomously simulated forward in time, and the output at each timestep is fed through a dimension-ality reduction stage trained to reconstruct the original window of physiological markers. This forces the model to learn latent representations that capture shared dynamics across the markers. We apply StressFADS to the analysis of approximately 50 hours of 1-Hz cardiovascular and respiratory marker time series from a double-blind, randomized controlled trial (N = 26) involving trauma recall and active or sham cervical transcutaneous vagus nerve stimulation (tVNS). We find that StressFADS learned latent factors that successfully quantify differences between stress induced by trauma recall, a neutral condition, and active or sham tVNS. This is promising and motivates future work in learning latent autonomic states that more faithfully track changes in stress and intervention effects for just-in-time stress mitigation.

关键词： Recurrent neural networks Prevention and mitigation Autonomic nervous system Time series analysis Dynamics Feedback control Neuromodulation Heart rate variability Stress Unsupervised learning

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