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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Abidemi, Sarumi Usman Mubarak, Auwalu Saleh Akanni, Olukayode Ameen, Zubaida Said Cacciagrano, Diletta Al-turjman, Fadi Artifiical Intelligence Engineering Department AI and Robotics Institue Near East University Mersin 10 Nicosia Turkey Computer Science Division School of Science and Technology Camerino University Camerino62032 Italy Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey

ISBN: (纸本)9783031286933

Many researchers have make efforts on creating several attendance systems to keep track of student attendance in school which is also part of academic curriculum that will have great impact in student academic performance. There have been different technologies such as RFID, face recognition, biometric, web-based attendance system etc. To capture attendance in a way that’s better than the traditional method of pen and paper or roll call system. Making this attendance record available on blockchain network and applying artificial intelligence algorithm for prediction or classification in correlation with the data captured from student attendance purpose. Several articles, papers and journals has been reviewed from different research plethora of research publishers’ database through internet such as Google scholar, IEEExplore, Springerslink etc. This will make it easy for comparing several attendance systems with respect to internet of things, blockchain and artificial intelligence to address the research gap in this aspect. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots 18

Hybrid polynomial-based trajectory planning for lower limb e...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Li, Shupei Zhang, Chi Yi, Feng Lv, Pingping Yuan, Ting Zhang, Meitong University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science & Technology School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5th and 6th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method. © 2023 IEEE.

关键词： Trajectories

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Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning

Improving the Performance of Backward Chained Behavior Trees...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mart Kartasev Justin Salér Petter Ögren Robotics Perception and Learning Lab. School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Sweden

In this paper we show how to improve the performance of backward chained behavior trees (BTs) that include policies trained with reinforcement learning (RL). BTs represent a hierarchical and modular way of combining control policies into higher level control policies. Backward chaining is a design principle for the construction of BTs that combines reactivity with goal directed actions in a structured way. The backward chained structure has also enabled convergence proofs for BTs, identifying a set of local conditions to be satisfied for the convergence of all trajectories to a set of desired goal states. The key idea of this paper is to improve performance of backward chained BTs by using the conditions identified in a theoretical convergence proof to configure the RL problems for individual controllers. Specifically, previous analysis identified so-called active constraint conditions (ACCs), that should not be violated in order to avoid having to return to work on previously achieved subgoals. We propose a way to set up the RL problems, such that they do not only achieve each immediate subgoal, but also avoid violating the identified ACCs. The resulting performance improvement depends on how often ACC violations occurred before the change, and how much effort, in terms of execution time, was needed to re-achieve them. The proposed approach is illustrated in a dynamic simulation environment.

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Adaptive region reaching control of fully actuated ocean surface vessels

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Journal of Control and Decision 2021年第3期8卷 251-259页

作者： X.M.Sun S.S.Ge Q.Xu Y.Zhou X.W.Zheng School of Engineering Science and Technology Shanghai Ocean UniversityShanghai 201306People’s Republic of China Robotics Institute and School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China School of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore School of Electronic and Information Engineering Beihang UniversityBeijingPeople’s Republic of China

In this paper,region reaching controller is designed for fully actuated ocean surface vessels to reach a desired target region instead of a *** are not the requirements for both the pre-specified trajectory outside the desired region and the desired pinpoint position inside the desired *** controller design is based on the potential energy function,backstepping recursive design methodology,and the Lyapnov stability analysis *** the target region is specified arbitrarily small,the target region reduces to a point,and hence the region reaching control can be a generalisation of the setpoint *** results are presented to illustrate the performance of the proposed controller.

关键词： Reaching control potential energy function adaptive control backstepping technique Lyapunov stability

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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computer Modeling in Engineering & sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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VFL+: Low-Coupling Vertical Federated Learning With Privileged Information Paradigm

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Dai, Wei Cui, Teng Zhang, Tong Chen, Badong China University of Mining Technology School of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China

Vertical Federated Learning (VFL) enables the construction of models by combining clients with different features without compromising privacy. Existing VFL methods exhibit tightly coupled participant parameters, resulting in substantial interdependencies among clients during the prediction phase, which significantly hampers the model's usability. To tackle these challenges, this paper studies a VFL approach with low coupling of parameters between clients. Drawing inspiration from federated cooperation and teacher-supervised learning, we propose a low-coupling vertical federated learning with privileged information paradigm (VFL+), allowing participants to make autonomous predictions. Specifically, VFL+ treats information from other clients as privileged data during the training phase rather than the testing phase, thereby achieving independence in individual model predictions. Subsequently, this paper further investigates three typical scenarios of vertical cooperation and designs corresponding cooperative frameworks. Systematic experiments on real data sets demonstrate the effectiveness of the proposed method. © 2017 IEEE.

关键词： Federated learning

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Touch-less Remedial Game Prototype for Hand Rehabilitation - Feedback Results 23

Touch-less Remedial Game Prototype for Hand Rehabilitation -...

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Proceedings of the 35th Australian computer-Human Interaction Conference

作者： Rodney G Zsolczay Dimitri Perrin Frederic Maire Selen Türkay EECS QUT Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia Electrical Engineering & Robotics QUT Australia EECS Queensland University of Technology Australia

ISBN: (纸本)9798400717079

Remedial hand therapy can be required for many reasons, so burn injuries were used as a specific case for development of the remedial prototype of this paper. With burns, 80% of injuries involve the hands. Infection is also a constant concern with burns patients. In this paper, we sought feedback on our remedial prototype, from both professional hand therapists and past patients. Both groups gave very positive responses. With regard to burn injuries, the prototype had been developed with the following three considerations: 1) Touch-less hand control to reduce chances of infection. 2) computer vision datasets for damaged/bandaged hands, as most existing datasets target healthy hands. 3) Ensure that the remedial game is engaging to help distract from pain and provide motivation, thereby encouraging patients to perform their required remedial exercises. Our contribution is the continued development of an application for hand rehabilitation, where hand detection and classification were specifically developed for a minority group of damaged/bandaged hands. The feedback obtained here helps to focus further iterative development.

关键词： Game artifact computer vision. datasets hand rehabilitation remedial therapy

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Neural Gradient Learning and Optimization for Oriented Point Normal Estimation 23

Neural Gradient Learning and Optimization for Oriented Point...

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2023 SIGGRAPH Asia 2023 Conference Papers, SA 2023

作者： Li, Qing Feng, Huifang Shi, Kanle Fang, Yi Liu, Yu-Shen Han, Zhizhong School of Software Tsinghua University Beijing China School of Informatics Xiamen University Xiamen China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi United Arab Emirates Department of Computer Science Wayne State University Detroit United States

ISBN: (纸本)9798400703157

We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excellent gradient approximation properties for the underlying geometry of the data. We utilize a simple neural network to parameterize the objective function to produce gradients at points using a global implicit representation. However, the derived gradients usually drift away from the ground-truth oriented normals due to the lack of local detail descriptions. Therefore, we introduce Gradient Vector Optimization (GVO) to learn an angular distance field based on local plane geometry to refine the coarse gradient vectors. Finally, we formulate our method with a two-phase pipeline of coarse estimation followed by refinement. Moreover, we integrate two weighting functions, i.e., anisotropic kernel and inlier score, into the optimization to improve the robust and detail-preserving performance. Our method efficiently conducts global gradient approximation while achieving better accuracy and generalization ability of local feature description. This leads to a state-of-the-art normal estimator that is robust to noise, outliers and point density variations. Extensive evaluations show that our method outperforms previous works in both unoriented and oriented normal estimation on widely used benchmarks. The source code and pre-trained models are available at https://***/LeoQLi/NGLO . © 2023 ACM.

关键词： Multiobjective optimization

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Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers

arXiv

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arXiv 2023年

作者： Yi, Yuhao You, Ronghui Liu, Hong Liu, Changxin Wang, Yuan Lv, Jiancheng College of Computer Science Sichuan University China School of Statistics and Data Science Nankai University China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Robotics Hunan University China

Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed in ad-hoc manners, imposing extra barriers on comparing, analyzing, and improving the rules across performance criteria. This paper studies near-optimal aggregation rules using clustering in the presence of outliers. Our outlier-robust clustering approach utilizes geometric properties of the update vectors provided by workers. Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively. In addition, we discuss two contradicting types of attacks under which no single aggregation rule is guaranteed to improve upon the naive average. Based on the discussion, we propose a two-phase resilient aggregation framework. We run experiments for image classification using a non-convex loss function. The proposed algorithms outperform previously known aggregation rules by a large margin with both homogeneous and heterogeneous data distributions among non-faulty workers. Code and appendix are available at https://***/jerry907/AAAI24-RASHB. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic Engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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