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IEEE International Conference on Multimedia and Expo (ICME)

作者： Haoxuan Wang Ping Wei Shuaijia Chen Zhimin Liao Jialu Qin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

LiDAR-based point cloud segmentation is a significant and challenging task for 3D scene understanding. Recent voxel-based methods are often built on submanifold sparse residual calculation with small kernel size, which limits the local feature interactions and neglects the global contextual information. In this paper, we propose a local-to-global perception LiDAR-based point cloud segmentation network LGPSeg. From the local perspective, we design the Dynamic Spatial Aggregation Convolution to expand the receptive field range while avoiding a large increase in the model parameters. From the global perspective, we propose the BEV-Voxel Fusion to aggregate the global contextual information on the BEV feature maps through advanced 2D operators. By combining the local and global features, the 3D object and background information can be better captured. Our method achieves state-of-the-art results on two large datasets, SemanticKITTI and nuScenes, and even outperformed multimodal-based methods.

关键词： Point cloud compression Three-dimensional displays Convolution Semantic segmentation Aggregates Kernel

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Continuous Query-based Data Trading

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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework 4

Early Diabetes prediction with optimal feature selection usi...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Ashisha, G.R. Mary, X. Anitha Ashif, H. Mohamed Karthikeyan, I. Roshan, J. Karunya Institute of Technology and Sciences Electronics and Instrumentation Engineering Coimbatore India Karunya Institute of Technology and Sciences Robotics Engineering Coimbatore India Karunya Institute of Technology and Sciences Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300772

The notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. The use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an International Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. This growing disease requires a lot of attention. Machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. The major goal of this work is to design a model that can more accurately predict patients' diabetes. The research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. The result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes. © 2023 IEEE.

关键词： Forecasting

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Design and Analysis of Programmed Face Monitoring System

Design and Analysis of Programmed Face Monitoring System

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Electrical, Electronics, Communication and Computers (ELEXCOM), International Conference on

作者： Ashutosh Kewat Manjhi Amit Singh Rajawat Anshika Srivastava Department of Information Technology Artificial Intelligence and Robotics Centre for Artificial Intelligence (Artificial Intelligence and Data Science) Madhav Institute of Technology & Science Gwalior India

This study proposes a unique method for a programmed face monitoring system that makes use of a Proportional-Integral-Derivative (PID) controller and Mediapipe, a well-known computer vision framework. The system makes use of Mediapipe's real-time face detection technology and a PID controller algorithm to precisely monitor the identified face by dynamically adjusting the pan and tilt angles of a servo motor system. The usefulness of the proposed approach is demonstrated by the graphical representation depicting setting time, steady-state error and the oscillation type. The results were compared between Proportional-Integral-Derivative (PID), Proportional-Derivative (PD) and Proportional (P) controllers. The PID controller showed favorable results among all three of the controllers utilized in the face monitoring system. For PID controller-based faced monitoring system, the setting time of the system designed is obtained as 1 second, the steady-state error is low and the oscillation type observed is critically damped. The usage of this method for programmed face monitoring systems has a potential application in a number of fields, including robotics, human-computer interaction, and surveillance systems.

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Magnetically driven microrobots moving in a flow:a review

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Frontiers of Information Technology & Electronic Engineering 2023年第11期24卷 1520-1540页

作者： Jiamiao MIAO Xiaopu WANG Yan ZHOU Min YE Hongyu ZHAO Ruoyu XU Huihuan QIAN School of Science and Engineering the Chinese University of Hong Kong(Shenzhen)Shenzhen 518172China Shenzhen Institute of Artificial Intelligence and Robotics for Society(AIRS) Shenzhen 518129China

Magnetically driven microrobots hold great potential to perform specific tasks more locally and less invasively in the human *** reach the lesion area in vivo,microrobots should usually be navigated in flowing blood,which is much more complex than static ***,it is more challenging to design a corresponding precise control scheme.A considerable amount of work has been done regarding control of magnetic microrobots in a flow and the corresponding *** this paper,we review and summarize the state-of-the-art research progress concerning magnetic microrobots in blood flow,including the establishment of flow systems,dynamics modeling of motion,and control *** addition,current challenges and limitations are *** hope this work can shed light on the efficient control of microrobots in complex flow environments and accelerate the study of microrobots for clinical use.

关键词： Microrobot Flow Dynamics modeling Control

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3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation

arXiv

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arXiv 2024年

作者： Zhong, Xingguang Pan, Yue Stachniss, Cyrill Behley, Jens Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans. To this end, we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation, we extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids, a globally shared decoder, and time-dependent basis functions, which we jointly optimize in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans, we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach 1 on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete 3D maps, outperforming several state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots

Efficient and Accurate Transformer-Based 3D Shape Completion...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Federico Magistri Rodrigo Marcuzzi Elias Marks Matteo Sodano Jens Behley Cyrill Stachniss Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robots that operate in agricultural environments need a robust perception system that can deal with occlusions, which are naturally present in agricultural scenarios. In this paper, we address the problem of estimating 3D shapes of fruits when only partial observations are available. Generally speaking, such a shape completion can be realized by exploiting prior knowledge about the geometry of the fruit. This is typically done by template matching using traditional optimization algorithms, which are slow but accurate, or by encoding such knowledge into the weights of a neural network, leading to faster but often less accurate estimates. Our approach combines the best of both worlds. It exploits the benefit of having a template representing our object of interest with the advantages of using a neural network to learn how to deform a template. Our experimental evaluation demonstrates that our approach yields accurate estimation at a competitively low inference time in challenging greenhouse environments.

关键词： Knowledge engineering Geometry Accuracy Three-dimensional displays Shape Neural networks Green products

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Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 196-208页

作者： Yufei Li Yufei Xin Xinni Li Yinrui Zhang Cheng Liu Zhengwen Cao Shaoyi Du Lin Wang School of Information Science and Technology Northwest UniversityXi’an 710127Shaanxi ProvinceChina Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong UniversityXi’anShaanxi Province 710004China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applicationsand Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anShaanxi Province 710049China

Pneumonia is a serious disease that can be fatal,particularly among children and the *** accuracy of pneumonia diagnosis can be improved by combining artificial-intelligence technology with X-ray *** study proposes X-ODFCANet,which addresses the issues of low accuracy and excessive parameters in existing deep-learningbased pneumonia-classification *** network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution(ODConv)module,leveraging the residual module for feature extraction from X-ray *** feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate feature information from different spatial ***,the ODConv module extracts and fuses feature information in four dimensions:the spatial dimension of the convolution kernel,input and output channel quantities,and convolution kernel *** experimental results demonstrate that the proposed method can effectively improve the accuracy of pneumonia classification,which is 3.77%higher than that of *** model parameters are 4.45M,which was reduced by approximately 2.5 *** code is available at https://***/limuni/X ODFCA NET.

关键词： Pneumonia Coordinate attention Dynamic convolution ResNet18 X-ODFCANet

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Multi-level Spatio-temporal Matching Network for Multi-turn Response Selection in Retrieval-based Dialogue Systems 12

Multi-level Spatio-temporal Matching Network for Multi-turn ...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Ma, Mei Wang, Jianji Lan, Xuguang Zheng, Nanning Xi'An Jiaotong University Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi'an710049 China

ISBN: (纸本)9781665438582

The important task of multi-turn response selection in conversation systems must consider sufficient semantic information and spatio-temporal information when building retrieval-based chatbots. However, existing studies do not pay enough attention to both factors. In this study, a scheme of multi-turn response selection that combines a primary temporal matching module, an advanced temporal matching module, and a spatial matching module is proposed to extract matching information from context and response. The temporal matching modules progressively construct representations of the context and candidate responses at different granularities. Similarity matrices of the context and candidate responses are calculated and stacked using the spatial matching module. Convolutional neural network is then utilized to extract the spatial matching information. Finally, matching vectors of the three modules are fused to calculate the final matching score. Experimental results on two public datasets verify that our model can outperform state-of-the-art methods. © 2021 IEEE.

关键词： Semantics

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GROUNDING ROBOT POLICIES WITH VISUOMOTOR LANGUAGE GUIDANCE

arXiv

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arXiv 2024年

作者： Bucker, Arthur Ortega-Kral, Pablo Francis, Jonathan Oh, Jean Robotics Institute Carnegie Mellon University United States Bosch Center for Artificial Intelligence Germany

Recent advances in the fields of natural language processing and computer vision have shown great potential in understanding the underlying dynamics of the world from large-scale internet data. However, translating this knowledge into robotic systems remains an open challenge, given the scarcity of human-robot interactions and the lack of large-scale datasets of real-world robotic data. Previous robot learning approaches such as behavior cloning and reinforcement learning have shown great capabilities in learning robotic skills from human demonstrations or from scratch in specific environments. However, these approaches often require task-specific demonstrations or designing complex simulation environments, which limits the development of generalizable and robust policies for new settings. Aiming to address these limitations, we propose an agent-based framework for grounding robot policies to the current context, considering the constraints of a current robot and its environment using visuomotor-grounded language guidance. The proposed framework is composed of a set of conversational agents designed for specific roles-namely, high-level advisor, visual grounding, monitoring, and robotic agents. Given a base policy, the agents collectively generate guidance at run time to shift the action distribution of the base policy towards more desirable future states. We demonstrate that our approach can effectively guide manipulation policies to achieve significantly higher success rates both in simulation and in real-world experiments without the need for additional human demonstrations or extensive exploration. Project videos at https://***/view/motorcortex/home. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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