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SSRN 2024年

作者： Zhang, Yanying Liu, Peter xiaoping Hou, Wenguo School of Mechanical Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China School of Information Science and Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

In image-guided liver surgery, the registration of preoperative reconstructed liver model with intraoperative captured surfaces is extremely challenging, due to the limited visibility of liver surfaces and the impact of soft tissue deformation. In order to solve this problem, this paper presents a novel nonrigid registration method that achieves accurate alignment between the preoperative reconstructed volumetric liver mesh and a portion of the intraoperative captured liver surface. A deformation subspace is incorporated to characterize the deformation of volumetric liver mesh during registration, ensuring volume preservation of the liver mesh. The proposed method enables to capture both surface and inside deformation of the liver, thus improving the flexibility and accuracy of the *** majorization-minimization (MM) algorithm with limited-memory broyden fletcher goldfarb shanno (L-BFGS) is introduced to solve the optimization problem in the registration process, thereby effectively improves the computational efficiency of the non-rigid registration. The experimental results show that the volume relative error is less than 2% and the registration error is less than 1mm. The proposed method can reduce mesh distortion, and maintain organ volume. It provides a promising solution for aligning the liver mesh between preoperative and intraoperative periods. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Implementation of a Scalable Platform for Real-Time Monitoring of Machine Tools

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SSRN 2024年

作者： Tapia, Endika Lopez-Novoa, Unai Sastoque-Pinilla, Leonardo López-De-Lacalle, Luis Norberto Aeronautics Advanced Manufacturing Center University of the Basque Country Bizkaia Science and Technology Park 202 Zamudio48170 Spain Department of Computer Languages and Systems University of the Basque Country Rafael Moreno pasealekua 2-3 Bilbao48013 Spain Department of Mechanical Engineering University of the Basque Country Torres Quevedo enparantza s/n Bilbao48013 Spain

In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher data processing needs than the previous ones. © 2024, The Authors. All rights reserved.

关键词： Machine tools

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Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design

arXiv

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arXiv 2023年

作者： Liu, Bingyi Geromel, René Su, Zhaoxian Guo, Kai Wang, Yongtian Guo, Zhongyi Huang, Lingling Zentgraf, Thomas School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Physics Paderborn University Paderborn33098 Germany School of Optics and Photonics Beijing Engineering Research Center of Mixed Reality and Advanced Display Beijing Institute of Technology Beijing100081 China Institute for Photonic Quantum Systems Paderborn University Paderborn33098 Germany

In this work, we utilize thin dielectric meta-atoms placed on a silver substrate to efficiently enhance and manipulate the third harmonic generation. We theoretically and experimentally reveal that when the structural symmetry of the meta-atom is incompatible with the lattice symmetry of an array, some generalized nonlinear geometric phases appear, which offers new possibilities for harmonic generation control beyond the accessible symmetries governed by the selection rule. The underlying mechanism is attributed to the modified rotation of the effective principal axis of a dense meta-atom array, where the strong coupling among the units gives rise to a generalized linear geometric phase modulation on the pump light. Therefore, nonlinear geometric phases carried by the third-harmonic emissions are the natural result of the wave-mixing process among the modes excited at the fundamental frequency. This mechanism further points out a new strategy to predict the nonlinear geometric phases delivered by the nanostructures according to their linear responses. Our design is simple and efficient, and offers alternatives for the nonlinear meta-devices that are capable of flexible photon generation and manipulation. © 2023, CC BY.

关键词： Harmonic generation

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Merging Parameter Estimation and Classification Using LASSO

arXiv

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arXiv 2024年

作者： Wang, Le Wang, Ying Qiu, Yu Li, Mian Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China Centre for Advanced Bio Production Stockholm10044 Sweden SAIC Motor RD Innovation Headquarters Shanghai201807 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not a priori be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness. Copyright © 2024, The Authors. All rights reserved.

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Federated Edge Learning via Integrated Sensing, Computation, and Communication

Federated Edge Learning via Integrated Sensing, Computation,...

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IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

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Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network 10th

Weapon Engagement Zone Maximum Launch Range Estimation Using...

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10th Brazilian Conference on Intelligent systems, BRACIS 2021

作者： Dantas, Joao P. A. Costa, Andre N. Geraldo, Diego Maximo, Marcos R. O. A. Yoneyama, Takashi Decision Support Systems Subdivision Institute for Advanced Studies SP Sao Jose dos Campos12.288-001 Brazil Computer Science Division Aeronautics Institute of Technology SP Sao Jose dos Campos12228-900 Brazil Electronic Engineering Division Aeronautics Institute of Technology SP Sao Jose dos Campos12228-900 Brazil

ISBN: (纸本)9783030916985

This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i.e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a non-discretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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A Reinforcement Learning Based Approach to Identify Resource Bottlenecks for Multiple Services Interactions in Cloud Computing Environments 16th

A Reinforcement Learning Based Approach to Identify Resource...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Xu, Lingxiao Xu, Minxian Semmes, Richard Li, Hui Mu, Hong Gui, Shuangquan Tian, Wenhong Wu, Kui Buyya, Rajkumar School of Software and Information Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Co. Ltd. Chengdu China Department of Computer Science University of Victoria Victoria Canada CLOUDS Lab School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9783030675394

Cloud service providers are provisioning resources including a variety of virtual machine instances to support customers that migrate their services to the cloud. From the customers’ perspective, selecting the appropriate amount of resources is tightly coupled with performance and cost. By identifying the potential resource bottlenecks in the early stage of the service deployment process, resource planning can be significantly optimized. However, due to the unpredictable workloads and heterogeneous resources, it is difficult to identify resource bottlenecks that can degrade system performance. To support system non-functional requirements (NFR) in a better manner, we propose a reinforcement learning based approach to support the NFR management of system concerning the multiple services interactions scenario by identifying the potential resource bottleneck and optimizing the demanded resources. The proposed approach can predict the resource bottleneck for multiple services interactions, e.g. bottleneck in CPU or overloads in specific service, and provide guidance for resource planning. We modeled and simulated the proposed approach using an extended version of the CloudSim toolkit. Comprehensive evaluations with realistic use case from Siemens Digital Industries Software’s MindSphere Solution on AliCloud show that our proposed approach can achieve high accuracy in terms of performance metrics, such as response time, queries per second (QPS), and resource usage. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

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Dynamic targets searching assistance based on virtual camera priority

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Virtual Reality & Intelligent Hardware 2021年第6期3卷 484-500页

作者： Zixiang ZHAO Quanwei ZHOU Xiaoguang HAN Lili WANG State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100191China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518052China The Chinese University of Hong Kong Shenzhen 999077China Shenzhen Research Institute of Big Data Shenzhen 999077China

Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality *** studies can help users with auxiliary tools,such as top view maps or trails,and exploration guidance,for example,automatically generated paths according to the user location and important static spots in virtual ***,in some virtual reality applications,when the scene has complex occlusions,and the user cannot obtain any real-time position information of the dynamic target,the above assistance cannot help the user complete the task more *** We design a virtual camera priority-based assistance to help the user search dynamic targets *** of forcing users to go to destinations,we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for *** assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps,which capture the depth information of the scene and the locations of the dynamic *** method automatically analyzes the priority of these virtual cameras,chooses the destination,and generates an instant path to assist the user in finding the dynamic *** method is suitable for various virtual reality applications that do not require manual supervision or *** A user study is designed to evaluate the proposed *** results indicate that compared with three conventional navigation methods,such as the top-view method,our method can help users find dynamic targets more *** advantages include reducing the task completion time,reducing the number of resets,increasing the average distance between resets,and reducing user task *** We presented a method for improving dynamic target searching efficiency in virtual scenes by virtual cam

关键词： Searching assistance Virtual environment Path guidance Redirection

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises

Design and Application of a One-Stop Mobile Application Shar...

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Chinese Control Conference (CCC)

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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A Framework of Internet of Things (IOT) for the Manufacturing and Image Classifaication System 2

A Framework of Internet of Things (IOT) for the Manufacturin...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Gupta, Ayan Das Rafi, Shaik Mohammad Singh, Nagendra Gupta, Vikram Kumar Jaiswal, Sushma Gangodkar, Durgaprasad Chandernagore Government College Department of Geography West Bengal Hooghly India Chandernagore Government College Affiliated to the University of Burdwan India Artificial Intelligence and Information Technology Sri Mittapalli College of Engineering Andhrapradesh Guntur India Embedded Systems Centre for Development of Advanced Computing Noida India Quality and Reliability IIT Kharagpur India CG Bilaspur India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Real-time identification of industrial processes and real-time image classification are necessary for recent days to reduce the excess power consumption and detect water pollution respectively. Usually, robotic technology in manufacturing industries is a costly initiative;thus, researchers are searching for a low-cost and effective loT (Internet of Things) technology that will detect and measure real time-condition of industrial machines. Moreover, the loT technology is useful in image classification as well for detecting water pollution. This research has explained the benefits and cost-effectiveness of available loT along with a comparison between image classification techniques. A primary quantitative survey method has been carried out to collect practical information based on the judgement of 60 respondents. Thereafter, the 'Purposive' sampling technique has been applied to discuss the primary data and uphold a significant judgement. Findings and analysis suggested that WSN (Wireless Sensor Network) is a cheaper technology that can be integrated into small- and large-scale manufacturing industries. Image classification loT has been identified for detecting water pollution where texture analysis is cheaper than spatiotemporal analysis. © 2022 IEEE.

关键词： Internet of things

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