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Optimization of 3D Holographic Projection for a Medical Image System using Taguchi Method and Fuzzy Inference System

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Journal of Imaging science and Technology 2023年第4期67卷 1-7页

作者： Chou, Ming-Chung Huang, Tian-Hsiang Tsai, Jinn-Tsong Chen, Yen-Ming J. Hwang, Kao-Shing Huang, Wei-Tai Ho, Wen-Hsien Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan

Two dimensional projections of three dimensional (3D) objects are used to teach anatomy. These projections are relatively unrealistic, and medical students cannot easily and accurately grasp the position and shape of each anatomical structure. As a remedy, 3D holographic projections allow for 3D anatomical structures to be projected in space without the use of worn contraptions. This study designed a method for optimizing 3D holographic projections with respect to the quality characteristics of 3D imaging definition and stereoscopic quality. The Taguchi method was used to determine the factors that most greatly influenced these quality characteristics, and the Delphi method and fuzzy theory were used to quantify image quality. Among three main factors of material of the projection panel, thickness of the projection panel, and angle made by the projection panel with the horizontal plane, thickness was the most influential. The optimal combination of factors was acrylic material, a thickness of 1 mm, and an angle of 45°. This paper's method aids medical pedagogy and can be used by the holographic projection manufacturing industry to optimize 3D imaging definition and stereoscopic quality. © 2023 Society for Imaging science and Technology.

关键词： Taguchi methods

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Evolutionary Approach to Security Games with Signaling 31

Evolutionary Approach to Security Games with Signaling

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31st International Joint Conference on Artificial Intelligence, IJCai 2022

作者： Żychowski, Adam Mańdziuk, Jacek Bondi, Elizabeth Venugopal, Aravind Tambe, Milind Ravindran, Balaraman Faculty of Mathematics and Information Science Warsaw University of Technology Poland Center for Research on Computation and Society Harvard University United States Robert Bosch Centre for Data Science and AI IIT Madras India Department of Computer Science and Engineering IIT Madras India

ISBN: (纸本)9781956792003

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Chromosomes

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SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning 41

SF-DQN: Provable Knowledge Transfer using Successor Feature ...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Shuai Fernando, Heshan Devaka Liu, Miao Murugesan, Keerthiram Lu, Songtao Chen, Pin-Yu Chen, Tianyi Wang, Meng Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States IBM Thomas J.Watson Research Center IBM Research Yorktown HeightsNY United States

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition *** this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and the latter characterizes the task-specific reward *** Q-function decomposition, coupled with a policy improvement operator known as generalized policy improvement (GPI), reduces the sample complexity of finding the optimal Q-function, and thus the SF & GPI framework exhibits promising empirical performance compared to traditional RL methods like ***, its theoretical foundations remain largely unestablished, especially when learning the successor features using deep neural networks (SF-DQN).This paper studies the provable knowledge transfer using SFs-DQN in transfer RL *** establish the first convergence analysis with provable generalization guarantees for SF-DQN with *** theory reveals that SF-DQN with GPI outperforms conventional RL approaches, such as deep Q-network, in terms of both faster convergence rate and better *** experiments on real and synthetic RL tasks support the superior performance of SF-DQN & GPI, aligning with our theoretical findings. Copyright 2024 by the author(s)

关键词： Deep reinforcement learning

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The present situation and shifts observed in wetlands within the *** Seaway region of Canada,utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine

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Big Earth data 2025年第1期9卷 47-71页

作者： Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh Digital Environmental Group WSP Canada Inc.OttawaONCanada Department of Computer Science Chalmers University of TechnologyGothenburgSweden Digital Environmental Group WSP Canada Inc.EdmontonABCanada dIndependent Researcher Department of Photogrammetry and Remote Sensing Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina

This study examined wetland trends in the *** Seaway(~500,000 km^(2))in Canada over the past four *** this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were *** samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged *** spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to ***,a change analysis was conducted to calculate the loss and gain of different wetland ***,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,*** was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 ***,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an ***,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),*** study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.

关键词： Remote sensing Google Earth Engine(GEE) cloud computing satellite change detection continuous change detection and classification(CCDC) wetlands

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Exploring Visibility-Based Shading Methods for Graphics and Visualization 15

Exploring Visibility-Based Shading Methods for Graphics and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Anandhasilambarasan, D. Seth, Kanika Aravind, Ar Goyal, Prakhar Kantharaj, I.K. Ingle, Yashwant S. Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350370249

Visibility-based shading techniques for laptop snapshots and Visualization can provide realistic and perceptual shading consequences with the aid of utilizing data from the scene's visibility shape. Those methods comprise visibility statistics to compute the approximate illumination of an object or scene, thinking about occlusion consequences from other objects. Visibility-based totally shading algorithms permit more practical actual-time renderings that extra accurately fit the way people perceive the illumination of scenes. Moreover, these techniques offer performance blessings over global illumination-based total methods, such as improved memory burden and faster computation instances, making them appropriate for plenty of applications. Popular visibility-based strategies, including ray casting and direct illumination, are based totally on the idea that each surface is ideally diffuse and homogeneous, which means their absorption and reflection coefficients are constant. To overcome this issue, a range of new techniques integrate visibility and physics-based total fashions, accounting for fabric homes (such as specular highlights) in the illumination computation. This combination of visibility and physics-based fashions can be, in addition, improved via incorporating mild shipping simulation strategies, inclusive of bidirectional reflectance distribution features and course tracing, to capture better and predict the rendering of actual international materials and surfaces. © 2024 IEEE.

关键词： Consequences Perceptual Rendering Shading Utilizing Visualization

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MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence 41

MOKD: Cross-domain Finetuning for Few-shot Classification vi...

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41st International Conference on Machine Learning, ICML 2024

作者： Tian, Hongduan Liu, Feng Liu, Tongliang Du, Bo Cheung, Yiu-Ming Han, Bo TMLR Group Hong Kong Baptist University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong TMLR Group University of Melbourne Australia Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan Univeristy China

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD. Copyright 2024 by the author(s)

关键词： Labeled data

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Video Stabilization via Prediction with Time-Series Network and Image Inpainting with Pyramid Fusion

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Chinese Journal of Electronics 2021年第6期30卷 1103-1110页

作者： CHENG Keyang LI Shichao RONG Lan WANG Wenshan SHI Wenxi ZHAN Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center Cyber Space Security Academy of Jiangsu University National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Xinjiang Lianhaichuangzhi Information Technology Co. Ltd.

Due to the poor filling effect of the video image defect commonly used in the video stabilization field, the video is seemed still unstable after the image stabilization process, which seriously affects the visual effect. To solve this problem, we improve a video stabilization method based on time-series network prediction and pyramid fusion restoration is proposed to optimize the visual effect after image *** flow of the proposed method is as follows: First,it is adaptive to determine whether the defect of the corresponding frame at the current time needs padding inpainting. Then, for the frame that needs to be inpainting,the frames generated before the current moment are sent to the model combining the convolutional neural networks and the gate recurrent unit to predict the part to be filled. Next the current defect image and the complete image to be filled are brought into the Laplacian pyramid reconstruction, and the improved weighted optimal suture is introduced for splicing during the fusion. Finally, the video frame is cut after reconstruction. The method is tested on a data set composed of videos commonly used in the field of video stabilization. The experimental results show that the average peak signal to noise ratio of the method is 2 to 5 d B higher than that of the comparison algorithm, and the average structural similarity index is improved by about 2% to 7% compared with the contrast algorithm.

关键词： image restoration image sequences video frame cutting improved weighted optimal suture gate recurrent unit video signal processing video stabilization method image fusion Laplacian pyramid reconstruction padding inpainting image resolution neural nets convolutional neural networks video image defect pyramid fusion restoration image inpainting visual effect optimization image reconstruction time-series network prediction image stabilization process

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Intelligent Control of an Integrated Energy System: Low Carbon Modeling and Intelligent Optimization

Intelligent Control of an Integrated Energy System: Low Carb...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Zezheng Fu Wenting Lin Degang Yang College of Computer and Information Science Chongqing Normal University Chongqing China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

An integrated energy system is a complex system that requires intelligent control to optimize its operation. This paper proposes an intelligent control method for integrated energy systems based on a low carbon model. The model integrates a variety of low carbon technologies such as carbon capture systems, demand response, carbon emissions trading, and clean energy generation. The effect of the proposed intelligent control methods on reducing system costs and carbon emissions is verified through a case study. The results show that the combination of the intelligent control method and the low carbon model leads to a significant reduction in system operating costs and carbon emissions. This provides an important reference for intelligent control of the existing integrated energy system from a low-carbon approach.

关键词： Green energy Costs Carbon dioxide Emissions trading Mathematical models Demand response Sustainable development Informatics Optimization Intelligent control

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