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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yusuke Fukuda Akira Takebayashi Hiroaki Yamamoto Toru Kano Dept. Major in Computer and Information Sience Ibaraki University Ibaraki Japan Medical Engineering Lab. Saitama Japan Tsuda Dental Clinic Ibaraki Japan Dept. of Mechanical Systems Engineering Ibaraki University Ibaraki Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Surgical guide plates, which are used in dental implant treatments, have some limitations that cause problems for the operator and patient. This study aims to develop an augmented-reality-based surgical guide system to solve these problems and discusses its use in clinical practice. Our surgical guide system achieved a good accuracy in drilling simulations on training typodonts, demonstrating its potential in clinical practice.

关键词： Training Drilling Electric potential Accuracy Surgery Implants Dentistry Consumer electronics

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Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding

arXiv

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arXiv 2023年

作者： Hwang, Minyoung Jeong, Jaeyeon Kim, Minsoo Oh, Yoonseon Oh, Songhwai Electrical and Computer Engineering and ASRI Seoul National University Korea Republic of Department of Electronic Engineering Hanyang University Korea Republic of Interdisciplinary Major in Artificial Intelligence Seoul National University Korea Republic of

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outperforms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1%andSPLby 20.6%againstthe state-of-the-art method of the SOON benchmark. Project page: https://***/projects/Meta-Explore/*** © 2023, CC BY-NC-SA.

关键词： Navigation

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Meta-learning Based Obstructive Sleep Apnea Detection Using Single-Lead ECG Signals

Meta-learning Based Obstructive Sleep Apnea Detection Using ...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Yinxian He Yu Zhou Kyungtae Kang Dept. of Computer Science and Engineering Major in Bio Artificial Intelligence Hanyang University Ansan Republic of Korea Dept. of Artificial Intelligence Hanyang University Ansan Republic of Korea

As a respiratory syndrome correlated with some cardiovascular diseases, obstructive sleep apnea (OSA) not only destroys the quality of our sleep, but also induces a variety of major chronic diseases such as heart disease, and diabetes, and even causes sudden death during sleep. Many studies have been conducted on the classification of OSA from normal events by machine learning. However, we found that differences in patients caused by the individuality of the ECG patterns and variability in the ECG do not create optimal rules for OSA classification by ECG signals. It is necessary to reduce the impact of individual differences in classification. In this study, we propose a method based on meta-learning to detect OSA using a 2D time-frequency scalogram that is converted from a single-lead ECG signal. According to the experiment results, we achieved 68.10% accuracy, 69.19% sensitivity, 67.71% specificity, and a 0.694 F1 score. The results showed this method based on meta-learning using the Siamese network is feasible without being pre-trained by massive 2D scalogram representations.

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Method for Quantum Denoisers Using Convolutional Neural Network

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Computational Intelligence and Neuroscience 2022年第0期2022卷 4885897页

作者： Kim, Bong-Hyun Madhavi, S. School of Software Computer Engineering Major Seowon University 377-3 Musimseo-ro Seowon-gu Cheongju-si Chungcheongbuk-do28674 Korea Republic of Computer Science and Engineering Department PVP Siddhartha Institute of Technology Andhra Pradesh Kanuru India

In many applications of quantum information science, high-dimensional entanglement is needed. Quantum teleportation is used for transferring information from one place to another using Einstein-Podolsk-Rosen pairs (EPR) and two classical bits of communication in a channel. Since we cannot produce multiple copies of an unknown state for amplification, we will generate multiple EPR pairs. However, after the distribution of the EPR pairs, they will have decreased fidelity with the ideal EPR state. So, to maintain the quantum states and maximize the quantification of the entanglement without losing the strength of the states, we propose to denoise the channel for a few types of noise. We created a random noise source and filtered out the irrelevant information without affecting the relevant information encoded in the quantum states. The proposed model is used for successful denoising of GHZ states from spin flips and bit flip errors. Much of the research work is not carried out by using machine-language-based neural networks for noise-reduction in quantum channels. In this paper, we propose a denoiser called quantum denoiser CNQD, which uses a feedforward convolution neural network model. We tuned our model with highly entangled GHZ states with zero phases and phase between [0, ∏] mixed with different kinds of noise. Finally, the proposed model can be used for optimal quantum communication via noisy quantum channels using GHZ states. © 2022 Bong-Hyun Kim and S. Madhavi.

关键词： Quantum entanglement

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Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data

arXiv

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arXiv 2022年

作者： Ahn, Seyoung Kim, Soohyeong Kwon, Yongseok Youn, Jiseung Park, Joohan Cho, Sunghyun Department of Computer Science and Engineering Major in Bio Artificial Intelligence Hanyang University 15588 Korea Republic of Department of Computer Science and Engineering Hanyang University ERICA 15588 Korea Republic of 34129 Korea Republic of

In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem. To address this problem, we propose a novel diffusion strategy for machine learning (ML) models (FedDif) to maximize the performance of the global model with non-IID data. FedDif enables the local model to learn different distributions before parameter aggregation by passing the local models through users via device-to-device communication. Furthermore, we theoretically demonstrate that FedDif can circumvent the weight-divergence problem. Based on this theory, we propose a communication-efficient diffusion strategy for ML models that can determine the trade-off between learning performance and communication cost using auction theory. The experimental results show that FedDif improves the top-1 test accuracy by up to 34.89% and reduces communication costs by 14.6% to a maximum of 63.49%.MSC Codes 68T05 Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning

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Orchestrated desaturation reprogramming from stearoyl-CoA desaturase to fatty acid desaturase 2 in cancer epithelial-mesenchymal transition and metastasis

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Cancer Communications 2025年第3期45卷 245-280页

作者： Zhicong Chen Yanqing Gong Fukai Chen Hyeon Jeong Lee Jinqin Qian Jing Zhao Wenpeng Zhang Yamin Li Yihui Zhou Qiaobing Xu Yu Xia Liqun Zhou Ji-Xin Cheng Department of Obstetrics and Gynecology Center for Reproductive MedicineGuangdong Provincial Key Laboratory of Major Obstetric DiseasesGuangdong Provincial Clinical Research Center for Obstetrics and GynecologyGuangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal MedicineThe Third Affiliated HospitalGuangzhou Medical UniversityGuangzhouGuangdongP.R.China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMassachusettsUSA Department of Urology Peking University First HospitalBeijingP.R.China College of Biomedical Engineering&Instrument Science Key Laboratory for Biomedical Engineering of Ministry of EducationZhejiang UniversityHangzhouZhejiangP.R.China MOE Key Laboratory of Bioorganic Phosphorus Chemistry&Chemical Biology Department of ChemistryTsinghua UniversityBeijingP.R.China State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijingP.R.China Department of Biomedical Engineering Tufts UniversityMedfordMassachusettsUSA

Background:Adaptative desaturation in fatty acid(FA)is an emerging hallmark of cancer metabolic *** such as stearoyl-CoA desaturase(SCD)and fatty acid desaturase 2(FADS2)have been implicated in multiple cancers,and their dominant and compensatory effects have recently been ***,how tumors initiate and sustain their self-sufficient FA desaturation to maintain phenotypic transition remains *** study aimed to explore the molecular orchestration of SCD and FADS2 and their specific reprogramming mechanisms in response to cancer ***:The potential interactions between SCD and FADS2 were explored by bioinformatics analyses across multiple cancer cohorts,which guided subsequent functional and mechanistic *** expression levels of desaturases were investigated with online datasets and validated in both cancer tissues and cell *** desaturation activities were characterized through various isomer-resolved lipidomics methods and sensitivity assays using desaturase ***-situ lipid profilingwas conducted using multiplex stimulated Raman scattering *** assays were performed both in vitro and in vivo,with RNA-sequencing employed for the mechanism ***:After integration of the RNA-protein-metabolite levels,the data revealed that a reprogramming fromSCD-dependent to FADS2-dependent desaturation was linked to cancer epithelial-mesenchymal transition(EMT)and progression in both patients and cell ***2 overexpression and SCD suppression concurrently maintained EMT plasticity.A FADS2/β-catenin selfreinforcing feedback loop facilitated the degree of lipid unsaturation,membrane fluidity,metastatic potential and EMT ***,SCD inhibition triggered a lethal apoptosis but boosted survival plasticity by inducing EMT and enhancing FA uptake via adenosine monophosphate-activated protein kinase ***,this desaturation reprogramming increased tra

关键词： metabolic reprogramming stearoyl-CoA desaturase fatty acid desaturase 2 epithelialmesenchymal transition cancer metastasis

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ReRobo Walker: Robotic Walker with Fall Detection and Active Safety

ReRobo Walker: Robotic Walker with Fall Detection and Active...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Damin Ding Yagang Wang Houji Zhang Qiaoling Meng Hongliu Yu Yanzhao Lin Qiu Chen Rehabilitation Engineering and Technology Institute School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Rehabilitation Engineering and Technology Institute School of Optical-Electrical School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Guangzhou Foreign Language School Guangzhou China Rehabilitation Engineering and Technology Institute University of Shanghai for Science and Technology Shanghai China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Major of Electrical Engineering and Electronics Graduate School of Engineering Kogakuin University Tokyo Japan

In recent years, Chinese society has become increasingly ageing while the fertility rate continues to decline. This situation has led to a growing public demand for rehabilitation devices such as walking aids. A novel intelligent robotic walker named ReRobo Walker is proposed which can assist groups of elderly people with dysfunctional legs with rehabilitation training and indoor and outdoor walking. We design robust mechanical structures for robotic walker, install special 3D force sensor, 2D LIDAR and other sensors, and design new algorithms to enable intelligent functionality while guaranteeing the safety of robotic walker.(1) Real-time monitoring of the user’s physical status, such as falls, through laser range sensors and 3D force sensor; (2) Modelling of the scene through 2D LIDAR for path planning, obstacle avoidance and navigation functions; (3) Precise control through LADRC-based algorithm for uphill assistance, downhill control and prevention of sharp shifts of the robotic walker. Experiment results demonstrate the solid mechanical structure, stable reliability and the effectiveness of intelligent control algorithms of the intelligent robotic walker.

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Jiawei Tian Seungho Lee Kyungtae Kang Dept. of Computer Science and Engineering Major in Bio Artificial Intelligence Hanyang University Ansan-si South Korea Dept. of Applied Artificial Intelligence Hanyang University Ansan-si South Korea Dept. of Artificial Intelligence Hanyang University Ansan-si South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of Science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes.

关键词： Radiography Ultrasonic imaging Reviews Precision medicine Surgery Medical services Transforms Real-time systems Medical diagnostic imaging Diseases

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Comprehensive Study on Cherenkov Radiation for Ultrafast PET Detectors, Considering Refractive Index and Wavelength

Comprehensive Study on Cherenkov Radiation for Ultrafast PET...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： H.S. Shim H. Kang J. Kim J.S. Lee Y. Jeong Interdisciplinary Program of Bioengineering Seoul National University Seoul Republic of Korea Integrated Major in Innovative Medical Science Seoul National University Seoul Republic of Korea Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea

This study aims to investigate the characteristics of Cherenkov radiation with respect to wavelength and refractive index. Refractive index is a function of wavelength, and when it changes, not only does the gain of Cherenkov radiation increase, but also the photon loss in the medium increases. Therefore, we examine how photons of different wavelengths behave under changing refractive index conditions. We adjust the refractive index to maintain a constant emission angle of Cherenkov radiation based on the deceleration rate of electrons moving in the medium. When electrons generating Cherenkov radiation move a total of 200 μm, both long-wavelength and short-wavelength photons are emitted at the initial stage; however, only short-wavelength photons are emitted at the late stage. Although the shorter-wavelength photons arrive first among the photons that reach the detector, the longer-wavelength photons are detected more, forming a significant peak, which is due to the fact that the former experience more absorption-based photon loss in the medium than the latter.

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Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition

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computer Modeling in engineering & Sciences 2020年第2期122卷 505-523页

作者： Lei Chen Kanghu Bo Feifei Lee Qiu Chen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China. Algorithm Department UnisocShanghai201203China. Major of Electrical Engineering and Electronics Graduate School of EngineeringKogakuin UniversitySinjuku-kuTokyo163-8677Japan.

Scene recognition is a popular open problem in the computer vision *** lots of methods proposed in recent years,Convolutional Neural Network(CNN)based approaches achieve the best performance in scene *** propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network(Multi-CNN)for scene *** existing works that usually use individual convolutional neural network,a fusion of multiple different convolutional neural networks is applied for scene ***,we split training images in two directions and apply to three deep CNN model,and then extract features from the last full-connected(FC)layer and probabilistic layer on each ***,feature vectors are fused with different fusion strategies in groups forwarded into SoftMax *** proposed algorithm is evaluated on three scene datasets for scene *** experimental results demonstrate the effectiveness of proposed algorithm compared with other state-of-art approaches.

关键词： Scene recognition deep feature fusion multiple convolutional neural network.

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