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28th International Conference on Neural Information Processing, ICONIP 2021

作者： Nartey, Obed Tettey Yang, Guowu Agyapong, Dorothy Araba Yakoba Wu, JinZhao Sarpong, Asare K. Frempong, Lady Nadia Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Electronic Information Guangxi University Nanning530004 China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Medicine Department of Radiology and Imaging Sciences Emory University 1364 Clitton Rd. AtlantaGA30322 United States

ISBN: (纸本)9783030921842

Semi-supervised learning algorithms make use of both labelled training data and unlabelled data. However, the visual domain gap between these sets poses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of generating noisy pseudo-labels and placing over-confident labelling belief on incorrect classes leads to deviation from the solution. To solve this challenge, the study investigates a curriculum-styled approach for deep semi-supervised segmentation which relaxes and treats pseudo-labels as continuous hidden variables by developing an adaptive pseudo-label generation strategy to jointly optimized the pseudo-label generation and selection process. A regularization scheme is further proposed to smoothen the probability outputs and sharpen the less represented pseudo-label regions. The proposed method was evaluated on three publicly available computer Tomography (CT) scan benchmarks and extensive experiments on all modules have demonstrated the efficacy of the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Curricula

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Comparative performance of machine learning models for temperature prediction in vanadium redox flow batteries

Comparative performance of machine learning models for tempe...

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International Conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)

作者： Anxiu Zhang Filbert H. Juwono Hongfei Li Erick Purwanto W. K. Wong Linfeng Liu Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China

ISBN: (数字)9781837242863

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction.

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Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation

arXiv

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arXiv 2023年

作者： Li, Kehan Zhao, Yian Wang, Zhennan Cheng, Zesen Jin, Peng Ji, Xiangyang Yuan, Li Liu, Chang Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China Dalian University of Technology Dalian China

Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions with manually labeled object masks. In this work, we reveal that informative interactions can be made by simulation with semantic-consistent yet diverse region exploration in an unsupervised paradigm. Concretely, we introduce a Multi-granularity Interaction Simulation (MIS) approach to open up a promising direction for unsupervised interactive segmentation. Drawing on the high-quality dense features produced by recent self-supervised models, we propose to gradually merge patches or regions with similar features to form more extensive regions and thus, every merged region serves as a semantic-meaningful multi-granularity proposal. By randomly sampling these proposals and simulating possible interactions based on them, we provide meaningful interaction at multiple granularities to teach the model to understand interactions. Our MIS significantly outperforms non-deep learning unsupervised methods and is even comparable with some previous deep-supervised methods without any annotation. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Ultrathin-shell epitaxial Ag@Au core-shell nanowires for high-performance and chemically-stable electronic, optical, and mechanical devices

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Nano Research 2021年第11期14卷 4294-4303页

作者： Yangzhi Zhu Sanggon Kim Xuezhi Ma Peter Byrley Ning Yu Qiushi Liu Xiaoming Sun Da Xu Sangshan Peng Martin C.Hartel Shiming Zhang Vadim Jucaud Mehmet R.Dokmeci Ali Khademhosseini Ruoxue Yan Department of Chemical and Environmental Engineering Bourns College of EngineeringUniversity of California-RiversideRiversideCA92521USA Terasaki Institute for Biomedical Innovation Los AngelesCA90064USA Department of Electrical and Computer Engineering University of California-RiversideRiversideCA92521USA State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringHunan UniversityChangsha410082China Department of Bioengineering University of California-Los AngelesLos AngelesCA90095USA Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Material Science and Engineering Program University of California-RiversideRiversideCA92521USA

Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to their unique plasmonic, mechanical, and electronic properties. However, the lifetime, reliability, and operating conditions of AgNW-based devices are significantly restricted by their poor chemical stability, limiting their commercial potentials. Therefore, it is crucial to create a reliable oxidation barrier on AgNWs that provides long-term chemical stability to various optical, electrical, and mechanical devices while maintaining their high performance. Here we report a room-temperature solution-phase approach to grow an ultra-thin, epitaxial gold coating on AgNWs to effectively shield the Ag surface from environmental oxidation. The Ag@Au core-shell nanowires (Ag@Au NWs) remain stable in air for over six months, under elevated temperature and humidity (80 °C and 100% humidity) for twelve weeks, in physiological buffer solutions for three weeks, and can survive overnight treatment of an oxidative solution (2% H2O2). The Ag@Au core-shell NWs demonstrated comparable performance as pristine AgNWs in various electronic, optical, and mechanical devices, such as transparent mesh electrodes, surface-enhanced Raman spectroscopy (SERS) substrates, plasmonic waveguides, plasmonic nanofocusing probes, and high-aspect-ratio, high-resolution atomic force microscopy (AFM) probes. These Au@Ag core-shell NWs offer a universal solution towards chemically-stable AgNW-based devices without compromising material property or device performance.

关键词： epitaxial growth core-shell nanowire plasmonic waveguides atomic force microscopy(AFM)probe transparent electrode wearable electronics

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Sum-Rate Maximization for Intelligent Reflecting Surface Assisted MIMO SWIPT Systems

Sum-Rate Maximization for Intelligent Reflecting Surface Ass...

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Future Telecommunication Technologies (SOFTT), Symposium on

作者： H. J. Liong C. W. R. Chiong Lenin Gopal Filbert H. Juwono Department of Electrical and Computer Engineering Curtin University Malaysia Miri Sarawak Malaysia Department of Electrical and Electronic Engineering University of Southampton Malaysia Eko Galleria Nusajaya Johor Malaysia Computer Science Program University of Southampton Malaysia Eko Galleria Nusajaya Johor Malaysia

ISBN: (纸本)9781665455985

Intelligent reflecting surface (IRS) has recently stimulated an upsurge of research interest due to its capability of enhancing the spectral and energy efficiencies for future sixth generation (6G) wireless communication systems. This motivates the integration of IRS into simultaneous wireless information and power transfer (SWIPT) systems to improve the performance of energy harvesting and information de-coding. To unleash the full potential of IRS and achieve high sum-rate, the optimal joint transmitter precoding and reflective beamforming design is crucial yet challenging to solve. The issue has not been well investigated and the solution remains elusive. Hence, this research aims to jointly optimize the transmitter precoding and reflective beamforming matrices for IRS-assisted multi-user multiple-input multiple-output (MIMO) SWIPT systems under quality of service (QoS) constraints. Simulation results demonstrate the effectiveness of IRS in enhancing the system performance.

关键词： Wireless communication Transmitters Array signal processing Precoding System performance Simulation Quality of service

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Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning

arXiv

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arXiv 2023年

作者： Jin, Peng Huang, Jinfa Xiong, Pengfei Tian, Shangxuan Liu, Chang Ji, Xiangyang Yuan, Li Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School Shenzhen China Shopee Shenzhen China Department of Automation BNRist Tsinghua University Beijing China

Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity. Concretely, we propose Hierarchical Banzhaf Interaction (HBI) to value possible correspondence between video frames and text words for sensitive and explainable cross-modal contrast. To efficiently realize the cooperative game of multiple video frames and multiple text words, the proposed method clusters the original video frames (text words) and computes the Banzhaf Interaction between the merged tokens. By stacking token merge modules, we achieve cooperative games at different semantic levels. Extensive experiments on commonly used text-video retrieval and video-question answering benchmarks with superior performances justify the efficacy of our HBI. More encouragingly, it can also serve as a visualization tool to promote the understanding of cross-modal interaction, which have a far-reaching impact on the community. Project page is available at https://***/HBI/. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Post-Operative Recovery Process Assessment of Total Hip Arthroplasty with Instrumented Implant

Post-Operative Recovery Process Assessment of Total Hip Arth...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Carlos Rodrigues Miguel Correia João Abrantes Marco Rodrigues Jurandir Nadal C-BER - Centre for Biomedical Engineering Research INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal MovLab - Laboratory of Technologies for Interactions and Interfaces CICANT - Centre for Research in Applied Communication Culture and New Technologies Lisbon Portugal Department of Electronic and Systems Federal University of Pernambuco Recife Brazil Biomedical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This study presents variability assessment of real time measurements from in-vivo internal joint loads with instrumented implant during post-operative (PO) recovery process from total hip arthroplasty on daily living gait activities. A total of 112 trials walking supported by crutches in both hands, contralateral and ipsilateral sides, walking on treadmill at constant velocities, accelerating, decelerating and free walking, were assessed from 9 different patients ranging 0.3 to 76-month PO. Variability was assessed based on standard deviation of the vertical joint load normalized to each subject body weight with this metric adequacy to monitor PO recover.

关键词： Legged locomotion Temperature measurement Force measurement Instruments Force Time series analysis Implants

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Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation

Multi-granularity Interaction Simulation for Unsupervised In...

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International Conference on computer Vision (ICCV)

作者： Kehan Li Yian Zhao Zhennan Wang Zesen Cheng Peng Jin Xiangyang Ji Li Yuan Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China

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Data Acquisition for a Magnetic Levitation Vehicle

Data Acquisition for a Magnetic Levitation Vehicle

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IEEE Annual Southern Power electronics Conference (SPEC)

作者： Gabriel Klajman Messer Elkin Ferney Rodriguez Velandia Richard Magdalena Stephan César Augusto Gonçalves Amado Program of Electrical Engineering (PEE) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Electrical Engineering (DEE) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Electronic and Computer Engineering (DEL) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil

The main objective of this paper is to discuss the data acquisition of the MagLev-Cobra magnetic levitation vehicle. The vehicle is located at the Technology Center of the Federal University of Rio de Janeiro. It was inaugurated in 2014 and is the first vehicle of its kind to use superconducting levitation in the world. As in other technological systems, it is extremely important to analyze the magnitudes related to its operation and efficiency. In this work, the main characteristics of the MagLev-Cobra are discussed, in addition to the analysis of experimental data of the magnitudes to be sensed.

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IoT–based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor

IoT–based tire pressure monitoring system for air and tempe...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Agung Pangestu Iing Sodikin Muhammad Yusro Rum Sapundani Rosyid Ridlo Al Hakim Sinka Wilyanti Electrical Engineering Department Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia Department of Electronic Engineering Faculty of Engineering State University of Jakarta Jakarta Indonesia Electromedical Engineering Technology Faculty of Health Science University of Muhammadiyah Purwokerto Purwokerto Indonesia Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (纸本)9781665453905

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1—the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2—the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-time via a smartphone using the IoT-based.

关键词： Temperature measurement Temperature distribution Thermometers Microcontrollers Tire pressure Real-time systems Pressure gauges

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