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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ryan Hare Ying Tang Wei Cui Joleen Liang Department of Electrical and Computer Engineering Rowan University Glassboro USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Squirrel AI Learning Shanghai China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.

关键词： Games Databases Forestry Education Problem-solving Presses Artificial intelligence

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Stressnet: Deep learning to predict stress with fracture propagation in brittle materials

arXiv

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arXiv 2020年

作者： Wang, Yinan Oyen, Diane Guo, Weihong Mehta, Anishi Scott, Cory Braker Panda, Nishant Giselle, M. Fernández-Godino Srinivasan, Gowri Yue, Xiaowei Grado Department of Industrial and Systems Engineering Virginia Tech BlacksburgVA24061 United States Los Alamos National Laboratory Los AlamosNM87544 United States Department of Industrial and Systems Engineering Rutgers University New BrunswickNJ08901 United States College of Computing Georgia Institute of Technology GA30332 United States Department of Computer Science University of California Irvine IrvineCA92697 United States

Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a novel deep learning model, "StressNet," is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 hours, with an average MAPE of 2% relative to test data. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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Scenario referring expression comprehension via attributes of vision and language 2nd

Scenario referring expression comprehension via attributes o...

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2nd Chinese Conference on Pattern Recognition and computer Vision, PRCV 2019

作者： Wei, Shaonan Wang, Jianming Sun, Yukuan Jin, Guanghao Liang, Jiayu Liu, Kunliang School of Electronics and Information Engineering Tianjin Polytechnic University Tianjin China School of Computer Science and Technology Tianjin Polytechnic University Tianjin China Tianjin International Joint Research and Development Center of Autonomous Intelligence Technology and Systems Tianjin Polytechnic University Tianjin China Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin Polytechnic University Tianjin China Center for Engineering Internship and Training Tianjin Polytechnic University Tianjin China

ISBN: (纸本)9783030317256

Referring Expression Comprehension (REC) is a task that requires to indicate particular objects within an image by natural language expressions. Previous studies on this task have assumed that the language expression and the image are one-to-one correspondence, that is, the language refers to the target region must exist in the current image and then the region with the highest score will be located, no matter whether they match or not. However, in practical applications, REC is required to locate the reference target region from a series of matched, semi-matched and mismatched scene image sequences. It is the 3D version of this challenge that refers to as Scenario Referring Expression Comprehension (SREC) in this paper. To accomplish such a task, we made a testset based on the existing real-scenario dataset enhancement, constructed a Dual Attributes Recursive Retrieve Reasoning Model (DA3R) for the first time with the Attributes of both images and expressions, and finally verified the feasibility of the method on the testset by assess with three different types of enhanced expression. © Springer Nature Switzerland AG 2019.

关键词： Image enhancement

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Erratum to “A Nonlinear Receiver Leveraging Cascaded Inverter-Based Envelope-Biased LNAs for In-Band Interference Suppression in the Amplitude Domain” [Nov 21 3360-3374]

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IEEE Journal of Solid-State Circuits 2022年第2期57卷 671-671页

作者： Dawei Ye Rongjin Xu C.-J. Richard Shi State Key Laboratory of ASIC and System Fudan University Shanghai China Institute of Brain-Inspired Circuits and Systems (iBiCAS) Fudan University Shanghai China MOE Frontiers Center for Brain Science Fudan University Shanghai China Department of Electrical and Computer Engineering University of Washington Seattle WA USA

In the above article [1] , Fig. 4 is wrong. The correct one is shown below:

关键词： Interference suppression Internet of Things CMOS analog integrated circuits Low-noise amplifiers

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An efficient approach to improve security for mapreduce computation in cloud system 18

An efficient approach to improve security for mapreduce comp...

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2018 International Conference on Learning and Optimization Algorithms: Theory and Applications, LOPAL 2018

作者： Bennasar, Hanane Bendahmane, Ahmed Essaaidi, Mohammad Computer Science and Systems Engineering Laboratory Abdelmalek Essaadi University Tetouan Morocco Mohamed V University Rabat Morocco

ISBN: (纸本)9781450353045

Running MapReduce computation in public cloud raises a series of security challenges since the service providers may not be properly protected. Due to the fact that the MapReduce applications are long-running, which increases the chance of an attacker to massively perform malicious attacks by exploiting the workers vulnerability, many workers may be compromised. Those workers could misbehave and thereby tamper the results integrity of all computations assigned to them. To tackle this challenge, this paper proposes an effective Result Verification Mechanism (RVM) using a reputation threshold-based voting method to ensure the result integrity of MapReduce on the map and reduce phases. Therefore, render the MapReduce computation accurate. Another major contribution of this paper is that we implement RVM based on Apache Hadoop and perform a series of experiments. The evaluation study of the experimental results demonstrate that RVM can significantly reduce computation overhead and guarantee a low error rate as compared to the simple voting method like m-first voting. © 2018 Association for Computing Machinery.

关键词： MapReduce

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Micropatterned piezoelectric films induced by modulated corona field for weak signal detection

引用

Chemical engineering Journal 2025年 517卷

作者： Fu Lv Chenghang Yu Bing Liu Jiamin Lin Zijian Hong Yongjun Wu Zhaohui Ren Qilong Zhang Shurong Dong Jikui Luo Juan Li Yuhui Huang School of Materials Science and Engineering Cyrus Tang Center for Sensor Materials and Applications Innovation Center of Yangtze Delta Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou Zhejiang 310058 China Nanhu Brain-computer Interface Institute Hangzhou Zhejiang 311100 China School of Engineering Hangzhou City University Hangzhou Zhejiang 310015 China College of Electronic Information and Engineering Hangzhou Dianzi University Hangzhou Zhejiang 310018 China Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Taizhou Institute of Zhejiang University Taizhou Zhejiang 318000 China College of Information Science and Electronic Engineering Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou Zhejiang 310027 China College of Materials and Science Engineering Zhejiang University of Technology Hangzhou Zhejiang 310014 China

Enhancing the inherent piezoelectricity and introducing innovative structural designs are the two fundamental strategies for enhancing the pressure sensitivity of P(VDF-TrFE)-based sensors. In this study, we propose a scalable and cost-effective manufacturing approach that utilizes a modulated corona field to create micropatterns on the surface of P(VDF-TrFE) films, and enhance their piezoelectric properties during the annealing and recrystallization process. The micropatterned P(VDF-TrFE) films exhibit a distinctive “wavy” microstructure and possess ultrahigh piezoelectric properties (d 33 ∼ 35.6 pC/N), resulting in excellent sensitivity (∼477.94 mV/kPa). To unravel the underlying mechanism responsible for this superior performance, we employ finite element modeling and phase-field simulations. Our findings reveal that the modulated corona field generates an equipotential surface with a characteristic “wavy” distribution and promotes the creation of nucleation sites for the polar β phase in P(VDF-TrFE) films. A real-time monitoring system is further exploited with the obtained micropatterned P(VDF-TrFE) films for detecting radial artery pulse signals. The results obtained from this work hold great promise for the development of highly sensitive sensors, with wide-ranging applications in healthcare and environmental monitoring.

关键词： Flexible wireless biosensors Micropatterned structure Phase-field simulations Piezoelectric polymer

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MBLLEn: Low-light image/video enhancement using CNNs 29

MBLLEn: Low-light image/video enhancement using CNNs

引用

29th British Machine Vision Conference, BMVC 2018

作者： Lv, Feifan Lu, Feng Wu, Jianhua Lim, Chongsoon State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Panasonic R and D Center Singapore Singapore City Singapore

We present a deep learning based method for low-light image enhancement. This problem is challenging due to the difficulty in handling various factors simultaneously including brightness, contrast, artifacts and noise. To address this task, we propose the multi-branch low-light enhancement network (MBLLEN). The key idea is to extract rich features up to different levels, so that we can apply enhancement via multiple subnets and finally produce the output image via multi-branch fusion. In this manner, image quality is improved from different aspects. Through extensive experiments, our proposed MBLLEN is found to outperform the state-of-art techniques by a large margin. We additionally show that our method can be directly extended to handle low-light videos. © 2018. The copyright of this document resides with its authors.

关键词： Image enhancement

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A hierarchical power routing scheme for interlinking converters in unbalanced hybrid AC-DC microgrids 44

A hierarchical power routing scheme for interlinking convert...

引用

44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Mahmoudian Esfahani, Mohammad Habib, Hany F. Mohammed, Osama A. Energy Systems Research Laboratory Department of Electrical and Computer Engineering Florida International University MiamiFL United States

ISBN: (纸本)9781509066841

In this paper, a hierarchical power routing scheme for Interlinking Converters (ICs) in unbalanced hybrid AC-DC microgrids is proposed. The proposed scheme aims to minimize power losses in the microgrid and increase the system loadability at the point of common coupling (PCC). Control variables of this optimization problem are active and reactive power bias factors of all three-phase ICs. At first, the optimization problem minimizes the power losses in the microgrid, afterward a supervisory control scheme is applied to maximize the loadability at the PCC by assigning the three phase power mismatches at the PCC to ICs. The obtained results from implementing the proposed strategy on the modified IEEE 13 bus system are compared with other strategies which are aimed to just maximize loadability or minimize power losses to show the validity and advantages of proposed method. © 2018 IEEE.

关键词： Hierarchical systems

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3DTeethSeg’22: 3D Teeth Scan Segmentation and Labeling Challenge

arXiv

引用

arXiv 2023年

作者： Ben-Hamadou, Achraf Smaoui, Oussama Rekik, Ahmed Pujades, Sergi Boyer, Edmond Lim, Hoyeon Kim, Minchang Lee, Minkyung Chung, Minyoung Shin, Yeong-Gil Leclercq, Mathieu Cevidanes, Lucia Prieto, Juan Carlos Zhuang, Shaojie Wei, Guangshun Cui, Zhiming Zhou, Yuanfeng Dascalu, Tudor Ibragimov, Bulat Yong, Tae-Hoon Ahn, Hong-Gi Kim, Wan Han, Jae-Hwan Choi, Byungsun van Nistelrooij, Niels Kempers, Steven Vinayahalingam, Shankeeth Strippoli, Julien Thollot, Aurélien Setbon, Hugo Trosset, Cyril Ladroit, Edouard Centre de Recherche en Numérique de Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Technopôle de Sfax Sfax3021 Tunisia Udini 37 BD Aristide Briand Aix-en-Provence13100 France Inria Univ. Grenoble Alpes CNRS Grenoble INP LJK France Department of Computer Science and Engineering Seoul National University Korea Republic of School of Software Soongsil University Korea Republic of University of Michigan Ann Arbor United States University of North Carolina Chapel Hill United States Shandong University China ShanghaiTech University China Department of Computer Science University of Copenhagen Denmark Osstem Implant Co. Ltd. Seoul Korea Republic of Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre P.O. Box 9101 Nijmegen6500 HB Netherlands Department of Computing Science Radboud University Nijmegen Netherlands Department of Artificial Intelligence Radboud University Nijmegen Netherlands Berlin Institute of Health at Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg’22 challenge was organized in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg’22 challenge. The 3DTeethSeg’22 challenge code can be accessed at: https://***/abenhamadou/3DTeethSeg22 challenge. © 2023, CC BY-SA.

关键词： Object detection

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Adaptive optics two-photon microscopy for in vivo imaging of mouse retina 6

Adaptive optics two-photon microscopy for in vivo imaging of...

引用

Adaptive Optics and Wavefront Control for Biological systems VI 2020

作者： Qin, Zhongya He, Sicong Chen, Congping Yang, Chao Yung, Jasmine Wu, Wanjie Leung, Christopher K. Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health School of Science and Institute for Advanced Study Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781510632592

Non-invasive retinal imaging has greatly facilitated the research of eye disease and neurodegenerative disorders in the central nervous system (CNS). Two-photon microscopy is a powerful tool for in vivo imaging of mouse retina because it provides intrinsic optical sectioning capability and the infrared laser is less likely to excite the photoreceptors. However, the dilated mouse eye has large optical aberrations, which must be corrected to achieve high-resolution or even diffraction-limited imaging. Here, we developed an adaptive optics (AO) two-photon microscope for in vivo imaging of retinal neurons through the eyeball of living mouse. We used the two-photon excited fluorescence signal of retina as the guide star to measure and correct the aberration of mouse eye. After AO correction, the fluorescence signal was increased by at least fivefold and the fine structures such as axons of retinal ganglion cells (RGC) were clearly resolved. To take advantage of the non-invasive high-resolution imaging, we demonstrated functional calcium imaging of RGC responding to the light stimulations. © 2020 SPIE.

关键词： Adaptive optics

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