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Simple and Smart Flow Injection Type Water Quality Meter Driven by Brilliant Colored Reagent 22

Simple and Smart Flow Injection Type Water Quality Meter Dri...

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22nd International conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Kawakami, Masayuki Kasama, Toshihiro Sato, Tomomi Takai, Madoka Yano, Daisaku Tazawa, Hidekatsu Maehara, Kaito Murakami, Hiroshi Miyake, Ryo The University Of Tokyo Japan Organo Corporation Japan Institute Of Microchemical Technology Co. Ltd. Japan Next Computer System Engineering Co. Ltd. Japan

ISBN: (纸本)9784886864352

This paper reports a truly simple and small flow injection-type water quality meter to watch residual level of chlorine in tap-water. A new measurement method by using a mixture of target reagent and the opposite-colored dye is proposed. The dye's colorimetric data brings the information of added reagent volume and flow speed along micro reaction channel. Using this, chlorine colorimetric data can be corrected, combined with advective diffusion model. This enables to abolish rigid and high precision flow system perfectly without losing analytical performance. © 2023 IEEJ.

关键词： Flow injection Residual chlorine Water quality meter

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Behavior and Scalability of the Regional Climate Model RegCM4 on high performance Computing Platforms 13th

Behavior and Scalability of the Regional Climate Model RegCM...

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13th International conference on Large-Scale ScientificComputations (LSSC)

作者： Ivanov, Vladimir Gadzhev, Georgi Bulgarian Acad Sci Natl Inst Geophys Geodesy & Geog Acad G Bonchev St Bl 3 Sofia 1113 Bulgaria

ISBN: (纸本)9783030975494;9783030975487

The RegCM is a regional climate model used in many studies. There are simulation runs in different domains, time periods, and regions in the world on all continents. The research works in our group are related to the historical and future climate, and its influence on the human sensation over Southeast Europe. We used the model versions 4.4 and 4.7. The main model components are the initial and boundary condition module, the physics processes parametrization module, and the dynamical core. Concerning the last one, we used the default one - the hydrostatic option corresponding to the MM5 model dynamical core. We run simulations with different combinations of parametrization schemes on the Bulgarian supercomputer Avitohol. The newer versions of the model have an additional option for using a non-hydrostatic dynamical core. The running of model simulations with different input configurations depends highly on the available computing resources. Several main factors influence the simulation times and storage requirements. They could vary much depending on the particular set of input parameters, domain area, land cover, processing cores characteristics, and their number in parallel processing simulations. The objective of that study is to analyse the RegCM model performance with hydrostatic core, and non-hydrostatic core, on the high-performance Computing platform Avitohol.

关键词： Regional climate simulation RegCM4 Scalability high performance computing

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Estimation Technique for Rotor Time Constant of Induction Machines During I/f Speed Control 11

Estimation Technique for Rotor Time Constant of Induction Ma...

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11th International conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Oh, Yun-Jai Yoon, Young-Doo Hong, Chan-Ook Choi, Seung-Cheol Hanyang University Department of Automotive Engineering Automotive-Computer Convergence Korea Republic of Hanyang University Department of Automotive Engineering Korea Republic of Ls Electric Korea Republic of

ISBN: (纸本)9788957083505

This paper proposes a method for estimating the rotor time constant of induction motors in the rotation state when there is no position or speed sensor. Since rotor time constant is used for vector controls and sensorless drives, accurate rotor time constant is necessary for high-performance drives. The proposed method estimates the rotor time constant through the relationship between the stator current and the stator flux. The stator flux is obtained through a flux observer. I/f speed control was used to estimate the rotor time constant in the rotating state. The proposed estimation method was tested using a 3.7 kW squirrel cage induction motor. © 2023 The Korean Institute of Power Electronics.

关键词： Induction machine

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Distilling Estonian Text Domains for Production-Oriented Machine Translation 24

Distilling Estonian Text Domains for Production-Oriented Mac...

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24th Nordic conference on Computational Linguistics, NoDaLiDa 2023

作者： Korotkova, Elizaveta Fishel, Mark Institute of Computer Science University of Tartu Estonia

ISBN: (纸本)9789916219997

This paper explores knowledge distillation for multi-domain neural machine translation (NMT). We focus on the Estonian-English translation direction and experiment with distilling the knowledge of multiple domain-specific teacher models into a single student model that is tiny and efficient. Our experiments use a large parallel dataset of 18 million sentence pairs, consisting of 10 corpora, divided into 6 domain groups based on source similarity, and incorporate forward-translated monolingual data. Results show that tiny student models can cope with multiple domains even in case of large corpora, with different approaches benefiting frequent and low-resource domains. © 2023 Association for Computational Linguistics.

关键词： Neural machine translation

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A Deep Wavelet Network for high-Resolution Microscopy Hyperspectral Image Reconstruction 17th

A Deep Wavelet Network for High-Resolution Microscopy Hyper...

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17th European conference on computer Vision, ECCV 2022

作者： Wang, Qian Chen, Zhao School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

ISBN: (纸本)9783031250811

Microscopy hyperspectral imaging (MHSI) integrates conventional imaging with spectroscopy to capture images through numbers of narrow spectral bands, and has attracted much attention in histopathology image analysis. However, due to the current hardware limitation, the observed microscopy hyperspectral images (MHSIs) has much lower spatial resolution than the coupled RGB images. high-resolution MHSI (HR-MHSI) reconstruction by fusing low-resolution MHSIs (LR-MHSIs) with high-resolution (HR) RGB images can compensate for the information loss. Unfortunately, radiometrical inconsistency between the two modalities often causes spectral and spatial distortions which degrade quality of the reconstructed images. To address these issues, we propose a Deep Wavelet Network (WaveNet) based on spectral grouping and feature adaptation for HR-MHSI reconstruction. For preservation of spectral information, the band-group-wise adaptation framework exploits intrinsic spectral correlation and decompose MHSI reconstruction in separate spectral groups. In spatial domain, we design the mutual adaptation block to jointly fuse the wavelet and convolution features by wavelet-attention for high-frequency information extraction. Specifically, to effectively inject spatial details from HR-RGB to LR-MHSI, radiometry-aware alignment is conducted between the two modalities. We compare WaveNet with several state-of-the-art methods by their performance on a public pathological MHSI dataset. Experimental results demonstrate its efficacy and reliability under different settings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hyperspectral imaging

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Prompt-Based Data Augmentation Using Contrastive Learning Under Scarcity of Annotated Data 27

Prompt-Based Data Augmentation Using Contrastive Learning Un...

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27th European conference on Artificial Intelligence, ECAI 2024

作者： Ul Haq, Muhammad Uzair Rigoni, Davide Sperduti, Alessandro Department of Mathematics "Tullio Levi-Civita" University of Padova Via Trieste 63 Padova35121 Italy Human Inspired Technology Research Centre University of Padova Via Luzzatti 4 Padova35122 Italy Amajor SB S.p.A Via Noventana 192 Noventa Padovana35027 Italy Department of Pharmaceutical and Pharmacological Sciences University of Padova Via Marzolo 5 Padova35131 Italy Augmented Intelligence Center Bruno Kessler Foundation Via Sommarive 18 Povo38123 Italy Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 TN Povo38123 Italy

ISBN: (纸本)9781643685489

Named Entity Recognition is a crucial task in Natural Language Processing (NLP) which aims to identify the entities in text. Given an adequate amount of annotated data, Large Language Models (LLMs) have been shown to be effective in this task when fine-tuned. However, the performance of LLMs is severely affected when annotated datasets are limited. To alleviate this problem, adding synthetic data via Data Augmentation (DA) techniques is a viable approach. Even so, DA for token-level tasks suffers from two main limitations: (i) token-label misalignment problem;and (ii) quality of generated synthetic data. In this paper, we propose a novel prompt-based DA approach using contrastive learning. The proposed method can generate high-quality synthetic data while preserving the token-label correspondences. Experimental results demonstrate that the proposed approach, when compared against multiple baselines on well-known Named Entity Recognition (NER) datasets, achieves State-of-the-Art performance. © 2024 The Authors.

关键词： Contrastive Learning

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TINYCOD: Tiny and Effective Model for Camouflaged Object Detection 48

TINYCOD: Tiny and Effective Model for Camouflaged Object Det...

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48th IEEE International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xing, Haozhe Gao, Shuyong Tang, Hao Mok, Tsui Qin Kang, Yanlan Zhang, Wenqiang Information Technology and Electrical Engineering Eth Zurich Zurich Switzerland Fudan University Academy for Engineering & Technology Shanghai China Fudan University School of Computer Science Shanghai China

ISBN: (纸本)9781728163277

关键词： Camouflaged object detection edge features feature fusion image segmentation lightweight model

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TTCNet: Transformer and Tubular Convolution Feature Attention Network for OCTA vessel segmentation

TTCNet: Transformer and Tubular Convolution Feature Attentio...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Chengzhang Yang, Zhangzheng Xiao, Yalong Wu, Tao Zou, Beiji Zhou, Haoze Central South University School of Computer Science and Engineering Changsha China Central South University School of Humanities Changsha China Traditional Chinese Medicine Hospital of Yueyang Yueyang China Zhejiang University Eye Center Second Affiliated Hospital School of Medicine Hangzhou China

ISBN: (纸本)9798350386226

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique that can reveal blood flow and the detailed structure of the retinal and choroidal vasculature. However, uneven image quality and the high complexity of blood vessel distribution bring great challenges to accurate blood vessel segmentation. In this paper, we propose an OCTA blood vessel segmentation network based on the classic encoder-decoder architecture called TTCNet, which integrates a dual-branch encoder to capture global context information effectively. In addition, Snake Detail Attention Block is designed to enhance the perception of the tubular structure of blood vessels and provide richer feature representation in the network. We also design a module named Pyramid Feature Attention Module in each level of skip connection to better extract feature information at different scales. Experiments are conducted on two public datasets, OCTA_6M and OCTA_3M. G-mean Score and Area Under the Curve of our network on these two datasets are 93.05, 93.18, and 95.21, 94.95%, respectively, achieving better results than other state-of-the-art methods. © 2024 IEEE.

关键词： Optical coherence tomography

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Establishment of an Effective Brain Tumor Classification System through Image Transformations and Optimization Techniques 1

Establishment of an Effective Brain Tumor Classification Sys...

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1st IEEE International conference on Innovations in high Speed Communication and Signal Processing, IHCSP 2023

作者： Arora, Gaurav Kumar Taj Mahaboob, Shaik Adilakshmi, S. Rani, Sasi Kala Kasthuri, A. Mamodiya, Udit IBM India Pvt. Ltd Uttar Pradesh Greater Noida India Jntua College of Engineering Department of ECE Andhra Pradesh Pulivendula India Vignana Bharathi Institute of Technology Department of Data Science Telangana India Sri Krishna College of Engineering and Technology Department Computer Science and Engineering Coimbatore India Vinayaka Mission's Kirupananda Variyar Engineering College Department Computer Science and Engineering Salem India Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350345957

In recent years, researchers and health care professionals have been interested in medical image fusion, which combines images of the human body, organs, and cells with general information to address medical difficulties. computer-aided imaging techniques offer quantitative assessment of images under validation, which helps doctors make unbiased, objective decisions quickly. Multi-sensor and multi-source image fusion models also diversify healthcare analysis characteristics. It led to strong information processing that reveals otherwise invisible details. The fused pictures can help pinpoint anomalies. Multimodal image fusion combines complementing pictures into one. The fused image is ideal for visual perception or computer segmentation, feature extraction, and object recognition. The goal is to create a single fused image with more information than the source photos alone. The fused image combines the source images' complementary and shared elements, delivering enhanced subjective and objective detail. In biomedical imaging, fusing multimodal images has shown useful for disease characterisation. Medical image analysis uses CT, MRI, and PET (PET). In this research paper, we developed a Multimodality Image Fusion using Centre based genetic algorithm (CBGA) and fuzzy logic (FL) technique with lifting wavelet transform (LWT) for fusion of spatially registered MRI and CT images. We also proposed an efficient Multimodality Medical Image Fusion system based on DWT and binary crow search optimization (BCSO) algorithm for fusing MRI and CT images. The performance of the suggested models is validated against many benchmark medical datasets. The experiment showed that the proposed models outperformed all others. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters 38

Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Dom...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Dong Zang, Qi Wang, Shuang Sebe, Nicu Zhong, Zhun School of Artificial Intelligence Xidian University Shaanxi China Department of Information Engineering and Computer Science University of Trento Italy School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Presently, pseudo-labeling stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-labels. However, we identify two key limitations within this paradigm: (1) under relatively severe domain shifts, most selected reliable pixels appear speckled and remain noisy. (2) when dealing with wild data, some pixels belonging to the open-set class may exhibit high confidence and also appear speckled. These two points make it difficult for the pixel-level selection mechanism to identify and correct these speckled close- and open-set noises. As a result, error accumulation is continuously introduced into subsequent self-training, leading to inefficiencies in pseudo-labeling. To address these limitations, we propose a novel method called Semantic Connectivity-driven Pseudo-labeling (SeCo). SeCo formulates pseudo-labels at the connectivity level, which makes it easier to locate and correct closed and open set noise. Specifically, SeCo comprises two key components: Pixel Semantic Aggregation (PSA) and Semantic Connectivity Correction (SCC). Initially, PSA categorizes semantics into "stuff" and "things" categories and aggregates speckled pseudo-labels into semantic connectivity through efficient interaction with the Segment Anything Model (SAM). This enables us not only to obtain accurate boundaries but also simplifies noise localization. Subsequently, SCC introduces a simple connectivity classification task, which enables us to locate and correct connectivity noise with the guidance of loss distribution. Extensive experiments demonstrate that SeCo can be flexibly applied to various cross-domain semantic segmentation tasks, i.e. domain generalization and domain adaptation, even including source-free, and black-box domain adaptation, significantly improving the performance of existing state-of-the-art methods. The code is available at https://***/DZhaoXd/SeCo. © 2024 Neural information

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