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ChemSystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

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Group maintenance strategy for stochastic selective maintenance

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International Journal of Wireless and Mobile Computing 2018年第4期14卷 362-368页

作者： Zhao, Jing Zeng, Jianchao Division of Industrial and System Engineering Taiyuan University of Science of Technology Taiyuan030024 China College of Information Engineering Guangdong Mechanical and Electrical Polytechnic Guangzhou510515 China School of Computer Science and Control Engineering North University of China Taiyuan030051 China

Selective maintenance is often applied to many industrial environments when the maintenance actions are performed between sequence missions. When the length of maintenance or work mission time is stochastic and there are multiple maintenance workers with different capacity, previous works to determine maintenance and mission times are unsuitable for it. Assuming that the time of work mission is random and maintenance time is determined, to maximise system reliability of next work mission, this paper proposes a stochastic model for a two-state system comprising several series-parallel components with multiple maintenance workers under the maintenance time limit. The optimal maintenance plan is obtained by hybrid intelligent optimisation algorithm. The validity and feasibility of the model are verified by the simulation. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Stochastic models

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Multimodal transformer with multi-view visual representation for image captioning

arXiv

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arXiv 2019年

作者： Yu, Jun Li, Jing Yu, Zhou Huang, Qingming Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China

Image captioning aims to automatically generate a natural language description of a given image, and most state-ofthe-art models have adopted an encoder-decoder framework. The framework consists of a convolution neural network (CNN)-based image encoder that extracts region-based visual features from the input image, and an recurrent neural network (RNN)-based caption decoder that generates the output caption words based on the visual features with the attention mechanism. Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intramodal interactions. Inspired by the success of the Transformer model in machine translation, here we extend it to a Multimodal Transformer (MT) model for image captioning. Compared to existing image captioning approaches, the MT model simultaneously captures intra- and inter-modal interactions in a unified attention block. Due to the in-depth modular composition of such attention blocks, the MT model can perform complex multimodal reasoning and output accurate captions. Moreover, to further improve the image captioning performance, multi-view visual features are seamlessly introduced into the MT model. We quantitatively and qualitatively evaluate our approach using the benchmark MSCOCO image captioning dataset and conduct extensive ablation studies to investigate the reasons behind its effectiveness. The experimental results show that our method significantly outperforms the previous state-of-the-art methods. With an ensemble of seven models, our solution ranks the 1st place on the real-time leaderboard of the MSCOCO image captioning challenge at the time of the writing of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior

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Sensors 2025年第10期25卷 2966-2966页

作者： Hossain, Delwar Thomas, J. Graham McCrory, Megan A. Higgins, Janine Sazonov, Edward Department of Electrical and Computer Engineering University of Alabama TuscaloosaAL35401 United States Weight Control and Diabetes Research Center The Miriam Hospital ProvidenceRI02903 United States Department of Psychiatry and Human Behavior Warren Alpert Medical School of Brown University ProvidenceRI02903 United States Department of Health Sciences Boston University BostonMA02215 United States Department of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States

The dynamic process of eating—including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics—may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The PRISMA 2020 guidelines were followed to review the full texts of 161 scientific manuscripts. The contributions of this review article are twofold: (i) A taxonomy of sensors for quantifying various aspects of eating behavior is established, classifying the types of sensors used (such as acoustic, motion, strain, distance, physiological, cameras, and others). (ii) The accuracy of measurement devices and methods is assessed. The review highlights the advantages and limitations of methods that measure and monitor different eating metrics using a combination of sensor modalities and machine learning algorithms. Furthermore, it emphasizes the importance of testing these methods outside of restricted laboratory conditions, and it highlights the necessity of further research to develop privacy-preserving approaches, such as filtering out non-food-related sounds or images, to ensure user confidentiality and comfort. The review concludes with a discussion of challenges and future trends in the use of sensors for monitoring eating behavior. © 2025 by the authors.

关键词： Motion sensors

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Constrained stabilization on the n−sphere ?

arXiv

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arXiv 2019年

作者： Berkane, Soulaimane Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this note, we solve the problem of constrained stabilization on the n−dimensional unit sphere by mapping it to a navigation problem on the Euclidean space Rn in the presence of spherical obstacles. As a consequence, any controller that was originally designed for navigating in Euclidean sphere worlds can be used to solve the stabilization problem on the n−sphere in the presence of conic constraints. A simulation example on the 2−sphere illustrates the effectiveness of the proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spheres

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Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment

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Informatics in Medicine Unlocked 2020年 18卷

作者： Mirbagheri, Mahya Hakimi, Naser Ebrahimzadeh, Elias Setarehdan, S. Kamaledin Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Seaman Family MR Research Centre University of Calgary Calgary Alberta Canada Department of Clinical Neurosciences University of Calgary Calgary Alberta Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary Alberta Canada

Background: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique for studying brain hemodynamics. Since brain hemodynamics also involves components from the heart rate (HR), it is possible to extract the HR signal from the fNIRS (EHR). By removing global systemic changes from the EHR, we hypothesized that the EHR includes physiological information beyond HR, which is the functional response of the brain. Method: To evaluate this hypothesis, the mental states of 10 healthy subjects were assessed under a stressful condition induced using the Montreal Imaging Stress Task (MIST). EHR and Reference HR (RHR) signals were then extracted from fNIRS and ECG, respectively. Next, a method based on Independent Component Analysis (ICA) was applied in order to discriminate the independent sources in the EHR signal. Thereafter, the least correlated components with RHR were reconstructed, which is the so-called target signal of EHR¯. Results: Similarity measurement of EHR¯ with fNIRS signals revealed a significant correlation between them. To show the effectiveness of the existing information in EHR¯ signals, they were employed for mental stress assessment by using the SVM classifier, leading to an accuracy of 97.3 ± 0.9%, which was 13%, 30.8% and 1.8% greater than those of the fNIRS, RHR, and EHR signals, respectively. Conclusions: We showed not only that the EHR¯ signal is a brain-related response, but also that it contains useful information for mental stress level measurement. Moreover, it is concluded that the EHR signal includes information from both brain and heart, which is hence useful for applications in which brain and heart functions are altered. © 2019

关键词： Brain hemodynamic response Functional near infrared spectroscopy Heart rate Independent component analysis Stress assessment

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Anderson Acceleration of Proximal Gradient Methods

arXiv

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arXiv 2019年

作者： Mai, Vien V. Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

Anderson acceleration is a well-established and simple technique for speeding up fixed-point computations with countless applications. Previous studies of Anderson acceleration in optimization have only been able to provide convergence guarantees for unconstrained and smooth problems. This work introduces novel methods for adapting Anderson acceleration to (non-smooth and constrained) proximal gradient algorithms. Under some technical conditions, we extend the existing local convergence results of Anderson acceleration for smooth fixed-point mappings to the proposed scheme. We also prove analytically that it is not, in general, possible to guarantee global convergence of native Anderson acceleration. We therefore propose a simple scheme for stabilization that combines the global worst-case guarantees of proximal gradient methods with the local adaptation and practical speed-up of Anderson acceleration. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

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Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

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Bioresource Technology 2025年 434卷 132773页

作者： Zhai, Mudi Fu, Bomin Wu, Zhaozhong Wang, Junsen Wang, Weijie Wang, Hongtao College of Environmental Science and Engineering Key Laboratory of Yangtze River Water Environment Ministry of Education Tongji University Siping Rd 1239 Shanghai200092 China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong999077 China School of Computer Science Northwestern Polytechnical University Xi'an710129 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments. © 2025 Elsevier Ltd

关键词： Generative adversarial networks

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A Distributed and Parallel Method of Change Detection in Remote Sensing Image Based On Fully Connected Conditional Random Field

A Distributed and Parallel Method of Change Detection in Rem...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tiantian Zhou Zebin Wu Jun Liu Jin Sun Yi Zhang Jiandong Yang Hongyi Liu Zhihui Wei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Maritime Tracking and Control Department China Satellite Jiangyin China

Change Detection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal high resolution images. Change Detection based on fully connected conditional random field not only improves the detection accuracy of remote sensing image, but also achieves better robustness. However, with the growth of high-resolution data volumes, this algorithm consumes a huge amount of time and computational resources, and therefore needs to be improved accordingly. Spark is an open-source distributed general- purpose cluster-computing framework. It has powerful memory computing and efficient task scheduling capabilities for complex iterative calculations. Based on Spark, this paper proposes a distributed and parallel method of change detection in remote sensing image based on Fully Connected Conditional Random Field that analyzes the data input form, and proposes a multi-temporal image reading strategy on cloud platforms. This method decomposes the algorithm flow, and performs distributed parallel processing on each stage and makes full use of the processing advantages of data locality to implement a reasonable intermediate data storage. Experimental results demonstrate that this parallel method achieves a promising speedup with high scalability, while guaranteeing remarkable detection accuracy.

关键词： Image resolution Change detection algorithms Scalability Distributed databases Sparks Task analysis Remote sensing

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Identification and Processing of Outliers in Data Based on Improved Quantile Method

Identification and Processing of Outliers in Data Based on I...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Xiong Wei Nie Wanyuan Wu Zhenyu Luo Zhihong China Ship Development and Design Center Wuhan China School of Control and Computer engineering North China Electric Power University Beijing China

At present, a variety of sensors were installed on various production equipments, and a large amount of data was collected by sensors during operation. Then, it is inevitable that there are a large number of outliers in the measured values. In view of the above situation, this article has studied common methods in data outlier filtering. Pauta Criterion, Chauvenet's Criterion and Quantile Method were analyzed. The shortcomings of these methods and the defects in the application were summarized, and according to the actual problem, Quantile Method has been improved in a targeted manner. The advantages and effectiveness of the proposed method were proved by simulation experiments. The experimental results shown that the improved Quantile Method has higher recognition accuracy of outliers than the other two methods, and the new method is suitable for data with large fluctuations. Compared with the other two methods, it has higher practical application value. After the outliers in the data were identified, the average method was used for further correction. The method proposed in this paper has fast calculation speed and high accuracy, can adapt to the data with large fluctuations, and can accurately correct the outliers according to the fluctuation trend of the data. In summary, the method is suitable for real-time filtering of data during data collection.

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