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7th NAFOSTED Conference on information and computer science, NICS 2020

作者： Hoang-Xuan, Nhat Nguyen, E-Ro Pham-Le, Thuy-Dung Hoang-Nguyen, Khoi Advanced Program in Computer Science Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9780738105536

Video object segmentation is the problem of labelling the foreground object of interest that has widespread applications. We reevaluate One-shot Video Object Segmentation (OSVOS), a simple method that adapts VGG to image segmentation using a structure similar to a Fully Convolutional Network. We propose a range of improvements to make OSVOS competitive to newer methods while keeping its simplicity. Specifically, we replace VGG with EfficientNet, and adopt the U-net architecture. We also utilize Focal Loss and Dice Loss to handle the imbalanced binary classification, and finally we remove the boundary snapping module. With our amendments, we achieve 82.4% J&F on DAVIS 2016 validation set, an improvement over the original 80.2% of OSVOS. We also achieve much faster inference time per frame than OSVOS. © 2020 IEEE.

关键词： Image segmentation

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Front-end circuit design for electroencephalography (EEG) signal 2

Front-end circuit design for electroencephalography (EEG) si...

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Indo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020

作者： Salahuddin Morsalin, S.M. Lai, Shin-Chi Master's Program of Green Technology for Sustainability Nanhua University Chiayi County Taiwan Nanhua University Department of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781728149998

Electroencephalography (EEG) signals are recorded for knowing the electrical activity of the brain from the scalp, and the recorded waveform provides acquits into the dynamic aspects of brain activity. This study incorporates the circuit design and device development to achieve the Electroencephalography signal acquisition front-end circuit design for future Brain-computer Interface (BCI) applications. The amplitude of acquired signals should be strong enough and is usually expressed in the unit of millivolts. The data acquisition procedure consists three stages: 1) The acquisition of original EEG signal can be done by the active electrode and an instrumentation amplifier with a smaller gain;2) Improves the signal quality by using band-pass filter and band-stop filter;3) Those EEG signals convert into the digital code through the analog-to-digital converter (ADC) that will be integrated into a micro-controller. The digital code is stored into an embedded memory and is further transmitted via the Bluetooth module. The experimental results show that the system could implement the acquisition and storage of the EEG signals efficiently. The size of the printed circuit board (PCB) for the proposed design is smaller than 5.5 cm2. This system would be a benefit to all involved in the use of EEG for clinical diagnosis and monitoring, or even for the braincomputer interface. © 2020 IEEE.

关键词： Electrodes

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APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

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arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Sodium-ion battery cycle life prediction using machine learning

Sodium-ion battery cycle life prediction using machine learn...

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

作者： Linfeng Liu Filbert H. Juwono W. K. Wong Erick Purwanto Tingyan Jin Yuyang Zhen Sustainable Energy Technology 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 Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing People's Republic of China School of Instrument Science and Technology Beijing University of Technology Beijing People's Republic of China

ISBN: (数字)9781837242863

In this study, we explore the classification and prediction capabilities of three models—Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)—on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.

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An Intelligent System Based on Evolutionary Algorithm for Scheduling University Course Timetable

SSRN

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SSRN 2022年

作者： Bimantara, I. Made Satria Yuhana, Umi Laili Supriana, I. Wayan Pardede, Eric Department of Informatics Institut Teknologi Sepuluh Nopember 60117 Indonesia Informatics Study Program Faculty of Mathematics and Natural Sciences Udayana University Indonesia Department of Computer Science and Information Technology La Trobe University BundooraVIC3086 Australia

University Course Timetabling (UCT) is a common problem in educational institutions. The preparation of the class schedule must pay attention to available resources without violating set constraints. This research applies Evolutionary Algorithm such as Genetic Algorithm into timetabling system, to replace human expertise in generating optimal lecture schedules. Genetic Algorithm (GA) uses chromosomes to represent the solution of the UCT. One chromosome represents a set of schedule solutions that AG generates. Genes and alleles become components of chromosomes that represent the value of each entity to be optimized. We modeled chromosomes into four gene types, each of which has three allele types, namely day, hour, and room alleles which will be optimized with GA. The N-point and cycle crossovers were implemented into the new chromosome encoding scheme and do not take place at the gene level, but at the allele level by grouping similar allele types before the crossover process is carried out. The success rate is measured by the percentage of the optimal number of schedules generated from the trials. The proposed method provides a success rate of 80 - 85% and can produce an optimal schedule with an average computation time of 8 minutes. GA can reduce human resources and save time in the process of preparing lecture schedules. © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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Machine learning-based global air quality index development using remote sensing and ground-based stations

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Environmental Advances 2024年 15卷

作者： Anggraini, Tania Septi Irie, Hitoshi Sakti, Anjar Dimara Wikantika, Ketut Center for Environmental Remote Sensing Chiba University 1-33 Yayoicho Inage-Ku Chiba 263-8522 Japan Doctoral Program in Geodesy and Geomatics Engineering Faculty of Earth Science and Technology Institut Teknologi Bandung Bandung 40132 Indonesia Center for Remote Sensing Institut Teknologi Bandung Bandung 40132 Indonesia Remote Sensing and Geographic Information Sciences Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung 40132 Indonesia

Air pollution refers to the presence of hazardous substances in the air that has adverse effects on health, causing millions premature deaths annually. Ground-based stations can provide accurate measurements for monitoring air pollution. However, the spatial coverage of air pollution measurements is limited by the number of measurement instruments available in specific hotspot areas. Satellite remote sensing can reduce spatial uncertainty;however, the measurement results are mostly limited to the upper atmosphere with high measurement sensitivity. To better represent surface conditions, this study aims to model the Air Quality Index for pollutants CO, NO₂, SO₂, PM_2.5, and PM₁₀ in the global region using remotely sensed data. To support this study, 425 data points from air pollution stations distributed globally are combined using Machine Learning Linear Regression methods. Furthermore, socioeconomic and environmental data are combined with air pollution satellite to form Multiple models. According to the results of this study, the Multiple Models are more accurate than the single models, showing that the addition of socioeconomic and environmental data can enhance accuracy. The results of this study are expected to help regions without air pollution monitoring stations to estimate the air quality index using satellite data, in turn, preventing air pollution disasters. © 2023 The Author(s)

关键词： Air pollution Air quality index Machine learning linear regression Monitoring stations Satellite

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Export- and import-based economic models for predicting global trade using deep learning

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Expert Systems with Applications 2023年 218卷

作者： Yang, Cheng-Hong Lee, Cheng-Feng Chang, Po-Yin Department of Information Management Tainan University of Technology Tainan City710302 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung80708 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung80708 Taiwan Department of Business Administration National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen County 892 Taiwan

Forecasting global foreign trade is essential for developing government trade policies and management strategies for multinational corporations. However, achieving an accurate trade forecast is challenging because of the complex structural relationships between exports, imports and other economic variables. Many traditional forecasting models, such as time series, econometric, and machine learning, provide less accurate forecasts for trade data. This paper proposes an ensemble learning approach to improve forecasting performance by hybridizing the structural relationships between trade and deep learning models to predict foreign trade for ten major countries. The proposed method first establishes a cointegration relationship between exports and imports and their structural variables. The cointegrated models are then used to predict the future of trade, which is used as a benchmark model for comparison. A hybrid deep learning algorithm uses the cointegrated variables as input variables to predict trade data, and then are compared with time-series forecasts and economic structural models. The experimental results reveal that the ensemble learning method can achieve excellent forecasting performance for the tested periods of trade data. In most cases, the root means square error and mean absolute percentage error values are smaller than the time series and economic structural models. © 2023 Elsevier Ltd

关键词： Forecasting

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A Reliable Method to Estimate the Bispectral Index Value Using a Single Frontal EEG Channel for Intra and Inter Subject Variability

A Reliable Method to Estimate the Bispectral Index Value Usi...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Mohammad Shahbakhti Matin Beiramvand Roza Krycinska Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Doctoral School in Medical and Health Sciences Jagiellonian University Medical College Krakow Poland Department of Interdisciplinary Intensive Care Jagiellonian University Collegium Medicum Krakow Poland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario.

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Cryo-EM structure of the plant 26S proteasome

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Plant Communications 2022年第3期3卷 116-126页

作者： Susanne Kandolf Irina Grishkovskaya Katarina Belacic Derek L.Bolhuis Sascha Amann Brent Foster Richard Imre Karl Mechtler Alexander Schleiffer Hemant D.Tagare Ellen D.Zhong Anton Meinhart Nicholas G.Brown David Haselbach Research Institute of Molecular Pathology(IMP) Vienna BioCenter(VBC)Campus-Vienna-BioCenter 11030 ViennaAustria Department of Radiology and Biomedical Imaging Yale UniversityNew HavenCT 06510USA Department of Pharmacology and Lineberger Comprehensive Cancer Center University of North Carolina School of MedicineChapel HillNC 27599USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA Vienna BioCenter PhD Program Doctoral School of the University at Vienna and Medical University of ViennaVienna BioCenter(VBC)ViennaAustria Institute of Physical Chemistry University of FreiburgAlbertstraße 21Freiburg 79104Germany CIBSS Centre for Integrative Biological Signalling Studies University of FreiburgFreiburgGermany

Targeted proteolysis is a hallmark of *** is especially important in long-lived cells that can be found in higher eukaryotes,like *** task is mainly fulfilled by the ubiquitin–proteasome ***,proteolysis by the 26S proteasome is vital to development,immunity,and cell *** the yeast and animal proteasomes are well characterized,there is only limited information on the plant *** determined the first plant 26S proteasome structure from Spinacia oleracea by single-particle electron cryogenic microscopy at an overall resolution of 3.3 A°.We found an almost identical overall architecture of the spinach proteasome compared with the known structures from mammals and ***,we noticed a structural difference in the proteolytic active b1 ***,we uncovered an unseen compression state by characterizing the proteasome’s conformational *** suspect that this new conformation of the 20S core protease,in correlation with a partial opening of the unoccupied gate,may contribute to peptide release after *** data provide a structural basis for the plant proteasome,which is crucial for further studies.

关键词： 26S proteasome spinach UPS cryo-EM conformational landscape

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