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Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Upendra, Roshan Reddy Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Institute for Information Sciences University of Kansas LawrenceKS United States

ISBN: (纸本)9783031353017

Accurate cardiac motion estimation is a crucial step in assessing the kinematic and contractile properties of the cardiac chambers, thereby directly quantifying the regional cardiac function, which plays an important role in understanding myocardial diseases and planning their treatment. Since the cine cardiac magnetic resonance imaging (MRI) provides dynamic, high-resolution 3D images of the heart that depict cardiac motion throughout the cardiac cycle, cardiac motion can be estimated by finding the optical flow representation between the consecutive 3D volumes from a 4D cine cardiac MRI dataset, thereby formulating it as an image registration problem. Therefore, we propose a hybrid convolutional neural network (CNN) and Vision Transformer (ViT) architecture for deformable image registration of 3D cine cardiac MRI images for consistent cardiac motion estimation. We compare the image registration results of our proposed method with those of the VoxelMorph CNN model and conventional B-spline free form deformation (FFD) non-rigid image registration algorithm. We conduct all our experiments on the open-source Automated Cardiac Diagnosis Challenge (ACDC) dataset. Our experiments show that the deformable image registration results obtained using the proposed method outperform the CNN model and the traditional FFD image registration method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image registration

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Fish weight prediction using empirical and data-driven models in aquaculture systems

Fish weight prediction using empirical and data-driven model...

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Control technology and Applications (CCTA),

作者： Xiao Chen Ibrahima N’Doye Fahad Aljehani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Predicting fish growth trajectory is crucial in achieving better control for the growing phase of the fish in aquaculture systems. However, fish biomass sensors that directly measure the fish’s weight are currently unavailable. Additionally, the fish growth model that relies on the fish weight is affected by environmental and feeding management factors, raising the difficulty of predicting the weight. This paper proposes comparative empirical and data-driven models for fish weight prediction. The empirical fish growth model relies directly on the growth ratio, an essential component for optimizing fish growth, while the data-driven model is based on long short-term memory (LSTM). Since the exact daily food ratio, proportional to the body weight of a growing fish, cannot be prescribed, we optimize the growth ratio of the empirical model in a sliding window framework based on the previous growth periods and then predict the fish weights a few days ahead through a nonlinear exponential regression approach. The LSTM method uses a normal distribution to generate multiple datasets over the experimental fish weight range and interpolate these data to provide a good prediction. To this end, we propose a weighted sum optimization method that combines the individual cost of the LSTM and empirical methods to achieve better short-term prediction performance. The simulation results demonstrate the effectiveness of the proposed models in predicting the fish weight and show that the combined weighted sum approach reduces the testing errors by 61.3% and 70.9% compared to the empirical and LSTM methods, respectively. Furthermore, an increasing specific growth rate (SGR) for both LSTM and combined models was observed in the short-term predictions.

关键词： Weight measurement Costs Biological system modeling Simulation Predictive models Fish Trajectory

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Long short-Term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control 7

Long short-Term memory (LSTM) model-based reinforcement lear...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Wijaya, Santo Heryadi, Yaya Arifin, Yulyani Suparta, Wayan Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta55281 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： Model predictive control

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Enhancement of superconductivity in the Fibonacci chain

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Physical Review B 2024年第13期109卷 134504-134504页

作者： Meng Sun Tilen Čadež Igor Yurkevich Alexei Andreanov School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing 100124 China Center for Theoretical Physics of Complex Systems Institute for Basic Science Daejeon 34126 Republic of Korea School of Computer Science and Digital Technologies Aston University Birmingham B4 7ET United Kingdom Basic Science Program Korea University of Science and Technology Daejeon 34113 Republic of Korea

We study the interplay between quasiperiodic disorder and superconductivity in a one-dimensional tight-binding model with the quasiperiodic modulation of on-site energies that follow the Fibonacci rule, and all the eigenstates are multifractal. As a signature of multifractality, we observe the power-law dependence of the correlation between different single-particle eigenstates as a function of their energy difference. We numerically compute the mean-field superconducting transition temperature for every realization of a Fibonacci chain of a given size and find the distribution of critical temperatures, analyze their statistics, and estimate the mean value and variance of critical temperatures for various regimes of the attractive coupling strength and quasiperiodic disorder. We find an enhancement of the critical temperature compared to the analytical results that are based on strong assumptions of the absence of correlations and self-averaging of multiple characteristics of the system, which are not justified for the Fibonacci chain. For the very weak coupling regime, we observe a crossover where the self-averaging of the critical temperature breaks down completely and strong sample-to-sample fluctuations emerge.

关键词： Superconductivity Fractals Mean field theory Methods in superconductivity Tight-binding model

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Utilization of Decentralized Autonomous Organization (DAO) for Virtual Property Transaction Governance in Metaverse: A Conceptual Framework

Utilization of Decentralized Autonomous Organization (DAO) f...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Gede Indra Raditya Martha Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute Technology Bandung Bandung Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia

Research related to digital assets & ownership is currently rising along with the escalation utilization of Blockchain through various use cases and its derevatives, for example in facilitating Digital Assets in the form of virtual property (virtual houses / buildings and virtual land) in the Metaverse. These digital assets often have utilities, features, and even limited copies of quantities so that these digital objects have more value for specific users on a platform and even general people who are interested in and value them. To be able to preserve the value of digital assets in the Metaverse such as virtual property previously mentioned especially for transactions, a set of regulations/governance, as well as sustainable development in the future in the Metaverse is really needed so that the use of Decentralized Autonomous Organization (DAO) in the metaverse is one way to maintain the sustainability of the ecosystem & market and its potential of virtual property. After conducting research for DAO development for virtual property transactions, starting with determining goals & purposes, breaking down the DAO focus object (virtual property), clustering relevant attributes, matching socio-economic impacts, and finally designing smart contracts & governance consensus, we obtained 4 main layers namely Consensus Governance, Smart Contract, Virtual Property & Socio-Economic.

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An Improved Unbiased Particle Filter

arXiv

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arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. We assume that, for numerical reasons, one has to time-discretize the diffusion process which typically leads to filtering that is subject to discretization bias. The approach in [16] establishes that when only having access to the time discretized diffusion it is possible to remove the discretization bias with an estimator of finite variance. We improve on the method in [16] by introducing a modified estimator based on the recent work of [17]. We show that this new estimator is unbiased and has finite variance. Moreover, we conjecture and verify in numerical simulations that substantial gains are obtained. That is, for a given mean square error (MSE) and a particular class of multi-dimensional diffusion, the cost to achieve the said MSE falls. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Implementation of Lightweight PRNG on PLC for Industrial Control Systems

Implementation of Lightweight PRNG on PLC for Industrial Con...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Andi Sama Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The Information technology/Operational technology convergence towards Industry 4.0 opens the opportunity to leverage recent advancements in Information technology for Operational technology, such as Cloud, Internet of Things, and Artificial Intelligence. Meanwhile, cyber-attacks are increasing for Operational technology. The security aspect in Operational technology systems has traditionally been a low priority, in contrast with speed. This introduces challenges as Industrial Control Systems such as programmable Logic Controller in Operational technology have been traditionally optimized for speed rather than security due to limited computing power. As computations need to be as efficient as possible, security in Operational technology has yet to be managed as robustly as in Information technology. Operational technology communication protocols for securing data transfer, for example, have no or just a few security capabilities, even for basic authentication and encryption. Common security algorithms rely on random numbers. However, Random Number Generator is not usually part of standard functions in programmable Logic Controllers, the core control component in Industrial Control Systems. In Industrial Control Systems, the Random Number Generator is mostly implemented as a software-based Pseudo Random Number Generator. This paper shows how to apply a Pseudo Random Number Generator in a Siemens Compact PLC S7-1200 using a modified lightweight XORshift algorithm. The XORshift algorithm can generate better randomness than the system’s clock-based implementation in the Siemens Library of Generic Functions.

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Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments

Improving Out-of-Distribution Generalization in Graphs via H...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yinhua Piao Sangseon Lee Yijingxiu Lu Sun Kim Department of Computer Science and Engineering Seoul National University Institute of Computer Technology Seoul National University AIGENDRUG Co. Ltd. Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more seman-tically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical seman-tic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the in-put graph to generate proxy predictions on local environ-ments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environ-ments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Par-ticularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively.

关键词： Training computer vision Attention mechanisms Computational modeling Semantics Stochastic processes Pattern recognition

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Microprocessor Instruction Design Tool for RISC-V Architecture

Microprocessor Instruction Design Tool for RISC-V Architectu...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Luo Jiahui Tomoyuki Morimoto Tadahiro Ogita Ryota Kawamata Wang Ziming Toshiyuki Tsutsumi Computer Science Program Graduate School of Science and Technology Meiji University Kanagawa Japan VIAX Co.Ltd. Department of Computer Science School of Science and Technology Meiji University Kanagawa Japan TOYOTA MOTOR CORPORATION. SUS Co. Ltd.

We have been developing MEIMAT (MEiji Microprocessor Architecture Design Tools). The MEIMAT is essentially designed to be able to represent any instruction of various processors by the MEIMAT meta-instruction in two ways of semantic and functional expressions. In this paper, we have verified the RISC-V support to the MEIMAT, and have improved so that MEIMAT further supports RISC-V. First, we have verified whether the RISC-V instructions can be expressed by the MEIMAT semantic expression. It is also verified that the functional expression is automatically generated from the semantic instruction, and the corresponding correct circuit diagrams are generated in the MEIMAT instruction visualization tool. Then, we have newly developed a stage configuration mechanism to implement the RISC-V instructions. Because the RISC-V architecture is implemented by 4 or 5 processing stages in most actual processors. To develop further an easy-to-understand design tool, we have also introduced illustration windows for RISC-V specified hardware modules such as a register file, especially a status register in the instruction visualization tool. Finally, through these verifications and improvements, we have successfully confirmed that the 43 instructions in the RISCV (RV32I) instructions set of the RISC-V architecture can be converted into MEIMAT meta-instructions, and then they can be represented as circuit diagrams with the RISC-V stage configuration in the MEIMAT instruction visualization tool.

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5-Fold Cross Validation on Supporting K-Nearest Neighbour Accuration of Making Consimilar Symptoms Disease Classification 2

5-Fold Cross Validation on Supporting K-Nearest Neighbour Ac...

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2nd International Conference on computer science and Engineering, IC2SE 2021

作者： Satria, Andy Sitompul, Opim Salim Mawengkang, Herman Universitas Sumatera Utara Program of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Department of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Department of Mathematics and Natural Science Medan Indonesia

ISBN: (纸本)9781665400459

Classification is a technique in machine learning that is used to built the group of data. The data that consist of class and target are grouped based on the data attachment to the sample data, so that the data group can be used as a basis for making decisions at a later stage. This study aims to find a classification of two diseases, dengue fever and typhus which have similar symptoms using a combination of two techniques, 5-Fold Cross Validation and K-Nearest Neighbor. The combination of these techniques is done to improve the accuracy of the classification, feature selection is done in preprocessing phase to collect verified feature to get information gain to measure which feature is kept and continue to next stage of classification process. A confusion matrix will be applied to test the performance in terms of the accuracy of the constructed classification. K-Nearest Neighbor (KNN) is a classification method that applies a supervised algorithm, while 5-Fold Cross Validation is a statistical method that is applied to test the performance of a method or technique. This investigation resulted in an increase in the value of accuracy and recall, with a score of 1.79 points and a recall value of 96.43%. © 2021 IEEE.

关键词： Classification (of information)

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