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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Mohd Daud, K. Zakaria, Z. Hassan, R. Mohamad, M.S. Ali Shah, Z. Artificial Intelligence and Bioinformatics Research Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Software Engineering Department School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kota Bharu Kelantan16100 Malaysia Faculty of Bioengineering and Technology Universiti Malaysia Kelantan Jeli Campus Lock Bag 100 Jeli Kelantan17600 Malaysia

Metaheuristic algorithms have been used in various domains to solve the optimization problem. In metabolic engineering, the problem of identifying near-optimal reactions knockout that can optimize the production rate of desired metabolites are hindered by the complexity of the metabolic networks. Through Flux Balance Analysis, different metaheuristics algorithms have been improved to optimize the desired phenotypes. In this paper, a comparative study of four metaheuristic algorithms have been proposed. Differential Search Algorithm (DSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Genetic Algorithm (GA) are considered. These algorithms are tested on succinic acid production in Escherichia coli. The comparative performances are measured based on production rate, growth rate, and computational time. Hence, from the results, the best metaheuristic algorithms to solve the metabolic network optimization is identified. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Metabolic engineering

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Practical Applications of Computational Biology and Bioinformatics, 13th International Conference 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Florentino Fdez-Riverola Miguel Rocha Mohd Saberi Mohamad Nazar Zaki José A. Castellanos-Garzón

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Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning

arXiv

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arXiv 2024年

作者： Jung, Sunyoung Choi, Yoonseok Al-Masni, Mohammed A. Jung, Minyoung Kim, Dong-Hyun Department of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science College of Software and Convergence Technology Sejong University Seoul Korea Republic of Cognitive Science Research Group Korea Brain Research Institute Daegu Korea Republic of

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motion-corrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans. The code is available at https://***/SunYJhxppy/Multi-Net. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Semantic interoperability for asset communication within smart factories 22

Semantic interoperability for asset communication within sma...

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22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017

作者： Diedrich, Christian Belyaev, Alexander Schröder, Tizian Willmann, Alexander Koziolek, Heiko Pethig, Florian Niggemann, Oliver Vialkowitsch, Jens Usländer, Thomas Wende, Jörg Institute for Automation Engineering Otto-von-Guericke-Universität Magdeburg39106 Germany Industrial Internet of Things Group Fraunhofer FOKUS Berlin10589 Germany Corporate Research Center ABB Ladenburg68526 Germany Intelligent Automation Lab Big Data Lemgo32657 Germany Robert Bosch GmbH Stuttgart70442 Germany Information Management and DCS Fraunhofer IOSB Karlsruhe76131 Germany IBM Hybrid Cloud Software IBM Deutschland GmbH Dresden01067 Germany

ISBN: (纸本)9781509065059

Industrie 4.0 (I4.0) aims at a manufacturer-independent, vertical- And horizontal-oriented communication and cooperation within smart factories. This is only manageable using international standards. The so-called reference architecture model for Industrie 4.0 (RAMI4.0) and the requirement specification of an I4.0 component are already available. RAMI4.0 could be the basis for the interoperability of the interactions. The working group "Semantic and interaction model for I4.0 components" (GMA 7.20) made progress in this direction. The language used for the interaction of I4.0 components needs model definitions for the interaction consisting of the structure of the components, syntax for the description of the messages and the means to assign the meaning to the language elements. This paper discusses the results for a broader audience. © 2017 IEEE.

关键词： Semantics

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Entrans: Leveraging kinetic energy harvesting signal for transportation mode detection

arXiv

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arXiv 2018年

作者： Lany, Guohao Xu, Weitao Ma, Dong Khalifa, Sara Hassan, Mahbub Hu, Wen Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Engineering University of New South Wales Sydney Australia Distributed Sensing Systems research group Data61 CSIRO Australia

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Accelerometers

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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τ-FPL: Tolerance-constrained learning in linear time

arXiv

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arXiv 2018年

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research - China Shanghai China College of Computing Georgia Institute of Technology AtlantaGA United States

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, τ -False Positive Learning (τ - FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ -FPL over existing approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Mathematical transformations

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C2MS: Dynamic Monitoring and Management of Cloud Infrastructures

C2MS: Dynamic Monitoring and Management of Cloud Infrastruct...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Gary A. McGilvary Josep Rius Íñigo Goiri Francesc Solsona Adam Barker Malcolm Atkinson Edinburgh Data-Intensive Research Group The University of Edinburgh ICG Software Industrial Polygon Torrefarrera Spain Dept. of Computer Science Rutgers University Dept. of Computer Science and Industrial Engineering Univrsity of Lleida School of Computer Science University of St Andrews

Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration, an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS), a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.

关键词： Servers Monitoring Temperature measurement Clouds Temperature sensors Educational institutions

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SMOG: Accelerating Subgraph Matching on GPUs

SMOG: Accelerating Subgraph Matching on GPUs

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

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Mars 2020 surface mission performance analysis: Part 2. surface traversability

Mars 2020 surface mission performance analysis: Part 2. surf...

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AIAA Space and Astronautics Forum and Exposition, 2018

作者： Ono, Masahiro Heverly, Matthew Rothrock, Brandon Ishimatsu, Takuto Almeida, Eduardo Calef, Fred Soliman, Tariq Williams, Nathan Gengl, Hallie Nicholas, Austin Stilley, Erisa K. Otsu, Kyohei Trautman, Marshall Lange, Robert Milkovich, Sarah Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Research Technologist Mobility and Robotics Systems Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Mechanical Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Science Data Understanding Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Geophysics and Planetary Geosciences Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Data Visualization Developer Instrument Software and Science Data Systems PasadenaCA91106 United States Software Systems Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Mission Systems Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Science Systems Engineer Jet Propulsion Laboratory California Institute of Technology 394B PasadenaCA91106 United States

ISBN: (纸本)9781624105753

– The Mars 2020 Rover Mission (M2020) is characterized by demanding requirement on the distance and time for traveling between scientific Regions Of Interest (ROIs). As a result, surface traversability is one of the major driving factors for the landing site selection of M2020. With the newly developed Mars Terrain Traversability analysis Tools (MTTT), we performed traversability analysis of the eight candidate landing sites with an unprecedented granularity. This paper describes the MTTT analysis capabilityies, as well as how the MTTT capabilities were used to downselect from eight to three candidate landing sites for further evaluation. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Site selection

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