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3rd Asian Conference on Artificial Intelligence technology, ACAIT 2019

作者： Chen, Qiang Chen, Yinong Zhu, Jinhui De Luca, Gennaro Zhang, Mei Guo, Ying College of Computer and Information Science Southwest University Chongqing China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States School of Software Engineering South China University of Technology Guangzhou China School of Automation Science and Control Engineering South China University of Technology Guangzhou China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

Guide dogs are helpful for visually impaired people for navigating through the streets. However, it is expensive and time consuming to train a guide dog. In addition, a guide dog cannot decide when and where to cross a street safely, and it is up to the human to decide. Here, the authors propose a framework for creating a guide dog robot by using artificial intelligence and other technologies. The proposed framework is based on an Intel UP squared board, together with a Neural Compute Stick Movidius to process the images gathered from a GoPro camera. MobileNet single shot detector (SSD) is the main framework to detect the moving objects in the environment. The final decision is made after fusing the information gathered from all the sources. The authors also apply the Amazon Alexa device for the voice communication between the guide dog robot and the visually impaired person. A prototype of the proposed system is implemented and tested. Experimental results show that the proposed framework can process the information at a traffic intersection scene and can guide a blind person to cross the street safely. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Object detection

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HUNTER: AI based holistic resource management for sustainable cloud computing

arXiv

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

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Enhancing Bioactivity and Corrosion Resistance of Laser Powder Bed-Fused Ti6al4v Using Titania-Reinforced Hydroxyapatite Nanocomposite Coatings Through Electrophoretic Deposition

SSRN

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SSRN 2024年

作者： Sohrabpoor, Hamed Salarvand, V. Torabpour, M. Yazdi, M. Saghafi Asadi, P. Brabazon, Dermot Stein, B.D. Raghavendra, R. Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States Department of Materials Science and Engineering Imam Khomeini International University Qazvin Iran Department of Mechanical Engineering Engineering Faculty Gebze Technical University Kocaeli Gebze41400 Turkey I-Form Advanced Manufacturing Research Centre & Advanced Processing Technology Research Centre School of Mechanical & Manufacturing Engineering Dublin City University Dublin Ireland Electron Microscopy Center Department of Biology Indiana University BloomingtonIN United States SEAM Research Center South East Technological University Ireland

Hydroxyapatite (HA) coatings are commonly used on titanium alloy substrates for orthopedic and dental implants because of their excellent osteoconductivity and biocompatibility. The disparity in the coefficient of thermal expansion (CTE) between the coating and substrate can result in the formation of cracks in the HA coatings during the coating process. This study examined the effect of titania (TiO2) nanoparticle additions in HA coatings to enhance their properties and prevent cracking. Using electrophoretic deposition (EPD), HA and HA-TiO2 composite coatings were applied to laser powder bed fused Ti6Al4V substrates. Optimal EPD conditions of 45 volts and 120 s were identified to produce consistent, crack-free coatings. Increasing the TiO2 nanoparticle concentration (≥50 wt.%) decreased the coatings' CTE, mitigating cooling-induced cracking. The microstructural analysis confirmed the uniform distribution of HA and TiO2 in the coatings. X-ray diffraction and Fourier-transform infrared spectroscopy data revealed that the HA structure remained intact even with the addition of TiO2. Higher TiO2 reduced the average surface roughness, with the HA-40%TiO2 coating exhibiting the lowest roughness (approximately 50 nm). Electrochemical tests corroborated the surface roughness findings. Incorporating TiO2 into the HA coatings decreased the thermal expansion coefficient, reducing tensile stresses. © 2024, The Authors. All rights reserved.

关键词： Titanium dioxide

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity

arXiv

引用

arXiv 2022年

作者： Huang, Dong Wang, Chang-Dong Lai, Jian-Huang The College of Mathematics and Informatics South China Agricultural University Guangzhou China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. With multiple views extended to many view groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available: https://***/huangdonghere/FastMICE. © 2022, CC BY.

关键词： Scalability

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Construction of Hierarchical Matrix on Distributed Memory Systems using a Task Parallel Language

Construction of Hierarchical Matrix on Distributed Memory Sy...

引用

International Symposium on computing and Networking Workshops (CANDARW)

作者： Zhengyang Bai Tasuku Hiraishi Akihiro Ida Masahiro Yasugi Keiichiro Fukazawa Gradudate School of Informatics Kyoto University Kyoto Japan Department of Information and Computer Science Kyoto Tachibana University Kyoto Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Yokohama Japan Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

A hierarchical matrix is an approximated form of the dense matrix that represents the N-by-N correlations of N objects. The construction of the hierarchical matrix is achieved by dividing a matrix into submatrices (partitioning), followed by calculating these submatrices' entries (filling). The matrix partitioning is divided into two sub-steps: cluster tree (CT) construction by dividing objects into clusters, hierarchically, and block cluster tree (BCT) construction by observing all cluster pairs at the same level of the cluster tree that satisfy a given admissibility condition. We can apply task parallelism to filling by treating each submatrix as a parallelization unit. In existing implementations, the partitioning is redundantly executed on every computing node, and filling is executed in parallel by assigning a set of tasks to each worker statically. However, we cannot get good load balance using such implementations because it is difficult to predict the workload of each task precisely. In this paper, we propose a new parallel implementation of the hierarchical matrix construction, where a BCT construction is executed in parallel using all workers of all computing nodes. When a worker finds a BCT leaf during BCT construction, it executes filling for the submatrix corresponding to the leaf. To get good load balance, we parallelized the BCT construction using the task parallel language Tascell, which enables easy and efficient parallelization of tree recursive algorithms by employing a dynamic load balancing strategy. In numerical experiments with 3D electric field analyses using up to 16 computing nodes each of which has 36 cores, our implementation achieved up to 1.9-fold speedups compared to an implementation using the static task assignment strategy.

关键词： Parallel languages Three-dimensional displays Correlation Heuristic algorithms Conferences Parallel processing Load management

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QoS-aware and multi-objective virtual machine dynamic scheduling for big data centers in clouds

Research Square

引用

Research Square 2021年

作者： Li, Jirui Zhang, Rui Zheng, Yafeng School of Information Technology Henan University of Chinese Medicine Zhengzhou450008 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States School of Computer and Information Engineering Henan University of Economics and Law Zhengzhou450008 China

Efficient resource scheduling is one of the most critical issues for big datacenters in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models. © 2021, CC BY.

关键词： Big data

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How to Improve Information technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health informatics Experts

引用

Yearbook of Medical informatics 2021年第1期30卷 61-68页

作者： Topaz, Max Peltonen, Laura-Maria Mitchell, James Alhuwail, Dari Barakati, Seyedeh-Samin Lewis, Adrienne Moen, Hans Veeranki, Sai Pavan Kumar Block, Lori Risling, Tracie Ronquillo, Charlene School of Nursing Columbia University NY United States Department of Nursing Science University of Turku Finland School of Computing and Mathematics Keele University United Kingdom Information Science Department Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Daphne Cockwell School of Nursing Ryerson University Toronto Canada Splatsin Health Services Enderby Canada Department of Future Technologies University of Turku Finland Health & Bioresources/Molecular Diagnostics AIT Austrian Institute of Technology GmbH Graz Austria Technical University of Graz Graz Austria School of Nursing University of British Columbia Vancouver Canada College of Nursing University of Saskatchewan Saskatoon Canada

OBJECTIVES: To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic. METHODS: The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey. A non-probability convenience sampling strategy was employed. Data were analyzed with content analysis. RESULTS: A total of 65 participants from 16 countries responded to the conducted survey. The four major themes regarding recommended improvements identified from the content analysis included: improved technology availability, improved interoperability, intuitive user interfaces and adoption of standards of care. Respondents also identified several key healthcare information and communication technologies that can help to provide better healthcare to patients during the COVID-19 pandemic, including telehealth, advanced software, electronic health records, remote work technologies (e.g., remote desktop computer access), and clinical decision support tools. CONCLUSIONS: Our results help to identify several important healthcare information and communication technologies, recommended by health informatics experts, which can help to provide better care to patients during the COVID-19 pandemic. The results also highlight the need for improved interoperability, intuitive user interfaces and advocating the adoption of standards of care. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://

关键词： COVID-19 Health informatics international survey telehealth

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Routh table test for stability of commensurate fractional degree polynomials and their commensurate fractional order systems

引用

Control Theory and technology 2019年第3期17卷 297-306页

作者： Sheng-Guo WANG Shu LIANG Liang MA Kaixiang PENG College of Engineering and College of Computing and Informatics University of North Carolina at Charlotte NC 28223-0001 U.S.A. School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

A Routh table test for stability of commensurate fractional degree polynomials and their commensurate fractional order systems is presented via an auxiliary integer degree polynomial. The presented Routh test is a classical Routh table test on the auxiliary integer degree polynomial derived from and for the commensurate fractional degree polynomial. The theoretical proof of this proposed approach is provided by utilizing Argument principle and Cauchy index. Illustrative examples show efficiency of the presented approach for stability test of commensurate fractional degree polynomials and commensurate fractional order systems. So far, only one Routh-type test approach [1] is available for the commensurate fractional degree polynomials in the literature. Thus, this classical Routh-type test approach and the one in [1] both can be applied to stability analysis and design for the fractional order systems, while the one presented in this paper is easy for peoples, who are familiar with the classical Routh table test, to use.

关键词： Fractional order systems stability commensurate fractional degree polynomials Routh table test

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Effect of COVID-19 on photovoltaic demand and installation

Effect of COVID-19 on photovoltaic demand and installation

引用

International Conference on Green Energy, computing and Intelligent technology (GEn-CITy 2023)

作者： F. Zulfiqar F. Muhammad-Sukki S. H. Abu-Bakar N. A. Bani A. A. Mas'ud School of Computing Engineering & the Built Environment Edinburgh Napier University Edinburgh EH10 5DT UK Renewable Energy Research Laboratory Electrical Engineering Section British Malaysian Institute Universiti Kuala Lumpur Gombak 53100 Malaysia Razak Faculty of Technology & Informatics Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia Department of Electrical Engineering Jubail Industrial College Al Jubail 35718 Saudi Arabia

The research investigates the effects of the COVID-19 pandemic on the photovoltaic (PV) sector. This research examines the alterations in PV demand and installation amidst the COVID-19 pandemic and identifies the underlying factors that have influenced these changes. The article delves into the difficulties encountered by PV installers amidst the ongoing pandemic. This study delves deeper into the effects of government policies and investor circumstances on the PV industry amidst the pandemic. It examines the policies that have influenced the supply chain, demand, financing, and shift towards renewable energy. The results suggest that the outbreak of the pandemic has resulted in noteworthy alterations in the demand and installation of PV systems, which have had both favourable and unfavourable effects on the sector. The response of the industry to the pandemic has been significantly influenced by government policies and investor conditions. The investigation culminates by proposing potential avenues for further inquiry within this domain.

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