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16th International Conference on Autonomous Agents and Multiagent systems, AAMAS 2017

作者： Teng, Teck-Hou Chuin, Hoong Kumar, Akshat Urban Computing and Engineering Corporate Laboratory 71 Stamford Road 178895 Singapore Lau School of Information Systems 80 Stamford Road 178902 Singapore

ISBN: (纸本)9781510855076

Global increase in trade leads to congestion of maritime traffic at the ports. This often leads to increased maritime incidents or near-miss situations. To improve maritime safety while maintaining efficiency, movement of vessels needs to be better coordinated. Our work formulates this problem of coordinating the paths of vessels as a multi-Agent path-finding (MAPF) problem. To address this problem, we introduce an innovative application of MAPF in the maritime domain known as Vessel Coordination Module (VCM). Based on the local search paradigm, VCM plans on a joint state space updated using the Electronic Navigation Charts (ENC) and the paths of vessels. We introduce the notion of path quality that measures the number of positions on a vessel path that is too close to some other vessels spatially and temporally. VCM aims to improve the overall path quality of vessels by improving path quality of selected vessels. Experiments are conducted on the Singapore Straits to evaluate and coin- pare performance of our proposed approach in heterogeneous maritime scenario. Our experiment results show that VCM can improve the overall path quality of the vessels. © Copyright 2017, International Foundation for Autonomous Agents and Multiagent systems (***). All Rights Reserved.

关键词： Efficiency

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An Integrated Approach for Fault Detection in Industrial Processes Using Approximate Entropy and Slow Feature Analysis 43

An Integrated Approach for Fault Detection in Industrial Pro...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Chuangyan Li, Peng Wu, Jiande Lang, Xun Ai, Mingxi Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9789887581581

Multivariate statistical process monitoring has been widely used in industry process. However, traditional algorithms often ignore the complexity dynamic features of actual industry process. This study proposes a novel algorithm based on Approximate Entropy and Slow Feature Analysis (AE-SFA), which has completed the full-condition monitoring of a dynamic complexity system and quantitative divided static components and dynamic components. The algorithm achieves the optimal detection rate under T2 and SPE control limits. To enrich the experiment, we used the Tennessee Eastman process dataset to demonstrate the superiority of our algorithm. From the comprehensive score of the optimal detection rate, the accuracy of the proposed method is improved by 3.24% and 10.50% of T2 and SPE statistics, respectively. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Statistical process control

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An improved variational mode decomposition with adaptive initial center frequency for bearing fault feature extraction

An improved variational mode decomposition with adaptive ini...

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2024 China Automation Congress, CAC 2024

作者： Wang, Ying Lang, Xun Wu, Jiande Li, Yuzhen Li, Peng Zhang, Yufeng Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is significantly influenced by the initial center frequency. An improper setting of can significantly reduce the stability of decomposition, resulting in the loss or masking of feature information by noise. To address the above issue, an improved VMD algorithm is proposed in this work. Our algorithm redefines based on the narrowband characteristics of intrinsic mode function (IMF). Under the new definition, can be adaptively adjusted based on the signal spectrum processed by Wiener filtering. This adjustment significantly enhances the flexibility and anti-mixing performance of the original VMD. To validate our proposed method, comparative experiments were conducted using two sets of fault data from Case Western Reserve University (CWRU). The results demonstrate that our method is more effective and superior in extracting fault features. © 2024 IEEE.

关键词： Variational mode decomposition

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DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum cloud computing

DRLQ: A Deep Reinforcement Learning-based Task Placement for...

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IEEE International Conference on cloud computing, cloud

作者： Hoa T. Nguyen Muhammad Usman Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Parkville Victoria Australia School of Physics The University of Melbourne Parkville Victoria Australia Data61 CSIRO Clayton Victoria Australia

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a novel Deep Reinforcement Learning (DRL)- based technique for task placement in quantum cloud computing environments, addressing the optimization of task completion time and quantum task scheduling efficiency. It leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. This approach is one of the first in the field of quantum cloud resource management, enabling adaptive learning and decision-making for quantum cloud environments and effectively optimizing task placement based on changing conditions and resource availability. We conduct extensive experiments using the QSimPy simulation toolkit to evaluate the performance of our method, demonstrating substantial improvements in task execution efficiency and a reduction in the need to reschedule quantum tasks. Our results show that utilizing the DRLQ approach for task placement can significantly reduce total quantum task completion time by 37.81 % to 72.93% and prevent task rescheduling attempts compared to other heuristic approaches.

关键词： cloud computing Quantum computing Processor scheduling Decision making Computer architecture Dynamic scheduling Deep reinforcement learning

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Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction

Comparative Analysis of Machine Learning Models for Diabetes...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Materwala, Huned United Arab Emirates University College of Information Technology Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu-Dhabi Al-Ain15551 United Arab Emirates

ISBN: (纸本)9781728176246

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper. © 2020 IEEE.

关键词： Medical informatics

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A Machine Learning-Based Elastic Strategy for Operator Parallelism in a Big Data Stream computing System 1

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12th International Conference on Broadband Communications, Networks, and systems, BROADNETS 2021

作者： Li, Wei Sun, Dawei Gao, Shang Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University MelbourneVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

Elastic scaling in/out of operator parallelism degree is needed for processing real time dynamic data streams under low latency and high stability requirements. Usually the operator parallelism degree is set when a streaming application is submitted to a stream computing system and kept intact during runtime. This may substantially affect the performance of the system due to the fluctuation of input streams and availability of system resources. To address the problems brought by the static parallelism setting, we propose and implement a machine learning based elastic strategy for operator parallelism (named Me-Stream) in big data stream computing systems. The architecture of Me-Stream and its key models are introduced, including parallel bottleneck identification, parameter plan generation, parameter migration and conversion, and instances scheduling. Metrics of execution latency and process latency of the proposed scheduling strategy are evaluated on the widely used big data stream computing system Apache Storm. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

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iQuantum: A Case for Modeling and Simulation of Quantum computing Environments

iQuantum: A Case for Modeling and Simulation of Quantum Comp...

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Quantum Software (QSW), IEEE International Conference on

Today’s quantum computers are primarily accessible through the cloud and are expected to be deployed in edge networks in the near future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing. However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems. To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model quantum computing environments for prototyping and evaluating system design and scheduling algorithms. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum. Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.

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A Topology-Aware Scheduling Strategy for distributed Stream computing System 1

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12th International Conference on Broadband Communications, Networks, and systems, BROADNETS 2021

作者： Li, Bo Sun, Dawei Chau, Vinh Loi Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University GeelongVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

Reducing latency has become the focus of task scheduling research in distributed big data stream computing systems. Currently, most task schedulers in big data stream computing systems mainly focus on tasks assignment and implicitly ignore task topology which can have significant impact on the latency and energy efficiency. This paper proposes a topology-aware scheduling strategy to reduce the processing latency of stream processing systems. We construct the data stream graph as a directed acyclic graph and then, divide it using the graph Laplace algorithm. On the divided graph, tasks will be assigned with a low-latency scheduling strategy. We also provide a computing node selection strategy, which enables the system to run tasks on the topology with the least number of computing nodes. Based on this scheduling strategy, the tasks of the data stream graph can be redistributed and the scheduling mechanism can be optimized to minimize the system latency. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Big data

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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A Data Stream Prediction Strategy for Elastic Stream computing systems 1

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12th International Conference on Broadband Communications, Networks, and systems, BROADNETS 2021

作者： Zhang, Hanchu Sun, Dawei Sajjanhar, Atul Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University GeelongVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

In a distributed stream processing system, elastic resource provisioning/scheduling is the main factor that affects system performance and limits system applications. However, in the data stream computing platform, resource allocation is often suboptimal due to the large fluctuations of the data stream rate, which creates a performance bottleneck for the cluster. In this paper, we propose a data stream prediction strategy (Dp-Stream) for elastic computing system to mitigate the resource allocation issue. First, we establish a back propagation (BP) neural network prediction model based on genetic simulated annealing algorithm to predict the trend of the data stream rate in the next time window of the cluster;second, according to the time latency, the estimation model adjusts the resources allocated to the critical operations of the critical path in the Directed Acyclic Graph (DAG) and finally, the resource communication cost is optimized. We evaluate the prediction accuracy and system latency of the proposed scheduling strategy in Storm. The experimental results prove the feasibility and effectiveness of the proposed strategy. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Storms

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