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2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social computing, SocialCom 2011

作者： Wang, Xun Williams, Mary-Anne Innovation and Enterprise Research Laboratory Centre for Quantum Computing and Intelligent Systems University of Technology Sydney Australia

ISBN: (纸本)9780769545783

Risk is an important and ubiquitous concept that plays a crucial role in decision makings across domains. Risk is also a vague notion that carries different meaning under different domain context and perspectives. This paper aims to provide a formal generalised definition of risk based on the possible world paradigm and expected utility theory and the meanings of risk from both qualitative and quantitative level. This definition of risk is developed from the perspective an intelligent agent or information system. It provides a solid theoretical foundation upon which we can construct an intelligent generalised risk modelling and management framework using techniques from Artificial Intelligence research. This framework can be implemented as an integral part of an information system for better decision support and management of businesses. © 2011 IEEE.

关键词： Risk assessment

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Predicting cement compressive strength using double-layer multi-expression programming

Predicting cement compressive strength using double-layer mu...

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4th International Conference on Computational and Information Sciences, ICCIS 2012

作者： Zhang, Qingke Yang, Bo Wang, Lin Zhu, Fuxiang Shandong Province Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China Computer Networks and Distributed Systems Laboratory Beijing Institute Technology Beijing China

ISBN: (纸本)9780769547893

This paper presents a novel algorithm named Double-layer Multi-expression Programming (DMEP). Then DMEP model is applied to the prediction of 28-day Portland cement compressive strength. We compare DMEP model with other four soft computing models, namely Multi-Expression Programming model (MEP), Gene Expression Programming model (GEP), Neural Networks model (NN) and Fuzzy logic model (FL) on 28-day cement strength prediction. The experiment results show that DMEP model has a lower rate in RMSE and MAE, and the prediction average percentage error of the cement strength data using DMEP model is reduced to 2.18%, which is lower than other four models. The results obtained from the computational tests demonstrate that DMEP is a promising technique for the prediction of cement strength. © 2012 IEEE.

关键词： Soft computing

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Measuring efficiency in opportunistic ad hoc networks

Measuring efficiency in opportunistic ad hoc networks

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作者： Smith, Anthony Hill, Richard Distributed and Intelligent Systems Research Group School of Computing and Mathematics University of Derby Derby DE22 1GB United Kingdom

Opportunistic Networks are examples of wireless, ad hoc networks where there is an absence of a continuous end-to-end path. The proliferation of mobile device usage creates opportunities for nodes to forward packets in a dynamic way, utilizing nodes as they present themselves. In more conventional, static network infrastructures, it is typical to measure efficiency of message passing between nodes. We review approaches to the measurement of efficiency in networks, and propose a qualitative and quantitative metrics framework and simulation model that would be suitable for the evaluation of performance in opportunistic networks. © 2011 World Scientific Publishing Company.

关键词： Efficiency

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The Combination of Two Control Strategies for Series Hybrid Electric Vehicles

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 596-608页

作者： Can Luo Zhen Shen Simos Evangelou Gang Xiong Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Cloud Computing Center Chinese Academy of Sciences IEEE Department of Electrical and Electronic Engineering Imperial College London Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries

With most countries paying attention to the environment protection, hybrid electric vehicles have become a focus of automobile research and development due to the characteristics of energy saving and low emission. Power follower control strategy(PFCS) and DC-link voltage control strategy are two sorts of control strategies for series hybrid electric vehicles(HEVs). Combining those two control strategies is a new idea for control strategy of series hybrid electric vehicles. By tuning essential parameters which are the defined constants under DClink voltage control and under PFCS, the points of minimum mass of equivalent fuel consumption(EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure(WLTP). The fuel economy of series HEVs with the combination control schemes performs better compared with individual control scheme. The results show the effects of the combination control schemes for series HEVs driving in an urban environment.

关键词： DC-Link voltage control power follower control strategy series hybrid electric vehicles tuning

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Pre-training of an artificial neural network for software fault prediction 7

Pre-training of an artificial neural network for software fa...

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7th International Conference on Computer and Knowledge Engineering, ICCKE 2017

作者： Owhadi-Kareshk, Moein Sedaghat, Yasser Akbarzadeh, Mohammad-R.-T. Center of Excellence On Soft Computing and Intelligent Information Processing Ireland Dependable Distributed Embedded Systems Laboratory Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (纸本)9781538608043

Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve this classification problem. We propose to use a pre-training technique for a shallow, i.e. with fewer hidden layers, Artificial Neural Network (ANN). While this method is usually employed to prevent over-fitting in deep ANNs, our results indicate that even in a shallow network, it improves the accuracy by escaping from local minima. We compare the proposed method with four SVM-based classifiers and a regular ANN without pre-training on seven datasets from NASA codes in the PROMISE repository. Results confirm that the pre-training improves accuracy by achieving the best overall ranking of 1.43. Among seven datasets, our method has higher accuracy in four of them, while ANN and support vector machine are the best for two and one datasets, respectively. © 2017 IEEE.

关键词： Software testing

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Demand Response-based Energy Management Model for Energy Communities considering Data Privacy of the Members

Demand Response-based Energy Management Model for Energy Com...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Barreto, Ruben Gomes, Luis Faria, Pedro Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350396782

This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective event. The paper’s novelty lies in increasing the energy community members' active participation by allowing them to submit one or more proposals to participate in a demand response event. In this way, a member can, on the one hand, have more freedom to choose how to participate and, on the other hand, have more opportunities to contribute to the DR event with their energy flexibility. This model was tested with real data from an energy community with 50 buildings, which can provide flexibility through reductions and shifting. The results showed that the model’s efficiency could increase by considering the multiple proposals per member. Moreover, it was verified that with just one event, it is possible to reduce the CO2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%. © 2023 IEEE.

关键词： Active Participation Clustering Demand Response Energy Community Energy Flexibility

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Edgel index for large-scale sketch-based image search

Edgel index for large-scale sketch-based image search

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作者： Cao, Yang Wang, Changhu Zhang, Liqing Zhang, Lei MOE Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China Microsoft Research Asia Beijing China

ISBN: (纸本)9781457703942

Retrieving images to match with a hand-drawn sketch query is a highly desired feature, especially with the popularity of devices with touch screens. Although query-by-sketch has been extensively studied since 1990s, it is still very challenging to build a real-time sketch-based image search engine on a large-scale database due to the lack of effective and efficient matching/indexing solutions. The explosive growth of web images and the phenomenal success of search techniques have encouraged us to revisit this problem and target at solving the problem of web-scale sketch-based image retrieval. In this work, a novel index structure and the corresponding raw contour-based matching algorithm are proposed to calculate the similarity between a sketch query and natural images, and make sketch-based image retrieval scalable to millions of images. The proposed solution simultaneously considers storage cost, retrieval accuracy, and efficiency, based on which we have developed a real-time sketch-based image search engine by indexing more than 2 million images. Extensive experiments on various retrieval tasks (basic shape search, specific image search, and similar image search) show better accuracy and efficiency than state-of-the-art methods. © 2011 IEEE.

关键词： Efficiency

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Optimizing House Energy Management with MILP Considering Shifting of Electric Appliances and Battery Storage System

Optimizing House Energy Management with MILP Considering Shi...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Faia, Ricardo Faria, Pedro Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350396782

The integration of renewable energy sources into the power grid has led to the need for efficient energy management systems that consider the presence of energy storage systems and controllable loads. In this paper, the authors propose a mixed-integer linear programming optimization model for energy management that takes into account the operation of shift electric appliances and battery storage systems. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electric appliance met with the user requirements. The authors consider the operational constraints of shifting electric appliances, which can be scheduled to operate during periods of low electricity prices. Additionally, they account for the energy storage system, which can be charged during periods of excess renewable energy production and discharged during periods of high demand. Four different scenarios are tested to prove the value of the model. © 2023 IEEE.

关键词： Battery storage systems Minimize Costs Shift appliances

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Cyber-physical-social System in intelligent Transportation

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IEEE/CAA Journal of Automatica Sinica 2015年第3期2卷 320-333页

作者： Gang Xiong Fenghua Zhu Xiwei Liu Xisong Dong Wuling Huang Songhang Chen Kai Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Science the Cloud Computing Center Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Beijing Engineering Research Center for Intelligent Systems and Technology Institute of AutomationChinese Academy of Science

A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it's a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail. © 2014 Chinese Association of Automation.

关键词： Amphibious vehicles Complex networks Embedded systems intelligent systems Transportation

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Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA

Time Series Forecasting of COVID-19 Infections in United Ara...

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

作者： Ismail, Leila Alhmoudi, Shaikhah Alkatheri, Sumyah United Arab Emirates University Distributed Computing and Systems Research Laboratory College of Information Technology Abu Dhabi15551 United Arab Emirates

ISBN: (纸本)9781728176246

Machine learning time series models have been used to predict COVID-19 pandemic infections. Based on the public dataset from Johns Hopkins, we present a novel framework for forecasting COVID-19 infections. We implement our framework for the United Arab Emirates (UAE) and develop autoregressive integrated moving average (ARIMA) time series forecast model. To the best of our knowledge, this is the only study to forecast the infections in UAE using the time series model. © 2020 IEEE.

关键词： COVID-19

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