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检索条件"任意字段=2018 Conference on Electrotechnology: Processes, Models, Control and Computer Science, EPMCCS 2018"

共 71 条记录，以下是51-60 订阅

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A New Approach to Client Onboarding Using Self-Sovereign Identity and Distributed Ledger

A New Approach to Client Onboarding Using Self-Sovereign Ide...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Reza Soltani Uyen Trang Nguyen Aijun An Department of Computer Science and Engineering York University Toronto Canada

Existing client onboarding and Know Your Customer (KYC) processes are typically slow, expensive and often accomplished in-person. Moreover, the current identity management models in practice deprive users from having complete control over their digital identity data. Users' identity attributes are stored on multiple centralized repositories, which often follow inadequate security policies. In this paper, we take advantage of Hyperledger Indy, a public and permissioned distributed ledger technology (DLT), to develop a digital onboarding framework based on the Self-Sovereign Identity (SSI) principles. With this framework we take a step towards tackling a number of weaknesses in current KYC processes and identity management models, while addressing the requirements associated with SSI, Privacy by Design and European Union's General Data Protection Regulation (GDPR).

关键词： Distributed ledger Blockchain Cryptography General Data Protection Regulation Data models Privacy

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Software Process Definition using Process Lines: A Systematic Literature Review

Software Process Definition using Process Lines: A Systemati...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Diogo Matheus Costa Eldânae Nogueira Teixeira Claudia Maria Lima Werner Systems Engineering and Computer Science Department PESC/COPPE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781728104386

Software processes have been the focus of discussion in literature, but defining a software process remains a challenge. The Software Process Line (SPrL) technique offers a systematic process reuse approach that identifies processes' similarities and variability. This study aims to characterize the state-of-the-art of the software process definition using SPrL. A Systematic Literature Review (SLR) was conducted and 26 papers were identified to be studied in depth. The results indicate a concentration of approaches that use mapping/rules techniques to support variability resolution and the need for integrated supporting tools. In addition, three problems that affect the software process definition using SPrL were observed: (1) low understanding of SPrL models complexity and their impact, (2) lack of understanding about the impacts of the defined software process, and (3) experts' knowledge dependence issues in decision-making to solve variability.

关键词： Software Bibliographies Tools Systematics Industries Process control Systems engineering and theory

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Knowledge acquisition during software development: Modeling with anti-patterns 1st

Knowledge acquisition during software development: Modeling ...

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1st Workshop on Knowledge Engineering and Software Engineering, KESE 2005, Co-Located with 28th German conference on Artificial Intelligence, KI-2005

作者： Smiari, Paraskevi Bibi, Stamatia Stamelos, Ioannis Department of Informatics & Telecommunications Engineering University of Western Macedonia Kozani Greece Department of Computer Science Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783319641607

Knowledge is a strategic resource;that should be timely acquired and exploited to manage and control software development. Software development is a knowledge intensive process characterized by increased uncertainty, presenting large variations among different development environments. Project uncertainty and volatility confounds the traditional knowledge-based processes since at any time traditional software project management techniques and patterns may be considered out of scope. In this chapter a dynamic and constantly adaptive knowledge encapsulation framework is presented. This framework analytically describes (a) metric collection methods along with metrics that attribute to knowledge creation regarding successful software development (b) representation mechanisms of the knowledge created in the form of anti-patterns (c) Bayesian Network analysis technique for converting the data to knowledge allowing inference mechanisms for testing the applicability of the anti-pattern. The presented approach is demonstrated into a case study showing both its feasibility and applicability. © 2018, Springer International Publishing AG.

关键词： Bayesian networks

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A Data-Driven Framework for Identifying Tropical Wetland Model

A Data-Driven Framework for Identifying Tropical Wetland Mod...

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UKACC International conference on control (control)

作者： Angesh Anupam David J. Wilton Sean R. Anderson Visakan Kadirkamanathan Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom Computer Science The University of Sheffield Sheffield United Kingdom

A wetland is a land area that is saturated with water. Most of the wetlands exhibit seasonal variations because of soil characteristics, climate variables and orography of a site. This study applies the orthogonal least square (OLS) algorithm under the system identification methodology for the identification of a nonlinear dynamic model structure of the tropical wetlands, using a remotely sensed dataset. Despite the availability of data from the multiple tropical sites, a single dynamic-model structure is able to explain the underlying processes, governing the wetland extents of the tropics. The model is validated against a fresh data set, derived using the similar remote sensing technique. Overall, this study is a novel application of the systems identification for obtaining a single model structure of a category of wetlands, enabling some understanding about their dynamics. The model can also be employed for the assessment of future wetlands in the advent of climate change.

关键词： Wetlands Mathematical model Brain modeling Meteorology Data models Remote sensing

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Effects of the rewiring of promoter activity states on gene expression

Effects of the rewiring of promoter activity states on gene ...

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Chinese control conference

作者： Heli Tan School of Mathematics and Computational Science Xiangtan University Xiangtan China

Gene transcription takes place often in a bursty manner. The gene promoters contain multiple activity states which compose complex patterns via chromatin remodeling. An entirely unexplored question is how the rewiring of promoter activity states affects gene expression. Here, we systematically analyze and compare two representative models of stochastic transcription: the one is that promoter activity states form a chain-type structure and the other is that they form a star-type structure. After the theoretical analysis and the numerical simulation, some interesting phenomena have been found. Specifically, both the increase in the mean level of mRNA and the decrease in the expression noise are quicker in the chain-type structure than in the star-type structure with the enhanced recruitment mechanism. The recruitment mechanism strength has unchanged the change rate of mean burst frequency in the chain-type structure but reduced it in the star-type structure. The star-type structure has shorter first passage time but more fluctuations than the chain-type structure. These results indicate that the transcription outcome is of promoter architecture-specific.

关键词： Gene expression computer architecture Biological system modeling DNA Stochastic processes Kinetic theory Mathematical model

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Detection of Change Point in Process Signals by Cascade Classification

Detection of Change Point in Process Signals by Cascade Clas...

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International Russian Automation conference (RusAutoCon)

作者： Sergey Gavrin Damir Murzagulov Alexander Zamyatin Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia

ISBN: (纸本)9781538649398

Nowadays, industrial companies are more than ever forced to dynamically adapt their business process executions to currently existing business situations in order to keep up with increasing market demands in global competition. Companies that are able to analyze the current state of their processes, forecast its most optimal progress and proactively control them basing on reliable predictions will stay significantly ahead their competitors. This result can be achieved by embedding predictive analytics systems, such systems allow to prevent emergencies and optimize existing processes on the basis of historical process data. The analysis and processing of process signals are at the heart of these systems. These tasks are reduced to a number of other subtasks, one of the most important of them is the detection of a change point in the process signals. Change points are abrupt variations in process signals. Such abrupt changes may represent transitions that occur between states of equipment or process stages. The paper proposes the application of the cascade classification of time series approach for detecting the region of a changepoint in process signals. It is assumed that this approach to the refinement of the moment of a changepoint is more suitable for signals characterizing the different flow stages of the processes. The rationale for using this approach in the field of automated control systems is presented, the results of experiments performed on model data with different levels of noise are demonstrated.

关键词： Classification algorithms Time series analysis Prediction algorithms Signal processing algorithms Automation Process control Data models

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A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data

A Gaussian Process Regression for Natural Gas Consumption Pr...

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International conference on Information Fusion

作者： Oussama Laib Mohamed Tarek Khadir Lyudmila Mihaylova Dep. of Computer Science University of Badji Mokhtar Annaba (UBMA) Annaba Algeria Dep. of Automatic Control and Systems Engineering (ACSE) University of Sheffield Sheffield UK

For several economical, financial and operational reasons, forecasting energy demand becomes a key instrument in energy system management. This paper develops a natural gas forecasting approach, which consists of two major phases: 1) it classifies the natural gas consumption daily pattern sequences into different groups with similar attributes. 2) the design and training of multiple autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (temperature) and calendar (day of the week, hour indicator) factors. The main novelty in this work consists of the investigation of multiple different clustering techniques for better analysis and clustering of natural gas consumption data. The impact of the obtained clusters, by each technique, is then summarized and evaluated with respect to the prediction accuracy.

关键词： Natural gas Forecasting Gaussian processes Load modeling Predictive models Clustering methods Kernel

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control diffusion of information collection for situation understanding using Boosting MLNs

Control diffusion of information collection for situation un...

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International conference on Information Fusion

作者： Erik Blasch Ali K. Raz Robert Cruise Tim Kelly Sriraam Natarajan Air Force Office of Scientific Research Dynamic Data Driven App Sys. Arlington VA Crane Division Naval Surface Warfare Center Crane IN Computer Science Univ. Texas at Dallas Dallas TX Aeronautics and Astronautics Purdue University West Lafayette IN

Information fusion includes the integration of data for situational understanding. As a situation unfolds, maintaining awareness depends on diverse collections of data. In complex and dynamic scenarios, human operators face the difficult task of choosing which data to collect next. Hence, there is a need for multilayered fusion processes that exploit multiple models and levels of abstraction for understanding and sense-making Data collection has its roots in sensor management;however, there is an analogous need for data management - such as the incorporation of public domain data. Mature sensor management includes methods to utilize platform, sensor, and scene modeling so as to guide the user for future data collection. Additionally, these physics-based models could be a method to guide human-derived information models. Using the Data Fusion Information Group Model (DFIG), we develop an equivalent method for diffusion control. This paper focuses on recent techniques in statistical relational learning (SRL), Markov logic networks (MLN), and ontologies to support the control diffusion of data sensing to answer user queries.

关键词： Information fusion Machine learning Automation and autonomy Dissemination

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Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning

Ensemble Kalman Filtering for Online Gaussian Process Regres...

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International conference on Information Fusion

作者： Danil Kuzin Le Yang Olga Isupova Lyudmila Mihaylova Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK Department of Electrical and Computer Engineering University of Canterbury Christchurch New Zealand Department of Engineering Science The University of Oxford Oxford UK

Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.

关键词： Kalman filters Gaussian processes Estimation Prediction algorithms Bayes methods Covariance matrices Noise measurement

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Detecting Stealthy False Data Injection Attacks in Power Grids Using Deep Learning

Detecting Stealthy False Data Injection Attacks in Power Gri...

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International Wireless Communications and Mobile Computing conference, IWCMC

作者： Mohammad Ashrafuzzaman Yacine Chakhchoukh Ananth A. Jillepalli Predrag T. Tosic Daniel Conte de Leon Frederick T. Sheldon Brian K. Johnson Department of Computer Science Department of Electrical and Computer Engineering University of Idaho Moscow ID USA Center for Secure and Dependable Systems

The electric power grid, as a critical national infrastructure, is under constant threat from cyber-attacks. State estimation (SE) is at the foundation of a series of critical control processes in a power transmission system. A false data injection (FDI) attack against SE can disrupt these control processes, crippling a power system and wreaking havoc in a region. With knowledge of the system topology, a cyber-attacker can formulate and execute stealthy FDI attacks that are very difficult to detect. Statistical and, more recently, machine learning approaches have been undertaken to detect FDI attacks on SE of the power grid. In this paper, we propose a Deep Learning (DL) based method to accurately detect stealthy FDI attacks on the SE of power grid. We compare the performance of the DL method with three popular machine learning algorithms, which are: gradient boosting machines (GBM), generalized linear modelings (GLM) and distributed random forests (DRF). All four algorithms analyze a dataset simulating the IEEE 14-bus system. The results demonstrate that these algorithms perform well in accurately and precisely detecting stealthy FDI attacks on the smart grid, with the DL-based approach showing best results.

关键词： Pollution measurement Machine learning State estimation Data models Smart grids

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