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Accurate state of charge (SOC) is critical for battery energy management system in electric vehicle (EV) application. Overcharge and over discharge will shorten battery's lifespan and induce potential safety problem, which may even permanently damage the lithium-ion battery. Thus, a data driven model is proposed for improving the accuracy of SOC estimation in this paper. A preliminary mathematic model...
An 8×8 (64) Internet of Things (IoT) wind farm platform is built using miniaturized wind turbines with wireless connectivity. The farm is being deployed on the south east coast of Ireland to remotely collect data for offline evaluation of a data driven wind turbine power output model and study aerodynamic interactions between turbines within the farm. The cluster is build such that a range of different...
Nowadays, smart grid for electrical power distribution is considered as one of the most important infrastructures enabling the consumer to interact with other stakeholders to reach technical and economic objectives. This interactivity is performed through the concept of agreements that are signed between different involved actors (utility operators, electrical energy producers, consumers, electrical...
Home Energy Management System (HEMS) is acknowledged as a promised approach to explore household appliances dynamic energy usage. The availability of an appropriate dataset is indispensable to evaluate the performance of HEMS operations in the designing phase. In this paper, we develop a tool capable of generating long-term semi-synthetic data to avoid deficiency of available datasets particularly,...
Biotechnological processes are very complex systems for modeling, due to the presence of live microorganisms, nonlinear behavior and time-variant features. Many parameters of the process are uncertain and the industrial operating conditions are determined empirically for them. This paper presents a robust dynamic simulator in Matlab/Simulink environment from the work of Scaglia and the study of experimental...
We have designed and implemented an unsupervised learning algorithm for finite mixture model using the scaled Dirichlet distribution for multivariate proportional data. In this paper, the task of learning finite mixture model involves estimation of model parameters as well as inferring the hidden class information of our observed data. We made use of the expectation maximization algorithm to find...
Data heterogeneity and proprietary interfaces present a major challenge for big data analytics. The data generated from a multitude of sources has to be aggregated and integrated first before being evaluated. To overcome this, an automated integration of this data and its provisioning via defined interfaces in a generic data format could greatly reduce the effort for an efficient collection and preparation...
This paper describes a novel approach for systematic support of engineers using model-driven system architectures for process and automation engineering of plants. We explored a new aspect of engineering virtual smart objects in plant data-models which is object's lifeflow. In principle, lifeflow is the dynamic and adaptive answer to the question of “what happens?” to each engineering object, in its...
The variability of modern automation systems is getting the key factor in determining the efficiency in manufacturing. The modularity of the employed automation components contributes significantly to the level of variability by enabling multiple compositions of automation components. In order to enable a high modularity, each automation component should be modeled as an integrated mechatronic model...
The development process of modern automated manufacturing plants requires a concurrent engineering process that integrates different engineering disciplines. Envisioning a concurrent development process, we propose an engineering process based on an AutomationML metamodel. The proposed metamodel contains standardized mechatronic component models, which acts as the base engineering data for the disciplines...
In this paper, we present a novel anomaly detection method which addresses the main challenge of self-organizing industrial systems: the state space explosion. In particular, the flexibility and dynamic nature of such systems result in an exponentially growing number of possible execution plans. To handle this problem, we propose to learn the underlying topology, instead of storing whole paths a work-piece...
Customer retention in Telecom market is a big research challenge in developed as well as developing economies as the market is almost saturated as well as competitive with large number of local and global service providers. It is also well known that from business point of view retaining an existing customer is much less costly than acquiring a new one. Hence retaining existing customer by making...
This paper analyzes the battery capacity degradation under different state of charge(SOC) ranges, which indicates that battery capacity degradation can be notably reduced by adjusting the way of charging electric vehicles(EVs). A customary way of charging is defined according to practical driving data. Economic analysis and numerical example, including ideal way of charging(IWC), the worst way of...
In order to connect existing electronic devices that are equipped with fieldbus communication capabilities into the emerging Cyber Physical Systems (CPS), new models that combine live data from the underlying devices together with the metadata information that describes them have to be created. The new solutions should be based on widely accepted communication standards and should be easy to implement...
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