Arash Mousavi

Fault detection in the hyperspace: Towards intelligent automation systems

This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection the proposed methodology shows an unprecedented performance, while featuring zero configuration and simple operations.

An ontology driven, multi-agent based framework for automated resource allocation in Mobile Workforce Management systems

Rapid advancement in Mobile Information and Communication Technology (ICT) caused an enormous increment in mobile workforce population of the world. Consequently, this rapid increment created a high-demand for reliable and automated mobile workforce management (MWM) amongst them, Resource Allocation (RAL), which is the focus of this paper, is of high importance. The better performance and reliability that an RAL system exhibits, the more reliable its corresponding MWM will be. A reliable resource allocation process in a mobile environment however, should be able to adequately challenge human resource risks (unpredictable unavailability) as well as environmental risks, such as frequent disconnection, caused by mobility of the resources. In this paper an ontology driven multi-agent based framework for such RAL systems is proposed. The main emphasis of this framework is to describe a dynamic coordination model for Mobile Workforce Brokerage System, a multi-agent system which is utilized to automate the resource allocation process in presence of the abovementioned risks and challenges.

Power Consumption Modeling of Data Center IT Room with Distributed Air Flow

Modern data centers are characterized by large sizes, high energy consumption and complexity involving IT, power supply, ventilation and cooling. Data center energy efficiency is a major concern fo ...

Towards energy smart data centers: Simulation of server room cooling system

Cooling is an extremely important process in modern data centers. Cooling systems of server rooms ensure appropriate operation conditions to IT systems, such as servers and data storage, but, on the other side, they consume a lot of energy. Current control systems, which are installed in data centers and are responsible for thermal management of the facilities, are following conservative control strategies that maintain constant thermal conditions irrespective of computer load and outside temperature, thus efficient use of energy has not been appropriately addressed by them. In this paper, a method of optimizing energy consumption while maintaining an acceptable level of thermal comfort for CPUs in the server room is proposed. In the proposed method, a behavioral thermal model for server room should be created first, and then thermal behavior of the server room would be simulated under different circumstances using its thermal model, in order to find an optimum control strategy capable of retaining balance between thermal comfort and efficient use of energy. The effectiveness of the proposed method has been investigated via simulating a typical server room using MATLAB and SIMULINK and the results are demonstrated.

Energy efficient automation model for office buildings based on ontology, agents and IEC 61499 function blocks

Execution of business processes is an important factor that distinguishes residential and office buildings based on their energy usage. Unlike residential buildings, in offices workflows determine how and when energy-consuming devices have to be utilized. Thus, energy efficient building automation systems for office buildings should take into account the dynamic and unpredictable nature of business processes. However, the existing systems lack this important feature. In this paper, a model for combining automation and business processes is proposed. The model is implemented using IEC 61499 Function Block architecture, multi-agent systems and ontology. The proposed method has been examined in a meeting room scheduling scenario, in which meeting scheduling and automated control of the meeting room equipment have been combined and the energy usage has been measured to evaluate the improvement in efficient use of energy using the proposed model.

On automatic generation of IEC61850/IEC61499 substation automation systems enabled by ontology

This paper presents an introductory step in the automatic generation of distributed control software for power distribution automation systems based on Ontology Driven Engineering enabled by industrial standards IEC61850 and IEC61499. The novelty of this approach is the ability of automatically generating the logical connections between the logical nodes. The paper covers the several stages of the transformation process, such as developing the IEC61850 ontology and the ontology transformation rules. The developed IEC61850 ontology includes the logical nodes descriptions and additional contextual relations between the logical nodes which is lacking in the IEC61850 SCL configuration language. Then, the IEC61850 ontology is transformed to an existing IEC61499 ontology, adding classes of IEC61850 logical nodes as IEC61499 function blocks in the IEC61499 ontology. The means of the transformation of the ontologies is based on the eSWRL sematic web rules language, an extension to the rule language SWRL. The end result is the development of an IEC61850 ontology and a set of eSWRL rules which facilitates the ontology transformation.

Whitening of Background Brain Activity via Parametric Modeling

Several signal subspace techniques have been recently suggested for the extraction of the visual evoked potential signals from brain background colored noise. The majority of these techniques assume the background noise as white, and for colored noise, it is suggested to be whitened, without further elaboration on how this might be done. In this paper, we investigate the whitening capabilities of two parametric techniques: a direct one based on Levinson solution of Yule-Walker equations, called AR Yule-Walker, and an indirect one based on the least-squares solution of forward-backward linear prediction (FBLP) equations, called AR-FBLP. The whitening effect of the two algorithms is investigated with real background electroencephalogram (EEG) colored noise and compared in time and frequency domains.

Improvement of energy efficiency in data centers via flexible humidity control

The main goal of climate control systems in data centres is to keep the temperature and humidity in a suitable level for computational devices. Therefore, cooling and humidification systems are essential parts of every Building Automation System (BAS), which is utilized in server rooms. Although the current climate control systems ensure appropriate thermal conditions to computational nodes such as servers, they waste substantial amount of energy. The main cause of this inefficiency is that the current climate control systems, which are responsible for thermal management of the data centres, follow rigid control strategies that maintain constant thermal conditions irrespective of climate changes caused by various computational loads in the plant. To address this issue, in our previous works we proposed a method of optimizing energy consumption in data centre cooling systems while maintaining an acceptable level of thermal comfort for CPUs in the server room. In this paper, we present the enhancement of our previous method by incorporating humidity control into it. The enhanced method consists of a thermal model of server room, and a simulation tool to find an energy efficient control strategy for the climate control system in different situations by comparing different control strategies. The effectiveness of the proposed method has been investigated via simulation and the result, which shows 41.5% reduction in total energy consumption is presented.

Power quality assessment of energy efficient cooling systems in data centers

Power quality is an important aspect in data centers and power system operators prefer data centers with high standard power quality. Modern data centers consume large amount of electrical power. As the cooling systems are accounted for 40% of the total power consumption in data centers, energy efficiency in this sector is of high importance. This research investigates if the existing energy efficient cooling strategies can also meet the power quality level defined by the IEEE 519-2014 standard. In this research three automation strategies based on IEC 61499 standard for cooling system in a typical server room are considered and the effects of each one on power quality parameters are investigated via simulation. The investigation has been performed for both; internal power grid of data center and upstream power network that data center is connected to. Power quality parameters which are considered are Total Harmonic Distortion (THD), voltage and current distortions. Results of simulation prove that the automation strategies with highest energy efficiency, also meet the higher power quality standard.

Monitoring and modelling open compute servers

Energy efficient control of server rooms in modern data centers can help reducing the energy usage of this fast growing industry. Efficient control, however, cannot be achieved without: i) continuously monitoring in real-time the behavior of the basic thermal nodes within these infrastructures, i.e., the servers; ii) analyzing the acquired data to model the thermal dynamics within the data center. Accurate data and accurate models are indeed instrumental for implementing efficient data centers cooling strategies. In this paper we focus on a class of Open Compute Servers, designed in an open-source fashion and currently deployed by Facebook. We thus propose a set of methods for collecting real-time data from these platforms and a control-oriented model describing the thermal dynamics of the CPUs and RAMs of these servers as a function of both manipulable and exogenous inputs (e.g., the CPU utilization levels and the air mass flow produced by the servers fans). We identify the parameters of this model from real data and make the results available to other researchers.