Today's electrical energy generation and distribution systems are being faced with a number of issues, from violent weather to earthquakes and landslides, including acts of terrorism and vandalism. All these are grave concerns stemming from environmental as well as operational, and societal issues. Therefore, utilities' electric power distribution infrastructure is required to respond to such challenges very rapidly and effectively so as to preserve stability and continuity of operations. This is the true measure of what sustainable energy systems (SES) are all about and homeostaticity of energy systems seeks just that: to bring about a rapid, highly effective and optimally efficient state of equilibrium between energy supply and energy consumption in electric power systems (EPS). In this paper we present the theoretical groundwork and offer a partial example of the prescriptive homeostaticity model for controlling utility-operated microgrids. The paper explains how the engineering of homeostaticity works and how reactive and predictive homeostasis, acting concomitantly, play a key role in these systems dynamics, namely the grid-tied microgrid, the grid and the energy consumers. Reactive homeostasis (RH) is an immediate response of the energy system to a homeostatic stress or perturbation, such as energy deprivation or shortage or an energy imbalance. RH entails feedback mechanisms which enable a reactive compensation when the need arises, reestablishing homeostaticity in the system. Predictive homeostasis (PH), on the other hand, anticipates the contingencies that are likely to occur, and then acts upon them enabling the energy system to respond in a proactive manner to environmental challenges and/or other concerns. Major aspects of the model are briefly explained along with the mechanics of homeostasis applied to SES and expected results.
The information given by the evolution of a natural system is generally a non-linear, non-recurrent and non-stationary temporal series. Therefore, when analyzing these data with linear and stationary formalism (such as FFT) or a normative base (FFT, Wavelet), signals is introduced that are not part of the original set, and their interpretation is inadequate. So, it is necessary to introduce an adaptive and empirical formalism, which allows analyzing an non stationary and non-linear signal, breaking in sub signals of intrinsic mode (IMF) characterized by its frequency and instantaneous amplitude. The temporal analysis of the frequency is useful because in certain phenomena, the evolution of it gives valuable information on the analyzed system. In the process, the tactile perception associated with the capture of information excites the sensory synapse, activating for approx. 1-2 seconds the sensory memory (SeM) and if a level of "sufficient attention" is exceeded, the sensory information is encoded and controlled by the working memory (WoM), activating actions of knowledge registration in the synaptic mental form, enabling the process storage of this information, in the long-term memory. In this context, this paper presents the analysis of an EEG signal through the Hilbert Huang Transform (HHT), applied to memorization of objects using the tactile sense that is, depriving the individual of vision and hearing. An empirical mode decomposition (EMD) is developed and the Hilbert spectral analysis is obtained revealing the temporal-frequency energy distribution of phenomena. As a result of this process an activity marker appears that can be associated to the wait (timeout) between the memorization of two objects corresponding to 0.2 sec. The activity marker is located in the second component of signal intrinsic mode (IMF2). When observing the instantaneous frequency, after the subject in study leaves a first object and prepares to receive the second, it is perceived that this frequency varies between 3 and 7 Hz for the channels AF3, F7, F3, FC5 of the left frontal lobe. This pattern is repeated in the right frontal lobe, but disappears more quickly. Finally, it can be concluded that Hilbert Huang Transform allows discriminating instantaneous frequencies and locating patterns not detectable by traditional systems of signal analysis.
Methods based on Rapidly-exploring Random Trees (RRTs) have been in use in robotics to solve motion planning problems for nearly two decades. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers. These controllers handle the power of motors according to motion commands usually generated by planning algorithms, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a new variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by presenting a model for path planning of mobile robots based on the bidirectional RRT algorithm. A software for RENPSM has been developed within the Robot Operating System (ROS) and simulation experiments have been conducted by means of the Pioneer 3-DX robot simulation platform.
This paper presents a method to characterize, select and evaluate technological options to produce ferric chloride. The methodology uses AHP (Analytic Hierarchy Process) enhanced with fuzzy logic (FEAHP). With this methodology it is possible to include the vagueness associated with the evaluators in the decision making to evaluate a new business and select technologies. Producing ferric chloride will reduce or eliminate effluents from the molybdenum production process, such as ferrous chloride, reducing environmental contamination of the process and achieving a potential new business. A model was designed using existing literature on AHP, FEAHP and other assessment tools to select technologies and evaluate new businesses that reduce environmental impact. The model was validated using a case study related to using ferrous chloride, which is an effluent from the molybdenum leaching process, producing ferric chloride that can be transformed into a new business with the benefit of reducing environmental pollution. By applying the extended AHP method with fuzzy logic to model the uncertainty of the experts regarding the comparative judgments, the analysis performed in this work allowed to discard technological options to reprocess the ferrous chloride and to obtain ferric chloride, leaving finally two options that were the alternatives with greater weight, which later were evaluated economically. The method facilitates decision making regarding the selection or prioritization of technologies to evaluate new business in environmentally sensitive areas. The contribution of this work is the proposed method based in FEAHP to select technologies to produce a product and open a new market. The method allowed selecting options of technology in a rare business with great environmental impact for the zone. In the case study the results indicated the desirability of not increasing the production by more than 25%, discarding, for example, options of doubling the production because of the environmental risks.
The communications infrastructure, especially mobile communications, has experienced a significant growth in voice and data traffic, largely due to the subscriptors rising, as well as the new services and / or applications implemented to provide the services required in a smart city. The main challenges generated by this continuous growth are related to capacity, coverage and interference in the access network. In addition to the technical challenges, the market dynamics bring on the need to propose technical strategies aimed to satisfy the users' expectations with regard to the acquired services. In this paper we presents a planning algorithm that select the best cooperation technique between base stations. The aim of this algorithm is that the operator not only improve the network resources utilization of the deployed infrastructure, but at the same time, results in an improvement in the perception of the quality of services by users.
The Smart campus is a new idea in the development of information technology, being a combination of the Cloud Computing, the Internet of Things and other emergent technologies. This paper presents the result of our research efforts to find the best solutions for the development of a smart campus, which also guarantees data security with a special focus on data confidentiality. Our solution proposes 5 levels of security for the cloud security architecture; it provides a high level of security, as well as high data confidentiality — the essential backbone for a truly smart campus development.
Work of any researcher involves a continuous need to obtain information on research topics over the world. It is important to understand what has already been done and what is most relevant in the research area. Such kind of information can be obtained using huge knowledge bases, however, new approaches to their analysis are required. Application of recently developed models of network analysis combined with semantic analysis methods for publications related to Parkinson's disease allows to reveal the links between research clusters, rank their importance and attract scientific groups' attention to, for instance, previously unknown studies and developments.
What threatens the cyberspace is known as malware, which in order to infect the technological devices, it has to be capable of bypassing the antivirus motor. To avoid the antivirus detection, the malicious code requires to be updated and have undergone an obfuscation process. However, the problem of the updating is to consider that the malware maintains its functionality based on its specific characteristics, and also to be checked by specilized informatic resources. For the aforementioned, this paper proposes a procedure that allows to apply the AVFUCKER, DSPLIT, and Binary Division techniques with the aim of optimizing the necessary technological resources, and reducing the time of analysis of the malware's functionality and the evasion of the antivirus.
This research is centered on how predictive analysis can support decision making on energetic consumption in the great copper mining in Chile. In this article it is analyzed the data base of energetic consumption in the Grinding A0, A1 and A2, in Codelco, Chuquicamata, in the period 2007-2014. This study uses the Box-Jenkins method for predicting energetic consumption in the Grinding. Before this analysis, it is achieved to predict the behavior of the time series of energetic consumption under stationary conditions, through polynomial delay equations, Seasonal Auto Regressive Integrated Moving Average (SARIMA). As a result, it is obtained that most of the consumptions lines in the Grinding A0, A1, and A2, present some type of trend and stationary. The SARIMA model was able to adapt to the behavior of the variables of the energetic consumption of the copper productive process. It is possible to predict the energetic consumption as critical information in the decision making in the Grinding A0, A1 and A2.
Next Generation Mobile Networks are envisioned to integrate and coordinate Heterogeneous Networks with the aim to cope with the new mobile traffic demands by taking advantage of the features of each wireless network. In this context, it is accepted that a centralized management resource framework is required by Mobile Network Operators in order to efficiently manage the scarce and limited radio resources from each wireless network. One of the main challenges for radio resource management architecture is the network selection function. We investigate the problem of Radio Access Network selection for a Heterogeneous Networks scenario with the objective of distributing the traffic among several Radio Access Networks in a fair way. The problem is theoretically modeled as a sequential decision-making problem using Semi Markovian Decision Process, where the optimization problem seeks to maximize the long-term discounted reward of the Heterogeneous Network system. In addition to this, taking advantage of the departure of sessions, we have considered a load distribution process that allows offloading of traffic to alleviate the load of the macro cell. In order to solve the model and obtain an optimal policy, we have used Value Iteration algorithm. From the resulting policy, the blocking probability for each possible event in the system is calculated. Several simulations were carried out, and the obtained results indicate that the proposed network selection strategy exhibits good performance for distributing the traffic load in a fair way among several wireless networks.
The International Conference on Computers Communications and Control (ICCCC) has been founded in 2006 by I. Dzitac, F.G. Filip and M.-J. Manolescu and organized every even year by Agora University of Oradea, under the aegis of the Information Science and Technology Section of Romanian Academy. ICCCC2018 has been co-sponsored by IEEE Region 08. The goal of this conference was to bring together scholars and researchers, from academia and industry practitioners to present and discuss in a friendly environment their latest research findings on a broad array of topics in computing, computer communications and control. The Program Committee received papers describing original, previously unpublished, completed research, not currently under review by another conference or journal, addressing state-of-the-art research and development in all areas related to computer networking and control.
A decision making methodology is proposed as support and guidance to evaluate an Information and Communications Technology system (ICT) in healthcare service processes involving agents perspective. Through multicriteria approach, by the use of the Analytic Hierarchy Process (AHP), pilot studies are carried out in health institutions in Chile to examine ICT system demands, ICT infrastructure available, and the relative importance of quality of service needs. The results will give information to assist decision makers for resource distribution. The proposed conceptual prototype offer a base for detecting the more relevant ICT system to perform health service and to dimension the crucial factors concerning quality of service issues.
This paper proposes an ensemble Kalman filter implementation for non-linear data assimilation. As in any ensemble based method, the moments of background error distributions are approximated by means of an ensemble of model realizations. The precision background covariance is estimated via a modified Cholesky decomposition in order to decrease the impact of sampling errors. Once all hyper-parameters are estimated, samples from the posterior distribution are estimated via a Markov-Chain-Monte-Carlo (MCMC) method. The MCMC implementation is enhanced by means of linear approximations of the observation operator. Posterior ensembles are then built by using a series of rank-one updates over prior Cholesky factors. Experimental tests are carried out by using the Lorenz 96 model. The numerical results evidence that, as the degree of the observational operator increases, the accuracy of the proposed filter is not affected and even more, for full observational networks, posterior errors are much lower than those of backgrounds, in some cases, by several order of magnitudes.
Monitoring the concentration of gases in under-ground coal mines is a mandatory process that may save lives. Wireless Sensor Networks (WSNs) can alert mine personnel when dangerous levels of gases are detected, such as methane and carbon monoxide that may cause explosions or poisoning of workers. WSNs must be designed to cover all active mining areas throughout a specified time horizon, thus, posing a trade-off between the cost of installation (i.e., sensors) and operation (i.e., energy consumption). While most literature on node deployment for WSNs in mines focuses on single objective functions, we propose a novel two-stage approach that allows users to address both the cost of installation and the WSN lifetime: in the first stage, our model provides the lowest cost solution that satisfies a user-specified minimum lifetime; in the second stage, the configuration of the WSN is modified in order to maximize its lifetime, subject to maintaining the number of nodes (and, hence, installation costs) from the first stage. Illustrative examples are presented regarding two real coal mines in Boyaca (Colombia).
The next generation networks are expected to provide a wide variety of high data rate services and seamless connectivity in all environments including highly mobile networks. Today, there is a rapid increase in high mobile systems and applications. This rapid increase causes the requirement of systems to be reliable to share their resources without delay in order to ensure a better quality of service (QoS) for mobile users. In order to provide better QoS and ubiquitous communication in highly mobile environments, the proactive handover techniques are recommended to be used rather than the traditional reactive approaches. This paper presents a proactive approach for resource allocation in highly mobile networks and analysed the user contention for common resources such as radio channels in wireless networks. The proposed approach uses Markov chains to model the contention and results are obtained showing enhanced system performance in terms of mean queue length, the mean response time, throughput and blocking probability. Based on these results an operational space has been explored and are shown to be useful for emerging future networks such as 5G by allowing base stations to calculate the probability of contention based on the demand for network resources. The model has been validated using simulation and the results show that the proactive approach can be evaluated efficiently and accurately using the proposed analytical model. In addition, this study indicates that the proactive model enhances handover and resource allocation for highly mobile networks.
This paper analyses a particular reliability scheme known as hammock networks. These seem to be a very good fit for arrays of FinFET transistors (but also segmented bulk MOSFET, vertical FET, vertical slit FET, gate-all-around FET), as well as arrays of beyond CMOS devices. In particular, our aim is to study compositions of such networks. By composition of two hammock networks we mean a hammock network where the transistors/switches themselves are replaced by hammock networks (of transistors/switches). In order to study such compositions we start from fresh results computing exactly the reliability polynomials associated to small hammock networks. We use these results to compute exactly the reliability polynomials associated to compositions (of hammock networks). Finally, compositions of hammock networks are compared with a (square) hammock network of the same size (i.e., number of transistors/switches), while also accounting for the number of wires. Analyzing the results reveals that compositions of hammocks present interesting symmetries, and, while they do come close to the reliability of a single (square) hammock network, they are always slightly lagging behind.
In this paper we propose the usage of a prediction technique based on Markov Chains to predict nodes positions with the aim of obtain short paths at minimum energy consumption. Specifically, the valuable information from the mobility prediction method is provided to our distributed routing algorithm in order to take the best network decisions considering future states of network resources. In this sense, in each network node, the mobility method employed is based on a Markov model to forecast future RSSI states of neighboring nodes for determining if they farther or closer within the next steps. The approach is evaluated considering different algorithms such as: Distance algorithm, Distance Away algorithm and Random algorithm.
Technological development and software engineering have been provided by companies and diverse types of organizations with the ability to manage virtually all of their production processes and services in computer systems, generating, as a result, the need to store and analyze huge datasets. As a consequence, the intensive use of software has caused an exponential growth of the amount of data, making the analytical use of information an arduous task through traditional software development techniques. On the other hand, for many years, software producers have taken advantage of the simplicity and capacity of relational systems for the development of OLTP applications; however, this strategy, by itself, has not been enough to face the challenge of dealing with growing amounts of data stored. This work presents a particular approach of the multidimensional data model, with its own rules, restrictions, and operators, where the indicators of the productive processes of organizations, based on representative data obtained from their information systems, have been managed efficiently, privileging the speed of response. In this way, it is possible to analyze production processes or services, as well as to manage the information derived from different types of scientific experiments. By simulating the simplicity of the relational data model, a set of operators that represent the fundamental OLAP operations have been included, which can then be easily implemented in SQL. In addition, the multidimensional designs based on this model can be implemented in a ROLAP context, in any relational database engine. Finally, since the representation of the operators is inspired on the classical relational algebra, it is easy to assimilate, turning this model into a great potential for training specialists in multidimensional modeling. Consequently, the proposed model allows the use of multidimensional databases, in a simple and friendly context.
A software tool developed in Matlab for short-term load forecasting (STLF) is presented. Different forecasting methods such as artificial neural networks, multiple linear regression, curve fitting have been integrated into a stand-alone application with a graphical user interface. Real power consumption data have been used. They have been provided by the branches of the distribution system operator from the Southern-Western part of the Romanian Power System.
When humans are listening to music they perceive beats, rhythms and melodies. This is the basis of music stimuli recognition where the goal is to explore how music influences our brain activity. In previous studies the emotional state determination was based on users' feedback. However this method is unreliable in most cases because an emotion state is not exact and it is changing relatively slowly. In this paper we tried to recognize music-induced electroencephalogram patterns from the well-known Neurosky Mindwave Mobile device's signal with feed forward artificial neural network. The paper describes our self-developed EEG measurement framework and the efficiency of the neural network with different kinds of feature extraction strategies.
