Informatica

A relevant challenge introduced by decentralized installations of photo-voltaic systems is the mismatch between green energy production and the load curve for domestic use. We advanced an ICT solution that maximizes the self-consumption by an intelligent scheduling of appliances. The predictive approach is complemented with a reactive one to minimize the short term effects due to prediction errors and to unforeseen loads. Using real measures, we demonstrated that such errors can be compensated modulating the usage of continuously running devices such as fridges and heat-pumps. Linear programming is used to dynamically compute in real-time the optimal control of these devices.

Basic motion structures of crowd aggregation and crowd dispersion are defined and a novel method for identifying these crowd behaviours is proposed. Based on integral optical flow, background and foreground are separated and intensive motion region is obtained. Crowd motion is analysed at pixel-level statistically for each frame to obtain quantity of pixels moving toward or away from each position and their comprehensive motion at each position. Regional motion indicators are computed and regional motion maps are formed to describe motions at region-level. Crowd behaviours are identified by threshold segmentation of regional motion maps.

The new nonlocal delayed feedback controller is used to control the production of drugs in a simple bioreactor. This bioreactor is based on the enzymatic conversion of substrate into the required product. The dynamics of this device is described by a system of two nonstationary nonlinear diffusion-reaction equations. The control loop defines the changes of the substrate concentration delivered into the bioreactor at the external boundary of the bioreactor depending on the difference of measurements of the produced drug delivered into the body and the flux of the drug prescribed by a doctor in accordance with the therapeutic protocol. The system of PDEs is solved by using the finite difference method, the control loop parameters are defined from the analysis of stationary linearized equations. The stability of the algorithm for the inverse boundary condition is investigated. Results of computational experiments are presented and analysed.

This paper introduces how predictor-based control principles are applied to the control of human excitement signal as a response to a 3D face virtual stimuli. A dynamic human 3D face is observed in a virtual reality. We use changing distance-between-eyes in a 3D face as a stimulus – control signal. Human responses to the stimuli are observed using EEG-based signal that characterizes excitement. A parameter identification method for predictive and stable model building with the smallest output prediction error is proposed. A predictor-based control law is synthesized by minimizing a generalized minimum variance control criterion in an admissible domain. An admissible domain is composed of control signal boundaries. Relatively high prediction and control quality of excitement signals is demonstrated by modelling results.

In the discrete form of multi-criteria decision-making (MCDM) problems, we are usually confronted with a decision-matrix formed from the information of some alternatives on some criteria. In this study, a new method is proposed for simultaneous evaluation of criteria and alternatives (SECA) in an MCDM problem. For making this type of evaluation, a multi-objective non-linear programming model is formulated. The model is based on maximization of the overall performance of alternatives with consideration of the variation information of decision-matrix within and between criteria. The standard deviation is used to measure the within-criterion, and the correlation is utilized to consider the between-criterion variation information. By solving the multi-objective model, we can determine the overall performance scores of alternatives and the objective weights of criteria simultaneously. To validate the proposed method, a numerical example is used, and three analyses are made. Firstly, we analyse the objective weights determined by the method, secondly, the stability of the performance scores and ranking results are examined, and finally, the ranking results of the proposed method are compared with those of some existing MCDM methods. The results of the analyses show that the proposed method is efficient to deal with MCDM problems.

This paper studies a set of novel integrated scheduling problems by taking into account the combinatorial features of various groups, parallel-batching, deteriorating jobs, and time-dependent setup time simultaneously under the settings of both single-machine and parallel-machine, and the objective of the studied problems is to minimize the makespan. In order to solve the single-machine scheduling problem, we first investigate the structural properties on jobs sequencing, jobs batching, and batches sequencing for the optimal solution, and then develop a scheduling rule. Moreover, for solving the parallel-machine scheduling problem, we exploit the optimal structural properties and batching rule, and propose a novel hybrid AIS-VNS algorithm incorporating Artificial Immune System algorithm (AIS) and Variable Neighbourhood Search (VNS). Extensive computational experiments are conducted to evaluate the performance of the proposed AIS-VNS algorithm, and comparison results show that the proposed algorithm performs quite well in terms of both efficiency and solution quality.

The probabilistic linguistic terms set (PLTS) can reflect different importance degrees or weights of all possible linguistic terms (LTs) given by the experts for a specific object. The PROMETHEE II method is an important ranking method which can comprise preferences as well as indifferences, and it has a unique characteristic that can provide different types of preference functions. Based on the advantages of the PLTS and the PROMETHEE II method, in this paper, we extend the PROMETHEE II method to process the probabilistic linguistic information (PLI), and propose the PL-PROMETHEE II method with an improved possibility degree formula which can avoid the weaknesses from the original formula. Then concerning the multi-attribute decision making (MADM) problems with totally unknown weight information, the maximum deviation method is used to get the objective weight vector of the attributes, and net flows of the alternatives from the PROMETHEE II method are used to rank the alternatives. Finally, a numerical example is given to illustrate the feasibility of the proposed method.

This paper aims to propose a new distance measure, the interval-valued 2-tuple linguistic induced continuous ordered weighted distance (IT-ICOWD) measure, which consists of the interval-valued 2-tuple linguistic induced continuous ordered weighted averaging (IT-ICOWA) operator and the ordered weighted distance (OWD) measure. In these operators, we consider the risk attitude of decision maker. Furthermore, we discuss some desired properties and various special cases of the IT-ICOWD measure. Additionally, a method of multiple attribute group decision making (MAGDM) in interval-valued 2-tuple linguistic environment is developed on the basis of the IT-ICOWD measure. Through this method, we obtain three simple and exact formulae to determine the order-inducing variables of the IT-ICOWD measure, the weighting vector of decision makers and the weighting vector of attributes, respectively. At last, a numerical example is presented to illustrate the practicability and feasibility of proposed method.

In this paper an exploratory classification, so called open set problem, is investigated. Open set recognition assumes there is incomplete knowledge of the world at training time, and unknown classes can be submitted to an algorithm during testing. For this problem we elaborated a theoretical model, Double Probability Model (DPM), based on likelihoods of a classifier. We developed it with double smoothing solution in order to solve technical difficulties avoiding zero values in the predictions. We applied the GMM based Fisher vector for the mathematical representation of the images and the C-SVC with RBF kernel for the classification. The last contributions of the paper are new goodness indicators for classification in open set problem, the new type of accuracies. The experimental results present that our Double Probability Model helps with classification, the accuracy increases by using our proposed model. We compared our method to a state-of-the-art open set recognition solution and the results showed that DPM outperforms existing techniques.

Intuitionistic uncertain linguistic variables (IULVs) are useful to express the qualitative and quantitative recognitions of decision makers. However, after reviewing the previous operational laws on IULVs, we find there are some limitations. To address these issues, we define several new operations on IULVs and give a new ranking method. To improve the utilization of IULVs, this paper defines two Choquet operators: the intuitionistic uncertain linguistic symmetrical Choquet averaging (IULSCA) operator and the intuitionistic uncertain linguistic symmetrical Choquet geometric mean (IULSCGM) operator, which can address the internal correlations among elements. To globally reflect the interactive characteristics of the importance of elements, two generalized Shapley intuitionistic uncertain linguistic symmetrical Choquet operators are presented. Subsequently, a new distance measure is defined, which is then used to build models to ascertain fuzzy measures on decision maker and criteria sets to address the case where the weighting information is partly known. After that, a new procedure to intuitionistic uncertain linguistic group decision making is developed. Finally, a specific example is offered to illustrate the practicality of the new procedure, and the comparison analysis is also made.