In this article, we present the physical foundations and the development of a Modelica library with components for modeling 1-D thermofluid systems. Modelica was selected because it is an object-oriented modeling language that facilitates the incremental design of the library. Modelica also allows the modeling of acausal components, where the input/output relation is defined by the boundary conditions under which models are simulated. We model single-substance systems that are macroscopically homogeneous, isotropic, and uncharged, which we call “simple systems.” To model the behavior of these systems, we assume the postulate of classical irreversible thermodynamics. For the graphical representation of complex systems, we utilize a modified bond graph symbology to enhance the readability of the diagrams. We also implement the equations for viscosity and thermal conductivity to complete the IAPWS-95 model of water, enabling its use as a fluid in the piping systems presented in our examples.

First, a procedure is developed for providing a multicriteria model-agnostic Counterfactual Explanation (CE) for classifiers, which is formalized as a constrained multi-objective optimization problem. The proposed multicriteria framework takes into account the proximity between different input vector values by breaking down the influence of each actionable variable. This way, the resulting CE Pareto set allows for a flexible consideration of the relative relevance of such actionable variables. Next, the procedure is extended for the case in which the classifier is not accessible and only a set of labeled data is available. Several architectures are considered to approximate the classifier and a combined post-processing of the resulting approximations of the CE Pareto set is also proposed, that is aimed to two relevant objectives: to improve the approximation of such CE Pareto set and to comparatively assess the performance of the architectures. A simulation example illustrates the theoretical results and the applicability of the proposed schemes.

This paper addresses the training of architectures (e.g., neural networks) formulated in a multi-objective framework where several performance indexes are jointly considered. Based on the possibility of tuning the hyperparameters defined in the learning schemes, a Pareto Front in the performance space is obtained, that provides a whole family of non-dominated architecture configurations. Since the tuning of such hyper-parameters implies solving a costly black box optimization problem, the search procedure is carried out making use of a Bayesian optimization scheme. This multi-objective training approach is directly applicable in classification problems where a performance trade-off among different types of classification errors can be naturally defined. In addition, we develop a general procedure for the multi-objective training of architectures in regression problems; this procedure can be specially useful when the distribution of the data noise is unknown. Some examples illustrate the applicability of the proposed schemes.

The evolution of headways (time intervals between consecutive vehicles) is an important performance indicator of public transportation systems. In this paper, we develop a data processing system that processes the expected arrival time data provided by the corresponding APIs of Madrid and London bus public transport systems with the aim of modeling and supervising their evolution along the time to detect their operational anomalies. By employing only estimates of the time headways between buses, we construct joint statistical modeling along the time of these headways within a stochastic process framework. Then, this model is employed to design a deviation detection procedure that provides a simple and adjustable anomaly detection scheme, as well as a complementary index for assessing the overall Quality of Service (QoS) provided by the operator. The proposed adjustable system can be easily tuned by managers or operators for online anomaly detection, which is an important asset to increase the efficiency of bus transportation services.

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work, we focus on the problem of a biologically inspired multiagent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multiagent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviors and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multiagent system, which have impact on more general collaborative stochastic problems.

VBF is a collection of C++ classes designed for analyzing vector Boolean functions (functions that map a Boolean vector to another Boolean vector) from a cryptographic perspective. This implementation uses the NTL library from Victor Shoup, adding new modules that call NTL functions and complement the existing ones, making it better suited to cryptography. The class representing a vector Boolean function can be initialized by several alternative types of data structures such as Truth Table, Trace Representation, and Algebraic Normal Form (ANF) , among others. The most relevant cryptographic criteria for both block and stream ciphers as well as for hash functions can be evaluated with VBF: it obtains the nonlinearity, linearity distance, algebraic degree, linear structures, and frequency distribution of the absolute values of the Walsh Spectrum or the Autocorrelation Spectrum, among others. In addition, operations such as equality testing, composition, inversion, sum, direct sum, bricklayering (parallel application of vector Boolean functions as employed in Rijndael cipher), and adding coordinate functions of two vector Boolean functions are presented. Finally, three real applications of the library are described: the first one analyzes the KASUMI block cipher, the second one analyzes the Mini-AES cipher, and the third one finds Boolean functions with very high nonlinearity, a key property for robustness against linear attacks.

In this paper, the dynamical behavior of the basic node used for constructing Hebbian artificial neural networks (NNs) is analyzed. Hebbian NNs are employed in communications and signal processing applications, among others. They have been traditionally studied on a continuous-time formulation whose validity is justified via some analytical procedures that presume, among other hypotheses, a specific asymptotic behavior of the learning gain. The main contribution of this paper is the study of a deterministic discrete-time (DDT) formulation that characterizes the average evolution of the node, preserving the discrete-time form of the original network and gathering a more realistic behavior of the learning gain. The new deterministic discrete-time model provides some unstability results (critical for the case of large similar variance signals) which are drastically different to the ones known for the continuous-time formulation. Simulation examples support the presented results, illustrating the practical limitations of the basic Hebbian model.

In this paper, a novel approach for video sequence compression using Cellular Neural Networks (CNN's) is presented. CNN's are nets characterized by local interconnections between neurons (usually called cells), and can be modeled as dynamical systems. From among many different types, a CNN model operating in discrete-time (DT-CNN) has been chosen, its parameters being defined so that they are shared among all the cells in the network. The compression process proposed in this work is based on the possibility of replicating a given video sequence as a trajectory generated by the DT-CNN. In order for the CNN to follow a prescribed trajectory, a supervised training algorithm is implemented. Compression is achieved due to the fact that all the information contained in the sequence can be stored into a small number of parameters and initial conditions once training is stopped. Different improvements upon the basic formulation are analyzed and issues such as feasibility and complexity of the compression problem are also addressed. Finally, some examples with real video sequences illustrate the applicability of the method.