Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterationsThis research received no external funding. Partial funding for open access charge: Universidad de Málag

Review of automated time series forecasting pipelines

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting

Device characteristics-based differentiated energy-efficient adaptive solution for multimedia delivery over heterogeneous wireless networks

Energy efficiency is a key issue of highest importance to mobile wireless device users, as those devices are powered by batteries with limited power capacity. It is of very high interest to provide device differentiated user centric energy efficient multimedia content delivery based on current application type, energy-oriented device features and user preferences. This thesis presents the following research contributions in the area of energy efficient multimedia delivery over heterogeneous wireless networks: 1. ASP: Energy-oriented Application-based System profiling for mobile devices: This profiling provides services to other contributions in this thesis. By monitoring the running applications and the corresponding power demand on the smart mobile device, a device energy model is obtained. The model is used in conjunction with applications’ power signature to provide device energy constraints posed by running applications. 2. AWERA 3. DEAS: A Device characteristics-based differentiated Energy-efficient Adaptive Solution for video delivery over heterogeneous wireless networks. Based on the energy constraint, DEAS performs energy efficient content delivery adaptation for the current application. Unlike the existing solutions, DEAS takes all the applications running on the system into account and better balances QoS and energy efficiency. 4. EDCAM 5. A comprehensive survey on state-of-the-art energy-efficient network protocols and energy-saving network technologies

Soft Morphological Computation

Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17

Parameters estimation of induction machine single-cage and double-cage model using Hybrid Simulated Annealing–Evaporation Rate Water Cycle algorithm

This paper presents the usage of the hybrid simulated annealing—evaporation rate water cycle algorithm (SA-ERWCA) for induction machine equivalent circuit parameter estimation. The proposed algorithm is applied to nameplate data, measured data found in the literature, and data measured experimentally on a laboratory three-phase induction machine operating as an induction motor and as an induction generator. Furthermore, the proposed method is applied to both single-cage and double-cage equivalent circuit models. The accuracy and applicability of the proposed SA-ERWCA are intensively investigated, comparing the machine output characteristics determined by using SA-ERWCA parameters with corresponding characteristics obtained by using parameters determined using known methods from the literature. Also, the comparison of the SA-ERWCA with classic ERWCA and other algorithms used in the literature for induction machine parameter estimation is presented. The obtained results show that the proposed algorithm is a very effective and accurate method for induction machine parameter estimation. Furthermore, it is shown that the SA-ERWCA has the best convergence characteristics compared to other algorithms for induction machine parameter estimation in the literature

Spannungsstabilitätsbewertung und –regelung elektrischer Netze mit Computational Intelligence

The primary objective of this dissertation is the utilization of an integrated and effective framework for voltage stability assessment and control based on computational intelligence techniques. A method based on artificial neural network (ANN) was developed to estimate the voltage stability margin (VSM) of a power system in real time and used for initiating appropriate control actions. The developed ANN method should provide accurate estimation for any system condition. A new method for generating training samples for ANN was proposed in this dissertation in order to take correlation of loads at different locations and variation of control settings into consideration. The next focus of this thesis is the development of a black-box optimization algorithm requiring minimum human intervention. The algorithm has to be capable of handling practical engineering optimization problems with complex cost characteristics, mixed-integer variables and a large number of constraints. An adaptive differential evolution namely JADE is extended in this thesis to consider variation of the population size namely JADE-vPS. The algorithm is featured by a parameter-free penalty approach to handle constraints. The results of benchmark problems for unconstrained optimization are very encouraging. For a voltage stability constrained optimal power flow problem, JADE-vPS outperforms the other counterparts in terms of robustness and quality of the solution. The final investigation is emphasized on fitness approximation for computationally expensive optimization problems. For some engineering problems, the system states corresponding to a given set of inputs are determined by a time-consuming procedure, such as numerical integration methods. In evolutionary computation, this calculation must be repeated for a huge number of times. This makes the entire process sluggish and might be infeasible for real-time implementation. In this thesis, a few models that use ANN to approximate VSM during the optimization course for determining the optimal control variables of voltage stability constrained optimal reactive power dispatch problems.Das Ziel dieser Dissertation ist die Entwicklung einer integrierten und effektiven Umgebung für die Stabilitätsbewertung und -regelung mit Hilfe von Computational Intelligence. Eine auf künstliche neuronale Netze (KNN) basierende Methode wurde entwickelt, zum Bewerten der Spannungsstabilitätsgrenze in Echtzeit. Diese wird verwendet um angemessen zu regeln. Diese KNN Methode liefert zuverlässige Abschätzungen für beliebige Netzzustände. Eine neue Methode zum Erzeugen von Trainingssets wurde in dieser Arbeit entwickelt, um die KNN zu trainieren. In dieser wurden die Zusammenhänge von unterschiedlichen Lastzuständen und verschiedenen Reglereinstellungen berücksichtigt. Ein weiterer Schwerpunkt dieser Arbeit ist die Entwicklung eines Black-Box Optimierungsalgorithmus mit möglichst geringer Benutzerkommunikation. Dieser Algorithmus soll in der Lage sein, praktische Optimierungen durchzuführen, mit komplexen Kostenstrukturen, gemischt ganzzahligen Variablen und einer großen Zahl von Randbedingung. Dazu wurde JADE, eine adaptive differential evolution Technik, erweitert zu JADE-vPS, um unterschiedlich große Populationen behandeln zu können. Dieser Algorithmus ist in der Lage nur mit den Randbedingungen eine Aufgabe zu optimieren, ohne weitere Benutzereingaben. Die Ergebnisse für Benchmarkfunktionen ohne Randbedingungen sind sehr erfolgversprechend. Für die Spannungsstabilität, beschrieben durch den optimalen Lastfluss, sind die Ergebnisse von JADE-vPS besser als von anderen betrachteten Algorithmen, in Bezug auf Robustheit und Qualität der Ergebnisse. Die abschließenden Untersuchungen fokussieren auf die Abschätzung der Fitness in rechenintensiven Optimierungsproblemen. Für einige Problemstellungen wird der Zusammenhang von Eingangsvariablen und Systemzustand durch eine zeitintensive Prozedure bestimmt, wie z.B. numerische Integration. In Evolutionstechniken müssen diese Berechnungen sehr häufig wiederholt werden. Daraus resultiert ein sehr langsamer Prozess, ungeeignet für Echtzeitanwendung. In dieser Arbeit werden einige Modelle vorgestellt die KNN verwenden um die Stabilitätsgrenze anzunähern, während des Optimierens der Regelungsvariablen unter Verwendung von Blindleistung als Randbedingung

Audio/Video Transmission over IEEE 802.11e Networks: Retry Limit Adaptation and Distortion Estimation

The objective of this thesis focuses on the audio and video transmission over wireless networks adopting the family of the IEEE 802.11x standards. In particular, this thesis discusses about the resolution of four issues: the adaptive retransmission, the comparison of video quality indexes for retry limit adaptation purposes, the estimation of the distortion and the joint adaptation of the maximum number of retransmissions of voice and video flows

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

A programmer's environment for music analysis

A model for representing music scores in a form suitable for general processing by a music-analyst-programmer is proposed and implemented. Typical input to the model consists of one or more pieces of music which are encoded in a file-based score representation. File-based representations are in a form unsuited for general processing, as they do not provide a suitable level of abstraction for a programmer-analyst. Instead, a representation is created giving a programmer's view of the score. This frees the analyst-programmer from implementation details, that otherwise would form a substantial barrier to progress. The score representation uses an object-oriented approach to create a natural and robust software environment for the musicologist. The system is used to explore ways in which it could benefit musicologists. Methodologies for analysing music corpora are presented in a series of analytic examples which illustrate some of the potential of this model. Proving hypotheses or performing analysis on corpora involves the construction of algorithms. Some unique aspects of using this score model for corpus-based musicology are: - Algorithms impose a discipline which arises from the necessity for formalism. - Automatic analysis enables musicologists to complete tasks that otherwise would be infeasible because of limitations of their energy, attentiveness, accuracy and time