Mathematical Morphology on the Spherical CIELab Quantale with an Application in Color Image Boundary Detection

Mathematical morphology is a theory with applications in image processing and analysis. This paper presents a quantale-based approach to color morphology based on the CIELab color space in spherical coordinates. The novel morphological operations take into account the perceptual difference between color elements by using a distance-based ordering scheme. Furthermore, the novel approach allows for the use of non-flat structuring elements. An illustrative example reveals that non-flat dilations and erosions may preserve more features of a color image than their corresponding flat operations. Furthermore, the novel non-flat morphological operators yielded promising results on experiments concerning the detection of the boundaries of objects on color images.     

Ecological efficiency and thermoeconomic analysis of a cogeneration system at a hospital

This work aims with an approach for cogeneration plants evaluation based on thermoeconomic functional diagram analysis. The second law of thermodynamics is used to develop a methodology to analyse cogeneration systems, based on exergoeconomics evaluation. The thermoeconomic optimisation method developed is applied to allow a better configuration of the cogeneration plant associated to a university hospital. Also ecological efficiency is evaluated. The method was efficient and contributes for thermoeconomics modelling and analysis and can be applied to any sort of thermal system, especially those with combined heat and power in thermal parity.     

Maximizing the set of recurrent states of an MDP subject to convex constraints

This paper focuses on the design of time-homogeneous fully observed Markov decision processes (MDPs), with finite state and action spaces. The main objective is to obtain policies that generate the maximal set of recurrent states, subject to convex constraints on the set of invariant probability mass functions. We propose a design method that relies on a finitely parametrized convex program inspired on principles of entropy maximization. A numerical example is provided to illustrate these ideas.     

Non-saturation Throughput of S-ALOHA Using the Time-Scale Decomposition Technique

S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.     

Part-by-part dimensional error compensation in compliant sheet metal assembly processes

Dimensional variation in assembly processes is one of the most important issues that affect quality. Although robust design and statistical process quality control help to reduce this problem, they cannot be used for instant variation reduction during assembly operations, especially during process ramp-up. This paper introduces a complete methodology for dimensional-related error compensation in compliant sheet metal assembly processes. The proposed methodology is divided into two steps: (1) an off-line error control-learning module using virtual assembly models to determine necessary adjustments; and (2) an in-line control implementation using a feed-forward control strategy based on the learned adjustments. The off-line learning step focuses on determining control actions or corrections to compensate for the negative effects incoming part errors have on Key Product Characteristics. Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine optimal control actions. The in-line feed-forward control identifies appropriate part-by-part adjustments using these learned control actions and incoming assembly component measurements. In this paper, two case studies are presented. First, a mathematical case study presents an empirical proof for the feasibility of the Iterative Sampling and Fitting Algorithm. Second, a simulation-based case study illustrates the effectiveness of the proposed methodology to improve dimensional quality in assembly operations for compliant sheet metal parts.     

Energy storage and control optimization for an electric vehicle

Two big issues involving electric vehicles are energy supply and power management control. To deal with the energy supply problem, this paper proposes the application of a hybrid energy source system, composed of battery pack and ultracapacitor bank. The power management control between the energy supplies was defined by a fuzzy logic with inference rules optimized through genetic algorithm. The genetic algorithm optimizes lower and upper limits of membership functions aiming to reduce the hybrid energy source system total mass while maximizing the electric vehicle drive range and performance. Through the Pareto frontier, we found the best trade‐off solution.     

Determination of alcohols and volatile organic acids in anaerobic bioreactors for H2 production by near infrared spectroscopy

In recent years, near infrared (NIR) spectroscopy has been investigated as a tool for monitoring anaerobic digesters, but several adversities in its application have been reported. This study proposes the application of NIR for the determination of alcohols and volatile organic acids from H₂ production bioreactors and evaluates different approaches to optimize the prediction models. Partial least squad (PLS) models were developed using samples from anaerobic batch reactors fed with crude glycerol for wastewater treatment. The analytes predicted were: methanol, ethanol, 1-butanol, acetic, propanoic, butyric, isocaproic and total volatile organic acids (VFA). The optimization of the predictive capacity of the models was achieved through the orthogonal signal correction (OSC) preprocessing and the selection of variables performed by the genetic algorithm (GA). The application of the proposed models were based on the following figures of merit: accuracy, precision, linearity, limits of detection and quantitation, measurement interval, sensitivity, selectivity, signal-to-noise ratio and bias. Despite the low selectivity (maximum of 0.12%), the models presented high sensitivity [γ^?1 = 0.19 (mg L^?1)^?1], low LOQ (1 mg L^?1) and correlation between reference and predicted values (r) at least 0.93, except for propanoic acid (r_pred = 0.85). The F-test revealed that the selection of variables by GA significantly improved the accuracy and linearity of the prediction models for methanol, acetic acid, isocaproic acid and VFA. NIR spectroscopy has proved to be a powerful tool for monitoring H₂ production bioreactors since provides fast, low cost and multicomponent information.     

Parking at the UC campus: Problems and solutions

This study underscores the importance of adopting integrated parking management policies that ensure not only more rational use of the available parking spaces, evenly balancing supply and demand and bringing in revenues to cover the parking facilities costs, but also the improved attractiveness of alternative transportation modes. Parking supply and demand flows within the UC campus are estimated. The results indicate that the parking facility is underpriced and that there is overcrowding. To reflect critically on these issues and identify research areas to address their socioeconomic implications, a survey regarding the characterization of campus commuters and their travel options is presented. Logistic regression modelling is applied to determine the relative importance of UC campus commuters’ attributes in their level of willingness to pay to have reserved parking on the campus. Finally, some policy proposals are discussed.