Within the last few years, the increase of the world's energy consumption has substantially impacted the environment. Solar energy initiative is more than ever involved to tackle this issue, especially when deploying PV (photovoltaic) systems over large-scale residential areas. However, not all surfaces in these areas are economically suitable, while some surfaces have low CO2 mitigation. With the availability of high-resolution remote sensing data, the estimation of suitable rooftops for PV systems installation can be performed automatically by estimating the PV potential. This paper presents a novel method for estimating NPV (net present value) of the potential PV systems installed on rooftops by using LiDAR (Light Detection And Ranging) data and PV systems' nonlinear efficiency characteristics. More importantly, the environmental impact is estimated for each rooftop through EPBT (energy payback time) and GGER (greenhouse gas emission rate), based on the life-cycle of a specific PV system. This is combined with NPV in order to find rooftops that are both economically and environmentally viable candidates for PV systems deployment. Results demonstrate a case study LiDAR data for predicting each building's economical and environmental impact, as well as providing an overall view of resulting cumulative CO2 mitigation over large residential area.
COBISS.SI-ID: 19549718
Meta-heuristic algorithms should be compared using the best parameter values for all the involved algorithms. However, this is often unrealised despite the existence of several parameter tuning approaches. In order to further popularise tuning, this paper introduces a new tuning method CRS-Tuning that is based on meta-evolution and our novel method for comparing and ranking evolutionary algorithms Chess Rating System for Evolutionary Algorithms (CRS4EAs). The utility or performance a parameter configuration achieves in comparison with other configurations is based on its rating, rating deviation, and rating interval. During each iteration significantly worse configurations are removed and new configurations are formed through crossover and mutation. The proposed tuning method was empirically compared to two well-known tuning methods F-Race and Revac. Each of the presented methods has its own features as well as advantages and disadvantages. The configurations found by CRS-Tuning were comparable to those found by F-Race and Revac, and although they were not always significantly different regarding the null-hypothesis statistical testing, CRS-Tuning displayed many useful advantages. When configurations are similar in performance, it tunes parameters faster than F-Race and there are no limitations in tuning categorical parameters for which Revac is not a suitable.
COBISS.SI-ID: 19750166
Targeted muscle reinnervation (TMR) is a surgical procedure, used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. However, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study, we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual trains of motor unit action potentials. The MUAP surface area, normalized to the electrode grid surface (0.25 ± 0.17 and 0.81 ± 0.46, p ( 0.001) and the MUAP duration (10.92 ± 3.89 ms and 14.03 ± 3.91 ms, p ( 0.01) were significantly smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 ± 0.11 mV and 0.14 ± 0.06 mV, p = 0.07) was not significantly different between the two groups. We also observed that MUAP surface representations largely overlap in TMR patients. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas. Surface EMG decomposition resolves these problems and could, thus, lead to improved prosthetic control.
COBISS.SI-ID: 19367702
In the computation intelligence domain, we connected evolutionary algorithms with artificial neural networks. Thus, the artificial neural network was applied as a local search heuristic to ensemble strategies in differential evolution. Indeed, the strategy of exploring solutions in the search space is affected in ensemble strategies. The results of experiments on CEC-14 benchmark function suite showed that this hybridization additionally improves the performances of the mentioned algorithm.
COBISS.SI-ID: 19305750
Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold’s selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the Cuckoo Search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two adjusted parameters, it is by far a better choice for real-life applications.
COBISS.SI-ID: 19733526