A Review on Image Enhancement and Restoration Techniques for Underwater Optical Imaging Applications

Underwater image processing always a challenging problem in oceanic engineering applications. Images captured in underwater are commonly suffers due to color distortion, detail blur, bluish or greenish tone, and low contrast to light scattering and absorption in the water medium. The image visibility is affected drastically during capturing caused by the degradation of light absorption and scattering effect. Hence, the effective Underwater Image Enhancement(UIE) and restoration techniques are primarily required for the underwater ecological study applications. Various UIE techniques are studied for different data sets, and applications. However, the selection of suitable method for particular applications among available techniques is still a challenging task. In this paper, an overview of recent UIE and restoration techniques and classification methods are elaborated with data sets and applications. The UIE techniques are grouped under various category such as spatial domain, transform domain, color constancy based method, retinex based approach. Similarly, the image restoration techniques are grouped under underwater optical imaging technique, polarization based approach, prior knowledge and convolutional neural networks. Finally, we review the research process of the underwater image enhancement and restoration with the essential background of the water images and recognize challenges

Automatic control and dispatching of charging currents to a charging station for power-assisted bikes

International audienceThis work deals with the automatic dispatching of the charging currents in a charging station for powerassisted bikes (ebike). The decision variables such as arduousness index and urgency are determined. The arduousness index is carried out from the GPS ride data. Urgency is calculated using the parking time and ebike batteries state of charge. They are used to determine ebike's charging priorities at the charging station using continue fuzzy logic. Photovoltaic power forecasting is determined over the control horizon using the artificial neural network. On the one hand, the values of the priority, the photovoltaic power forecasting and the storage battery's state of charge are calculated. They allow to control the states of the switches associated with each charging spot and the operating mode of the storage battery (source or load) using discrete fuzzy logic. On the other hand, the interest of the ride's arduousness for a charging station is presented. A comparative study between the charging method integrating the ride arduousness and not is carried out. A case study of the polytech Annecy campus at the University of Savoie Mont Blanc in France is proposed. Results show that: the arduousness index is essential for controlling the charging priority of ebikes at the charging station; Fuzzy logic allows to manage the current dispatching on a charging station; taking into account the ride's arduousness allows to save up to 413.03 (Wh) of profit and 97.90% energy flexibility on the charging station

Sizing Optimization of a Charging Station Based on the Multi-scale Current Profile and Particle Swarm Optimization: Application to Power-assisted Bikes

International audienceThe development of power-assisted bikes (ebike) is of growing interest because of their economic and environmental advantages. The present work deals with the sizing optimization of a charging station for ebike based on particle swarm optimization. It is based on the consumption profile of ebike batteries, solar and wind power, installation, replacement and maintenance costs of components. In a first step, the consumption profile of the ebike batteries is determined using the second order non-linear electrothermal model. Then, the solar and wind data over one year are used to determine the availability of energy at the implementation site of the charging station. Finally, the cost is defined as an objective function, taking into account the constraints on the number of solar photovoltaic panels, the number of wind turbines, the number of storage batteries and the annual charging demand. The context of a charging station to be implemented in the Polytech Annecy campus in France is studied. The results show that the particle swarm optimization allows a cost reduction of around 56.04% compared to a sizing without optimization

Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal

International audienceThis study proposes a short term forecasting of solar irradiation with multi horizons in the northwest of Senegal. The multilayer artificial neural network (ANN), based on the Levenberg Marquardt algorithm and the meteorological data are used. The latter are measured in real time on the study site. The variables of interest are: mean solar irradiation, maximum temperature and measurement time; they are selected using Weka software. The forecasting horizons are: 0.5 hour, 1 hour, 1.5 hours, 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours and 06 hours. They are proposed with the corresponding statistical criteria. The results show that, the solar energy forecasting can be extended over a six-hour horizon with a correlation coefficient of 0.97 and root mean square error of 0.07. These results will make it possible to complete the forecasting tools in the solar energy sector in Senegal, and help investors to choose the most suitable horizons for energy forecasting in photovoltaic solar power plants