The Vistula’s riverbed is a treasure-trove of relics concealed by continually shifting sands and by the turbid river water. So what lies down there, hidden in the Vistula’s depths?
Following flood events and cloudbursts alternating with long drought periods, interest grew in the reservoirs, lakes and water basins in the Tuscany region. In-depth studies are needed to understand the role of water bodies in territorial resili-ence to climate change. Water volume is the main information to be collected to quantify and monitor their capacity. In this study, a methodology was developed for the estimation of water volume, based on depth measurements taken by sensors with low detection time and costs that can quantify the resource on a regional scale. The depth measuring instrument was a portable sounder with 95 satellite positioning system (Deeper Smart Sonar PRO + (WI-FI + GPS). 204 water bodies were measured. The results indicate that depth is a fundamental parameter to be detected in the field, to obtain the volume with automatic and precise tools. The calculated volume correlates well with the real volume with an R2 = 0.94. Elaboration of the results led to a model being developed to estimate the volume, knowing only the lake surface area. The database created can be used to conduct future studies on the dynamics of water resources in relation to climate change. It will also be possi-ble to make comparisons with data obtained from satellite and LiDAR (light detection and ranging) surveys.
The paper presents and discusses a method of azimuth determination of ultrasonic echo arrival in air. The basis of the presented approach is the assumption that the received signal is a narrowband one. In this way, the direction of the signal arrival can be determined based on its phase shift using two receivers. When the distance between the receivers exceeds half of the wavelength of the received signal, a problem of ambiguity in determining the angle of arrival arises. To solve this, a method using multiple pairs of receivers was used. Its robustness and temperature dependence is analysed. The most important advantages of the presented approach are simplified computations and low hardware requirements. Experimental data made it possible to show that for strong echoes, the accuracy is higher than 0.5X. In the case of weak echos, it is reduced to about 2X. Because the method is based on phase shift measurement, the ultrasonic sonar that uses this method can be compact in size. Moreover, owing to the theoretical analysis, certain properties of the mutual location of the receivers were found and formally proved. They are crucial for determining proper receivers’ inter-distances.
Radial basis function neural networks (RBF NNs) are one of the most useful tools in the classification of the sonar targets. Despite many abilities of RBF NNs, low accuracy in classification, entrapment in local minima, and slow convergence rate are disadvantages of these networks. In order to overcome these issues, the sine-cosine algorithm (SCA) has been used to train RBF NNs in this work. To evaluate the designed classifier, two benchmark underwater sonar classification problems were used. Also, an experimental underwater target classification was developed to practically evaluate the merits of the RBFbased classifier in dealing with high-dimensional real world problems. In order to have a comprehensive evaluation, the classifier is compared with the gradient descent (GD), gravitational search algorithm (GSA), genetic algorithm (GA), and Kalman filter (KF) algorithms in terms of entrapment in local minima, the accuracy of the classification, and the convergence rate. The results show that the proposed classifier provides a better performance than other compared classifiers as it classifies the sonar datasets 2.72% better than the best benchmark classifier, on average.
In this study, the effect of the emergence angle of a source array on acoustic transmission in a typical shallow sea is simulated and analyzed. The formula we derived for the received signal based on the Normal Mode indicates that the signal is determined by the beamform on the modes of all sources and the samplings of all modes at the receiving depth. Two characteristics of the optimal emergence angle (OEA) are obtained and explained utilizing the aforementioned derived formula. The observed distributions of transmission loss (TL) for different sources and receivers are consistent with the obtained characteristics. The results of this study are valuable for the development and design of active sonar detection.
The study aimed to apply the protection from damage to engineering facilities located near a planned underwater aggregate extraction. The analysis was conducted in compliance with mining regulations and expert opinions. The study also aimed to assess the precision and correctness of the extraction, due to economic aspects. To reach the goals, in-situ research of the mining area was conducted, with the help of an advanced bathymetric device, based on the USV methodology. The instrument – named by the author as Smart-Sonar-Boat – was especially designed for underwater surveys in open-pit aggregate mines. The study analyzed the “Dwory” open-pit mine, located in southern Poland in the city of Oświęcim. The bathymetric results obtained contributed to improving the observation of changes in the bottom during the extraction. The applied USV method allowed for conducting the reliable evaluation of the mining work.
In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.