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Simultaneous use of an efficient routing protocol and mobile sink not only prevents rapid sensor energy depletion but also effectively improves the energy balance in wireless sensor networks. In this paper, a new data aggregation method is proposed called "Ant colony-based Routing and Petal-shaped Data Aggregation (ARPDA)", which includes clustering, Cluster Head (CH) selection, intra-cluster routing, determining polling points, and designing the sink node trajectory. In the proposed method, the network is divided by several virtual concentric circles that are equidistant from each other. The intersection point of these circles with the virtual lines passing through the origin of the network determines the probable polling points for the sink node. First, by efficiently clustering the sensor nodes and forming an intra-cluster routing tree based on the ant colony optimization algorithm, the sensor data is aggregated in CHs; then, by selecting the appropriate polling points and designing a petal-shaped path, the sink node periodically collects the aggregated data in CHs based on two linear and circular movements. Considering the sink movement in the CH selection process is another advantage of the proposed algorithm. Numerical results confirm the better performance of the proposed algorithm compared to the EDT algorithm. [ABSTRACT FROM AUTHOR]

In this paper, using a deep learning technique, the sensing characteristic of a refractive index (RI) sensor based on a multimode optical fiber speckle-gram has been extracted. The investigated sensor structure consists of a tapered optical fiber or microfiber (MF) induced on a standard multimode optical fiber (MMF). Due to the presence of evanescent waves around the MF, the guiding status of the end MMF is dependent on the external RI around the taper. Thereby, the propagating modes and resulting interference pattern or speckle-gram become sensitive to the changes in the taper environment. Herein, by analyzing and quantifying the speckle-gram modulation, the RI changes around the taper are characterized. To extract the sensing characteristic and improve its measurement accuracy, deep learning algorithms and specifically the ResNet deep neural network are used in the processing of the output speckle-grams. Using this algorithm, while the tapered fiber is immersed in different refractive index solutions in the range of 1.33 to 1.368, a set of output speckle images is captured for learning and then a number of them are used for measurement, and finally the accuracy of detecting the refractive index of the solutions RI has been achieved up to 95%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel state estimation plan using Adaptive Neuro-Fuzzy Inference System (ANFIS). To increase the speed and accuracy of estimation, an individual ANFIS is used for each bus. To improve the accuracy of training, the grasshopper optimization algorithm (GOA) is used for the training and optimization of ANFIS parameters. The main advantage of GOA training of ANFIS parameters is the increase in speed and accuracy in estimating power system state variables. One of the main features of the proposed design is its ability to provide an appropriate state estimation when incomplete data is sent to the control center due to the disconnection of communication or the failure of the measurement devices. The recovery of the missed data is implemented by the Group Method of Data Handling (GMDH) neural network. The GMDH neural network is widely used due to its proper speed for function estimation and approximation. Incomplete information obtained from measurements to estimate the state is processed by the GMDH neural network to recover lost information. The output of this neural network, which is the retrieval of complete measurement information, is given to ANFIS to estimate the state of the power system as input. [ABSTRACT FROM AUTHOR]

According to statistics, over half a billion vehicles are moving on the roads worldwide. All vehicles have a license plate as their primary identifier, which is one of the most suitable tools for vehicle identification. Automatic License Plate Recognition (ALPR) can be effective in improving road security, reducing traffic congestion, enhancing transportation efficiency, preventing car theft, and more. Traditional methods proposed for license plate detection mainly relied on manual feature extraction and lacked could not be generalized to variable image components in different conditions. With recent advancements in deep learning, algorithms have emerged that can automatically extract high-level representations of images in addition to learning complex image structures. Therefore, in this paper, the high capacity of deep neural networks is utilized for learning license plate identifiers. The proposed model consists of two stages: license plate localization and plate identification. For localization, a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) is used in an encoder-decoder network. The proposed model is evaluated on two datasets, FZU Cars and Stanford Cars, and based on the experimental results, it outperforms baseline methods in terms of accuracy on both datasets. [ABSTRACT FROM AUTHOR]

This paper presents an online scheme of a wide-area-damping-controller (WADC) for controlling the inter-area oscillatory modes in interconnected power systems using wide-area power system stabilizers and measuring signals. The proposed adaptive controller includes two individual approaches WSSS for evaluating the signal participation factor selection and GPSS as a global power system stabilizer for damping inter-area oscillations. For this purpose, considering the concept of center of inertia over inter-area signals, generators with the highest interactions are determined as oscillating areas while based on evaluating signal damping ratio, proper participation factors are estimated in which GPSS with positive damping performance is developed. In real-time working mode, based on oscillatory signals gathered from WAMS data, different combinations of PSSs are evaluated in which the combinations with the best controllability on damping the oscillations are selected. The effectiveness of the proposed widearea damping controller scheme is evaluated on the IEEE test system and practical Iran national power grid with the potential of unstable inter-area oscillations. Results show the effectiveness of the proposed WADC approach for fast and secure damping of inter-area oscillation. [ABSTRACT FROM AUTHOR]