Formation Control of Multiple UUVs Based on GRU-KF with Communication Packet Loss

This paper proposes the application of artificial intelligence to forecast the generation capacity of wind power plants by processing data through noise reduction and filtering. It subsequently employs Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for training, testing, and evaluation. Processing the initial data will help minimize noise and reduce the data space. The study focuses on preprocessing methods and selecting the appropriate neural network between LSTM and GRU. The initial data processing will assess the similarity through the Spearman rank correlation coefficient. The data used in the paper is taken from local wind turbines. The processed data will be input into the neural network for evaluation based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Percent Error (PE), and training time. The entire network simulation and evaluation process is performed using MATLAB software. The simulation results show the feasibility and suitability of the GRU network model combined with noise filtering methods, bringing high accuracy and less training time compared to the LSTM network. Specifically, the analysis contrasting the GRU network with the optimized dataset and the LSTM with the unprocessed dataset is more effective than a difference in RMSE of 15.592 and MAPE of 611.047%. Moreover, the time difference between the GRU and LSTM networks with the same dataset has a much earlier time difference from 6 to 28 seconds.

Efficient and accurate personalized recommendation algorithms can effectively improve user experience satisfaction, in order to improve the performance of user personalized recommendation algorithm, this study proposes a user personalized recommendation algorithm based on deep learning network. The algorithm uses Gate Recurrent Unit (GRU) network to build the main model of personalized recommendation algorithm to reduce the influence of over fitting of multi-layer network; The attention mechanism is introduced into GRU network, so that the recommendation model can obtain the feature information of user data more accurately and reduce the influence of irrelevant data on the model; At the same time, due to the introduction of variable length mini-batch allocation method, the model training data is more complete and reliable, which can effectively improve the accuracy of user personalized recommendation. The simulation experiment is based on Amazon dataset and MovieLens dataset. The experimental results show that the proposed method has good personalized recommendation ability.

A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

Considering the development of deep learning and the emergence of intelligent control demands in nuclear reactors, along with the presence of plant‐level real‐time information monitoring systems in most nuclear power plants, there is a considerable accumulation of sensor measurements from long‐term operation. This makes it feasible to conduct medium to long‐term predictions for various real‐time conditions in nuclear power plants. Therefore, this paper proposes the utilization of a gate‐based recurrent neural network called GRU (Gated Recurrent Unit) and its variants for parameter prediction of LOCA (Loss of Coolant Accident) scenarios. The main content of this paper consists of two parts: (1) Experimental verification is conducted to demonstrate that GRU has excellent capability in capturing long‐term sequential information and generalization ability, making it suitable for predicting accident conditions in nuclear power plants. Two accident trend prediction methods based on the GRU network are proposed for scenarios with limited data. The results show that these methods can effectively provide short‐term development trends for accident conditions. Additionally, by considering the feature extraction capacity of CNN, the fusion of CNN and GRU models is employed for parameter prediction under different sizes of broken area. The results indicate an improvement in the model's generalization ability. (2) In scenarios with limited and incomplete data, a more robust variant of GRU called GRU‐D model is utilized for both univariate and multivariate synchronous prediction of accident conditions with different missing values. Experimental results demonstrate that even with a data missing rate of 90%, the GRU‐D network exhibits excellent predictive accuracy and generalization ability in parameter prediction for the given conditions.

Based on the application of important water area blockade, a new discontinuous guidance approach of an unmanned underwater vehicle (UUV) perpendicularly towards side of surface ship is proposed, and a hardware-in-the-loop simulation (HILS) system is established to demonstrate this guidance approach. This approach can be described as follows: a near-surface straight trajectory which is perpendicular to the predicted ship trajectory is figured out by detection device of UUV which is hidden underwater after the ship target is located. When the horizontal distance between UUV and the ship target meets start-up condition, UUV climbs to a certain depth near the sea surface and then runs along the horizontal straight trajectory. During its motion, UUV periodically measures the sight line between the UUV and ship target. In order to restrain the horizontal rotation of the sight line, a discontinuous guidance approach is proposed by using the deviation of the two adjacent sight lines and the distance between the UUV and the ship target to adjust the UUV’s velocity. After certain times of adjustment of the UUV’s velocity, UUV impacts the side of ship target perpendicularly with required accuracy. This near-surface discontinuous guidance approach makes it possible for UUV to be guided through optical or radar detection. Further discussions are made including miss distance caused by this proposed guidance approach, cycle of sight line detection, UUV’s non-attack zone and UUV start-up condition. Finally, a set of HILS is carried out to demonstrate the performance of the discontinuous guidance approach on an experimentally validated UUV model.

Unmanned underwater vehicles (ROV/AUV) are autonomous or remotely controlled robotic systems that can move underwater at any desired angle. Unmanned underwater vehicles; It is used in areas such as underwater image taking, ship maintenance and repair, coast guard, examination of shipwrecks, underwater cleaning. In this study, the software design of the balance control of underwater vehicles was carried out using the PID algorithm. For the PID algorithm trial, a two-motor test setup with an IMU sensor was prepared. After the data from the sensor were recorded in MATLAB using the Kalman filter, the transfer function of the system was obtained using the System Identification Toolbox. With the obtained transfer function, the stable operation of the system is provided in real time. As a result of the research on software and hardware integration, microcontroller ARM-based STM32 was used.

The estimation of heat release rate (HRR) in a building fire is a meaningful yet challenging task to improve first emergency response. Inspired by the capability of deep learning method that mines patterns from raw data, the inverse model based on Gated Recurrent Unit (GRU) is presented for the inversion of HRR. First, a series of fire scenarios is simulated to form the dataset by forward fire modeling. Second, GRU is applied to learn representations from windows of time-series sensor data. Output layer is used to map the learned representations to targets. Third, the HRR is estimated by the trained GRU network with observed data from the fire. Finally, the accuracy and efficiency of the inverse model are evaluated in a multi-compartment configuration. Preliminary results indicate that GRU network can be applied to the inversion of fire HRR with higher accuracy and efficiency.

Dissolved gases content in power transformer oil can provide important information for transformer condition assessment. The recurrent neural network (RNN)has always performed well in the data labeled in the form of sequence but it has long term dependency and vanishing gradient problem. Gated Recurrent Unit (GRU) is an improved version of RNN which deal with problems in RNN. This paper proposed a new forecasting model based on GRU network. The studies show that the model can effectively predict the change state of oil gas in power transformer. Compared with the Long Short Term Memory (LSTM) networks which is also improved recurrent neural network, the accuracy rate is comparable, and the training time is shorter and the universality is better. When the interval and fluctuation of data are greater, the prediction of the GRU network is also more stable.

Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This paper proposes a hybrid modeling method using sequence prediction for this classic and important task. The proposed method can seamlessly integrate top-down, bottom-up and sequential information hidden in multi-year data. Two advanced sequence prediction models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are investigated in this paper. They successfully solve the vanishing and exploding gradient problems a standard recurrent neural network has. This paper firstly explains the theories of LSTM and GRU networks and then discusses the steps of feature selection, feature engineering and model implementation in detail. In the end, a real-world application example for a large urban grid in West Canada is provided. LSTM and GRU networks under different sequential configurations and traditional models including bottom-up, ARIMA and feed-forward neural network are all implemented and compared in detail. The proposed method demonstrates superior performance and great practicality.

Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This paper proposes a hybrid modeling method using sequence prediction for this classic and important task. The proposed method can seamlessly integrate top-down, bottom-up and sequential information hidden in multi-year data. Two advanced sequence prediction models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are investigated in this paper. They successfully solve the vanishing and exploding gradient problems a standard recurrent neural network has. This paper firstly explains the theories of LSTM and GRU networks and then discusses the steps of feature selection, feature engineering and model implementation in detail. In the end, a real-world application example for a large urban grid in West Canada is provided. LSTM and GRU networks under different sequential configurations and traditional models including bottom-up, ARIMA and feed-forward neural network are all implemented and compared in detail. The proposed method demonstrates superior performance and great practicality.

Speech is easily interfered by external environment in reality, which results in the loss of important features. Deep learning has become a popular speech enhancement method because of its superior potential in solving nonlinear mapping problems for complex features. However, the deficiency of traditional deep learning methods is the weak learning capability of important information from previous time steps and long-term event dependencies between the time-series data. To overcome this problem, we propose a novel speech enhancement method based on the fused features of deep neural networks (DNNs) and gated recurrent unit (GRU). The proposed method uses GRU to reduce the number of parameters of DNNs and acquire the context information of the speech, which improves the enhanced speech quality and intelligibility. Firstly, DNN with multiple hidden layers is used to learn the mapping relationship between the logarithmic power spectrum (LPS) features of noisy speech and clean speech. Secondly, the LPS feature of the deep neural network is fused with the noisy speech as the input of GRU network to compensate the missing context information. Finally, GRU network is performed to learn the mapping relationship between LPS features and log power spectrum features of clean speech spectrum. The proposed model is experimentally compared with traditional speech enhancement models, including DNN, CNN, LSTM and GRU. Experimental results demonstrate that the PESQ, SSNR and STOI of the proposed algorithm are improved by 30.72%, 39.84% and 5.53%, respectively, compared with the noise signal under the condition of matched noise. Under the condition of unmatched noise, the PESQ and STOI of the algorithm are improved by 23.8% and 37.36%, respectively. The advantage of the proposed method is that it uses the key information of features to suppress noise in both matched and unmatched noise cases and the proposed method outperforms other common methods in speech enhancement.

The S-wave velocity ([Formula: see text]) is a fundamental parameter in geophysical analysis, prestack seismic inversion, and reservoir prediction. For various reasons, the availability of directly measured values of [Formula: see text] is low, especially in old wells. Therefore, indirect estimations of [Formula: see text] data on the basis of available reservoir information are important, and the development of a high-efficiency and low-cost prediction method is necessary. We have developed a novel prediction method that combines the convolutional neural network (CNN) and the gated recurrent unit (GRU) algorithms, based on a deep convolutional GRU (DCGRU) approach. More specifically, a CNN structure is used to identify and memorize the complex relationship between [Formula: see text] and well-log data, whereas a GRU network is introduced to extract key features of the data series in the depth direction. Owing to its structure, the DCGRU approach can seamlessly account for data trends with depth, local correlations across data series, and the actual depth accumulation effect. This approach is tested on data sets from an actual reservoir; it provides more reliable and accurate [Formula: see text] predictions not only compared with empirical models but also compared with the CNN and GRU algorithms applied separately. Our approach has potential for accurately estimating [Formula: see text] from log data.

Raw materials consumption and many physical and chemical reactions in the industrial process are not conducive to analyze the production process. Therefore, the ethylene production prediction model based on gated recurrent unit (GRU) is brought forth in this paper, which can be used to accurately predict ethylene production capacity in complex industrial processes. Using the total consumption of raw materials, fuel, water, steam and electricity as the input of the network architecture and the ethylene production as the output, GRU network is set as the kernel to build the production prediction model. Form experiment results comparing with the back propagation (BP) neural network and the long short-term memory (LSTM), GRU can better predict ethylene production, thus reducing production energy consumption and improving production efficiency.

This paper demonstrates the design and development of a novel system which comprises of a command boat and Unmanned Underwater Vehicle (UUV) with which, one is able to extend the range of operation of the Remotely Operated underwater Vehicle (ROV) which is limited by the length of tether. The UUV proposed in our system can be utilized along with features and functionality of existing designs, along with an added advantage of incorporating additional accessories depending upon the application. The system comprises of three components: a control unit, command boat and an UUV. The control unit is situated in any physical planar surface. The operator controls the command boat–UUV system wirelessly, which are linked using a tether. The UUV can assist in exploration, research, search and recovery etc. In the case of search and recovery, the command boat is guided to the required location, then the system identifies the retrievable using camera and appropriate sensors, then the UUV is deployed which is further guided towards the retrievable, and a pair of robotic arms with tangible gripper recovers the retrievable (weighing atmost 2kg). The UUV and the retrievable is docked to the command boat and the system is acquired, hence completing the operation.