In order to protect our country's territorial waters from infringement and maintain maritime rights and interests, it is necessary to establish a marine defense system that combines both offshore defense and far sea defense. Coast-to-shore/GEO satellite-ship-to-ship bistatic inverse synthetic aperture radar (inverse synthetic aperture radar, ISAR) has all-day and all-weather characteristics, and can realize long-distance continuous tracking, imaging and identification of ship targets in offshore/far sea areas. In addition, these two bistatic ISAR imaging systems also have the advantages of high concealment, strong anti-strike capability, high flexibility, multiple imaging modes, and rich target information, thus improving the reliability of the marine defense system.

With the increasing scale of the Internet medical market, a large number of platforms have started to provide free medical diagnosis services to users. The number of questions on medical Q&A platforms is growing rapidly, and there are a lot of repetitive and similar questions, which can no longer meet the demand of users to get fast and accurate answers only by the manual answers of medical experts. To solve the above problems, this paper proposes a Chinese medical short text matching model based on fine-tuning BERT-Attention-BiLSTM (FBAB), use fine-tuning BERT model for representation of text, the attention mechanism is used to obtain the interaction information between sentences, use the Bi-directional Long Short-Term Memory (BiLSTM) to fuse the contextual information of the 2 sentences., and the feature information is further extracted by maximum pooling and mean pooling to obtain the sentence-level semantic representation, which is input to the prediction layer after stitching, and the softmax classification function is used to calculate the probability that the two sentences are similar. Experiments show that compared with the classical deep short text matching model, the FBAB model proposed in this paper shows better results on the medical short text corpus, which proves the effectiveness and feasibility of the model.

With the booming development of China's ocean shipping and tourism business, the demand for safety and economy when ships are sailing at sea is increasing. Accurate meteorological and hydrographic forecasts can provide meteorological navigation for ships to avoid typhoons and bad weather areas as much as possible and reduce the damage to the ship's hull from wind and waves. To obtain accurate and reliable wind speed prediction results, this paper combines the advantages of convolutional neural network and gated recurrent unit network to form a deep convolutional gated recurrent unit network model (CNN-GRU). For multiple locations, the CNN-GRU algorithm is used to extract the characteristic meteorological elements, and then the convolutional neural network is used to establish the spatial characteristic relationship between each location, and the gated recurrent unit network is used to establish the temporal characteristic relationship between historical time points, and the final wind speed prediction results are obtained based on the spatio-temporal correlation analysis. In this paper, the CNN-GRU model was established using meteorological data from 2019 to 2021, and the prediction results were compared with CNN model and GRU model and the accuracy was verified. The results show that the experimental results obtained by the CNN-GRU model are more accurate and prove the effectiveness of the proposed method.

In response to the increasingly complex navigational conditions and in order to ensure the safety of ship navigation, the improved particle swarm optimisation algorithm is proposed to optimise the weights of the fuzzy assessment method and establish an environmental safety risk evaluation model for ocean-going passenger ships. As the traditional particle swarm algorithm is easy to fall into local optimum, it is proposed to give particle perturbation when the particles are in stable state to jump out of local optimum. The optimized weights of the improved particle swarm algorithm are applied to assess the environmental risk of the ship under the analysis of the factors affecting the environmental safety of the ship's navigation. The assessment scores can greatly approach the expert environmental safety risk assessment scores. The evaluation results demonstrate the superiority of the improved particle swarm algorithm over the traditional particle swarm algorithm. The improved particle swarm algorithm confirms the feasibility of the model and the correction of the weights makes the experimental results more valuable.

According to the characteristics of ship maneuvering motion control, this paper constructs the membership function and control rules of the input and output language variables of the ship course fuzzy neural network controller, and designs a fuzzy controller. Aiming at the deficiencies of the basic fuzzy controller, a two-stage fuzzy controller with fuzzy PI control and self-adjusting parameters is proposed. When it is applied to the ship heading control system, the system has satisfactory results in terms of immunity and response speed. By establishing the description function of the fuzzy controller, the analysis concludes that the ship course fuzzy control system is stable.

With the increasingly severe global energy crisis and environmental pollution, the development of renewable energy has become the key to protecting the environment, saving energy and reducing emissions. As a kind of clean and low-cost renewable energy, wind power has been paid more and more attention by more and more countries. However, the time-varying and intermittent wind speed often leads to instability of wind power and hinders the integration of wind power into the grid. Therefore, effectively predicting the wind spee of wind farms can effectively reduce the cost of wind power grid connection and improve the security and stability of the power grid. In this paper, we propose a wind speed prediction model LSTM-FWA based on long short-term memory (LSTM) network and fireworks algorithm (FWA). The experimental results show that the prediction performance based on LSTM-FWA model is the most obvious. The LSTM-FWA was compared with other prediction models, and the performance of the model under various optimization strategies was comprehensively analyzed. Experimental results show that the proposed spatio-temporal optimized LSTM-FWA model has the best performance for wind speed prediction.

The goal is to develop an adaptive center-of-mass (COM)-based approach for device-less respiratory gating of list-mode positron emission tomography (PET) data. Our method contains two steps. The first is to automatically extract an optimized respiratory motion signal from the list-mode data during acquisition. The respiratory motion signal was calculated by tracking the location of COM within a volume of interest (VOI). The signal prominence (SP) was calculated based on Fourier analysis of the signal. The VOI was adaptively optimized to maximize SP. The second step is to automatically correct signal-flipping effects. The sign of the signal was determined based on the assumption that the average patient spends more time during expiration than inspiration. To validate our methods, thirty-one18F-FDG patient scans were included in this paper. An external device-based signal was used as the gold standard, and the correlation coefficient of the data-driven signal with the device-based signal was measured. Our method successfully extracted respiratory signal from 30 out of 31 datasets. The failure case was due to lack of uptake in the field of view. Moreover, our sign determination method obtained correct results for all scans excluding the failure case. Quantitatively, the proposed signal extraction approach achieved a median correlation of 0.85 with the device-based signal. Gated images using optimized data-driven signal showed improved lesion contrast over static image and were comparable to those using device-based signal. We presented a new data-driven method to automatically extract respiratory motion signal from list-mode PET data by optimizing VOI for COM calculation, as well as determine motion direction from signal asymmetry. Successful application of the proposed method on most clinical datasets and comparison with device-based signal suggests its potential of serving as an alternative to external respiratory monitors.