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Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting to eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that this method, using eye movement data, can provide control forgetting prediction with remarkably high accuracy. This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.

The phenomenon of change blindness in the process of aircraft flying is easy to cause pilots to miss the key information prompt, resulting in huge safety risks. In order to ensure flight safety by reducing the occurrence of change blindness, this research adopts the forced detection paradigm and eye tracking technology, combining keystroke accuracy, reaction time, average fixation time, fixation point number to explore stimulus presentation time, knob interface layout complexity, and influence of the position of the knob on change blindness. Therefore, it provides theoretical guidance on how to reduce the incidence of change blindness. The results showed that increasing the time of stimulus presentation could improve the efficiency of change detection and reduce the probability of change blindness. The more concise and reasonable the knob interface layout is, the more beneficial it is in reducing the cognitive load of the subjects and improving the change detection ability of the subjects, thereby reducing the incidence of change blindness. The subjects tended to devote more attention to the central knob, and the identification and processing of information were more comprehensive, meaning the success rate of change detection was higher. It is also proved that there is no interaction between these factors.

In order to improve the signal natural frequency of dynamic vibration absorber and eliminate the influence of nonlinear output force to the adaptive vibration absorption system. A new type of electromagnetic active vibration absorber is designed in this article. The internal magnetic circuit structure is changed through the electromagnet placed on the upper and lower, produce the electromagnetic force in two directions. Reaction force of upper mass is used to eliminate the target vibration. The effective frequency range of the vibration absorption increases, output force is basically linear. Aimed at multiple-point adaptive control strategy, a distributed multi-channel adaptive control algorithm is proposed, in which coupling between channels can be compensated on each control loop. Influence of secondary path on active control is analyzed, put forward the improved least mean square algorithm to identify the secondary. Active vibration control experiment platform is structured to verify the output force of absorber, and engineering application of the distributed multi-channel adaptive control algorithm. The results show that the distributed multi-channel adaptive control algorithm system has about 15 dB noise reduction, effect is obvious; new type of electromagnetic active vibration absorber is not limited to the natural frequency, output force can adaptive keep pace with the excitation frequency.

Based on the research achievements of the Haiyuan 1 oscillating buoy wave power generation device, this paper proposes the innovation about a group array of the buoy to increase the output of power generation and realize large-scale power generation. Firstly, the conversion efficiency of wave energy captured by buoys of arrays 3, 6, and 9 for power generation is theoretically estimated. According to wave theory, the energy conversion efficiency of buoy increases with the increase of array number. Then, the modeling and simulation of the buoy array wave energy generation system are carried out by using AMESim simulation software. Through the simulation and optimization of the parameters of the components in the array buoy storage power generation system, the stability and efficiency of the wave power generation device under different array buoys are studied. The simulation results show that by adjusting the volume of the accumulator, precharge pressure and the displacement of the hydraulic motor, the power generation system can not only realize the continuous power output, but also the power output increases with the increase of the number of accumulators. It is proved that the feasibility and rationality of the designed buoy power generation system in group array module, and the research results can make a reliable technical reference for the construction and implementation of large-scale wave power generation system.

In the cloud computing, in order to provide reliable and continuous service, the need for accurate and timely fault detection is necessary. However, cloud failure data, especially cloud fault feature data acquisition is difficult and the amount of data is too small, with large data training methods to solve a certain degree of difficulty. Therefore, a fault detection method based on depth learning is proposed. An auto-encoder with sparse denoising is used to construct a parallel structure network. It can automatically learn and extract the fault data characteristics and realize fault detection through deep learning. The experiment shows that this method can detect the cloud computing abnormality and determine the fault more effectively and accurately than the traditional method in the case of the small amount of cloud fault feature data.

Background Neck pain is a common discomfort or more intense forms of pain in the cervical region. Neck pain has a large impact on individuals and their families, communities, healthcare systems, and businesses throughout the world. Therapeutic strategies are widely used for patients with neck pain in clinical practice, but the effectiveness of each therapeutic strategy is still unclear. The aim of this study is to assess the efficacy and safety of therapeutic strategies for neck pain. Method Seven electronic databases will be searched regardless of publication date or language. Randomized controlled trials will be included if they recruited participants with neck pain for assessing the effect of each therapy. Primary outcomes will include pain score. The risk of bias will be assessed by 2 authors using the Cochrane tool of risk of bias. Network meta-analysis in random effects model will be conducted to estimate the indirect and mixed effects of therapeutic strategies for neck pain by R-3.5.1 software. The confidence in cumulative evidence will be assessed by grading of recommendations assessment, development, and evaluation. Results This study will be to assess the effect and safety of therapeutic strategies for neck pain. Conclusions This study will assess the effect of different therapeutic strategies for neck pain and provide reliable evidence for the choice of treatments. Systematic review registration PROSPERO (CRD42019102385).