In recent years, with the gradual development of the aviation industry, how to make supersonic aircraft truly used in military air defense has become a global problem. In this paper, the wing design of supersonic aircraft is studied, a swept back symmetrical wing suitable for supersonic reconnaissance aircraft is designed with reasonable aspect ratio, span length, sweep angle, thrust weight ratio and wing load. On the basis of the aerodynamic characteristics of the aircraft analyzed, the inboard electron, outboard electron, flasheron, leading edge flap and all moving tip are designed to ensure the controllability in the flight line and to maintain the balance and stability of the aircraft. The qualitative analysis of the flight performance of the wing is also performed. This paper provides a possibility for the design of supersonic aircraft in the future.

With the development and change of big data related technologies, more and more large amounts of data need to be analyzed. Now there are companies like Google, Yahoo, etc. Frameworks such as MapReduce, Hadoop, Spark, etc. are developed for processing large amounts of data. In this paper, relevant discussions and researches are carried out on time series forecasting under the new era of big data. Now there are time series forecasting methods based on map reduce, Hadoop, spark data processing framework, including nearest neighbor distribution method, neural network method, etc., which have made quite good achievements in big data time series forecasting. By reading the relevant research literature, it is universally acknowledged that the Spark’s framework has good application prospects and potential in predicting big data time series. As a result, this paper is mainly aimed at the optimization and improvement of the big data time series forecasting method on the basis of the spark framework. The author noticed that most of the default configurations of spark clusters are generated by default or automatically, rather than the optimal solution obtained after algorithm optimization, so there is still room for improvement in this regard. In this regard, this paper proposes a kernel method for visual data processing of related configurations and parameters, and then optimizes the default data configuration as much as possible to improve the accuracy and feasibility of the big data time series prediction method on the basis of the spark framework. In this paper, the optimized scheme is used to forecast the domestic electricity consumption in the past five years, and the results show that the optimized scheme has a good improvement performance on the basis of the original method.

This paper analyses the application of Automated Guided Vehicle (AGV) in ports and evaluates the 20 ports' efficiency with the consideration of AGV application. Using the data envelopment analysis (DEA) method and the simulated data, this paper further identifies the optimal number of AGVs for these 20 ports. The DEA-CCR and DEA-BCC results show that most of these 20 ports are already efficient. And Ningbo port, Osaka port, and Hamburg port are the most efficient port as they obtained the highest scores in super-efficiency analysis. But using the simulation data, the result suggests that some ports, such as Busan Port and Antwerp Port, are able to improve their efficiency by adjusting the application of AGV. The finding of this paper suggests that existing automated port can improve their economic efficiency by reducing the number of AGVs, or enhancing their service efficiency by increasing the application of AGVs. But for traditional ports, expanding the application of AGV can increase both port economic and service efficiency.

This article elaborates on molecular dynamics simulation, which is a technology that supports us in analyzing the structure and dynamics of materials and their properties at a microscopic level. In this article, the molecular dynamics simulation develops on Lennard-Jones potential. The simulation is based on the fundamental understanding between Argon-Argon molecules. The application for running molecular dynamics simulation is Moldy. The Argon particles’ size is changed by altering van der Waals radius from Lennard-Jones potential parameters, and the mixture of Argon and different sizes’ particles will be simulated by Moldy. The average potential energy is collected and compared. It discusses how the size of particles affect the average potential energy. Moreover, the trend of potential energy respects to timesteps for each molecule is depicted and compared graphicly by Gnuplot. It discusses how the potential energy changes according to timesteps for different sizes’ particles. In the end, the result is collected and summarized. It discovers that potential energy increases slowly when particles’ sizes increase, and the potential energy decreases significantly when particles’ sizes decrease. In addition, it founds out that molecules with different sizes have a noticeable change initially. Oddly, there is no observation of significant changes in the potential energy of the original molecule. Moreover, the initial decrease for molecules that increase in size is not as significant as that for molecules that decrease in size.

Classical and Quantum mechanics are the two milestones of physics and mathematics. The path integral describes the generalised form of action from classical to quantum mechanics. This paper has reviewed some fundamental concepts and results in classical dynamics and quantum mechanics. The research method of the whole project is mainly theoretical derivations of applied mathematics and mathematical physics. This paper provides different perspectives to investigate the applications of path integrals. This paper also builds a connection between path integrals and the Unruh temperature.

As a rehabilitation robot for aiding in the movement of lower limbs, the lower limb exoskeleton is a beneficial device. In order to make the most effective use of the exoskeleton, the control strategy plays a crucial role. This review paper provides a background and classification of lower limb exoskeleton control strategies, such as model-based and hierarchy-based control. Further, we presented mainly the high-level control architecture of lower limb exoskeletons, which is aimed at detecting the intention of human movement. An in-depth discussion is provided in this paper regarding manual user input (MUI), surface electromyography (sEMG), and brain-computer interface (BCI). Many people need exoskeletons, which is why this review was written. Exoskeletons, however, are expensive and cannot be mass-produced, and their control methods are immature, making them ineffective. Thus, the objective of this review is to identify research gaps and common limitations in previous research to obtain future directions for improving the usability of the control mechanism. In an alternative approach, MUI and BCI are combined to reduce the time spent switching movement modes and the amount of concentration required to do so.

The theory of antimatter was proposed long ago and thought of as made up of antiparticles. Antimatter was believed to exist based on the theory of relativity and quantum mechanics, which are thought to be two fundamental concepts in modern physics. However, it turned out that scientists had great difficulty in finding antimatter. This has led to a discussion about what dark matter is made of and how it exists. Depending on the context of algebraic quantum field theory, antimatter does not consist of antiparticles, which means that antiparticles are particles that consist of antimatter. The notion of antimatter will be explained through the quantum field theory (QFT) theory. How we define the antimatter depends on our criteria in the physical state space. Recent research in AQFT（Advanced Quantum Field Theory) shows that all different quantum states possess antimatter counterparts, which has greatly expanded the field of antimatter research. Then several possible explanations for the distribution of antimatter and their theoretical foundation will be discussed. After exploration and observation across nearly one century, scientists still cannot get a reasonably clear picture of the distribution of antimatter. Why antimatter appeared and disappeared is still unknown, and attempts to find antimatter that exists in nature are going on. Scientists have had some good success when focused on the center of black holes and supermassive objects in space. There have been a lot of observations of antimatter in progress since decades of years ago.

The multiple traveling salesmen problems (MTSP) is a combinatorial optimization and np-hard problem. In practice, the computational resource required to solve such problems is usually prohibitive, and, in most cases, using heuristic algorithms is the only practical option. This paper implements genetic algorithms (GA) and simulated annealing (SA) to solve the MTSP and does an experimental study based on a benchmark from the TSPLIB instance to compare the performance of two algorithms in reality. The results show that GA can achieve an acceptable solution in a shorter time for any of the MTSP cases and is more accurate when the data size is small. Meanwhile, SA is more robust and achieves a better solution than GA for complex MTSP cases, but it takes more time to converge. Therefore, the result indicates that it is hard to identify which algorithm is comprehensively superior to the other one. However, It also provides an essential reference to developers who want to choose algorithms to solve MTSP in real life, facilitating them to balance the algorithm’s performance on different metrics they value.

Directional liquid transport has many cutting-edge applications, such as fog collection, agricultural drip irrigation, biochemical microreactors, water harvesting, non-powered micro-drug delivery, thin-film lubrication etc. There are many surfaces or linear structures in the natural systems can occur directional transport of water. In this paper, two bionic structures inspired by natural structures and two artificially fabricated surface structures are presented and their flow laws and mechanical mechanisms are described. Thereby, it is analysed that surface-driven external forces, such as the gradient of surface energy and the gradient of Laplace pressure, and surface pinning in other directions are the key points to drive the directional flow of liquids.

Elliptic flow signal, v_2, is widely studied in heavy ion collisions. Researches have been done to obtain the pseudorapidity dependence of v_2, while the measurement of transverse momentum dependence is relatively few. This paper presents the transverse momentum dependence of v_2 in Pb+Pb collision at√(s_NN )=5.02 TeV. The measurement is performed in the region with pseudorapidity |η|<2.8 and transverse momentum 〖0.2 < p〗_T<6.0GeV. In this work, we use sub-event method to measure elliptic flow for the collisions in different ranges of transverse momentum. The result shows a polynomial relation between elliptic flow signal and transverse momentum. In Section 4, the limitation of our measurement is also discussed.

The CFD technology based on the N-S equation plays an extremely important role in the detailed aerodynamic shape design of the complex surface of civil aircraft. In this paper, a consistent parameterization method, response surface model and numerical optimization method are used to conclude the optimization design. The aerodynamic optimization based on CFD, the commonly used CFD methods and free-form surface modeling technology are studied, and the challenges faced in the process of aerodynamic shape optimization of civil aircraft are analyzed. The adoption of a genetic algorithm based on the response surface increases the effectiveness of the entire optimization process.The results show that the adopted design method is effective in solving the problem of complex shape optimization using computationally expensive CFD codes. The advantage of the proposed method is that it can flexibly shape the wing body design and can quickly respond to changes in design requirements during the design process; the proposed method can be used in the design of a wider range of complex aerodynamic shapes.

To many of us, FORMULA ONE (F1) racing is an expensive sport that requires high-level entry requirements. However, compared to its value for entertainment purposes, the significance of its technological contribution to transportation in all different areas is enormous. This study focuses on understanding the physics and implementation behind aerodynamic designs in modern F1 racing cars. For this purpose, this research is conducted by analyzing the structure of the individual parts and their integrated effects on F1 aerodynamic performance. The result revealed that generating appropriate vortices around the boundary flows and guiding the airflow in an optimized direction can boost the performance of F1 cars. This study emphasizes the importance of utilizing airflow control for maximum maneuverability and might benefit many transportation-related industries.

With the increasing amount of data, there is a higher demand for fast access to data. Therefore, tree data structures are popular because they can quickly access data. In addition, with the increase of the cores of computer microprocessors, the tree data structure that can be concurrently controlled may be one of the most effective data structures today. However, there is still a lack of a summary of the differences in the data structures of various trees. Therefore, this paper collects code structures of different types of trees and then comments on the speed of various tree data structure types concerning parallel processing situations. The study covers the structure and performance of different kinds of trees and summarizes the method of concurrent control of these data structures. This paper can help to clearly understand the relationships and differences between various tree data structures and help them quickly learn the application of concurrency control in trees.

Images are frequently distorted by noises that have a negative impact on the quality of image data. In this study, the author focuses on coping with a specific type of noise that has arisen regularly in recent years as a result of the pandemic: masks covering portions of the photographs of human faces. The paper employs the autoencoder model, which offers unsupervised learning. It compresses or encodes original data input into a smaller latent vector, then decodes it back to its original size, learning and extracting relevant features from the data in the process. In a further phase, the author employs a combination of convolutional autoencoders and denoising autoencoders, treating masks as corruptions in order to get more accurate predictions regarding the image of a human face without any covering. After training on 2,500 image pairs with and without masks and validating on 200 such image pairs, the model presented in this research achieves an overall accuracy of 93%. The research demonstrates that the combination of convolutional and denoising autoencoders is an excellent method for removing masks from facial images, and the author believes it can also be used to effectively remove other types of noise. However, the study also reveals that the picture data generated in this manner are always inferior to the original, and that the autoencoder can only process data of the same or comparable type on which it has been trained. In the future, improved models will exist to address these shortcomings and be applied to more real-life situations.

The diffusion model is the process by which variables are introduced and propagated through a population. The equation can be applied to any physical system that exhibits such processes through time. Generally speaking, diffusion occurs when a physical system is connected to another by a small number of interconnected points; the interconnected points' overall connectivity can be represented by a network. The diffusion model is a mathematically defined process used to analyse the movement of particles from a region of high concentration to that of low concentration. Based on the diffusion coefficient and polarization, several studies have established that the equilibrium port-to-port distance can be calculated. The diffusion model is useful for solving the problem of noise in imaging systems, especially when an object has similar properties in all directions. When discussing diffusion, it is essential to refer to the diffusion coefficient. The literatures find denoising diffusion model to involves the process where a pixel value is estimated based on values at surrounding pixels. On the other hand, a forward process is passing through an image and replacing pixels based on their quality estimates. Reconstruction involves reconstructing an image from its components, including the subsamples and low-quality components. This model achieves satisfactory performance on digital number image generation.

In this paper we show that multilayer feedforward networks with one single hidden layer.and certain types of activation functions can approximate univariant continuous functions defined on a compact set. arbitrarily well. In particular, our results contain some usual activation functions such as sigmoidal functions, RELU functions and threshold functions. Besides, since interpolation problems are highly related to approximation problem, we demonstrate that a wide range of functions have the ability to interpolate and generalize our results to functions which are not polynomial on R. Compared to existing results by numerous work, our methods are more intuitive and less technical. Lastly, the paper discusses the possibility of combining interpolation property and approximating property together, and demonstrates that given any Riemann integrable functions on a compact set in R, with several points on its graph, the finite combination of monotone sigmoidal functions can pass through these points and approximate the given function arbitrarily well with respect to L^1 (dx) (in the sense of Riemann integral) when the number of points getting large.

Nowadays, there are many researches on natural language processing (NLP). Through the research of NLP method, many problems in machine learning field have been solved. However, since the study of Chinese NLP has not developed rapidly until recent years, there is still much to be studied on Chinese NLP. As an excellent pre-training model, whether Bidirectional Encoder Representation from Transformers (BERT) performs well on specific Chinese NLP remains to be studied. Therefore, this paper uses BERT for Chinese NLP, and trains BERT model by collecting news title data to achieve Chinese text classification. Finally, the prediction results are studied by statistical methods. The research shows that BERT method performs well on Chinese NLP and can predict different types of news headlines well. Although it performs differently on different kinds of titles, its performance is satisfactory on the whole, and the prediction results are relatively balanced in different categories. Therefore, BERT can be used as a very practical and efficient NLP method. At the same time, it can also be predicted that it will play a great role in Chinese NLP.

Helping trapped people understand the external situation and provide navigation to escape the fire is the key to reducing fire casualties. Thanks to the rapid development of artificial intelligence technology, the combination of simultaneous localization and mapping (SLAM) and path planning technology has gradually become a new research hotspot, which can help provide on-site fire information, maps and navigation for trapped people and firefighters. However, improving the accuracy of SLAM techniques under harsh conditions (e.g., thick smoke, high temperature) is still an open topic. Focusing on SLAM noise reduction and path planning, in this paper, we detail the latest research progress of SLAM technology in fire escape assistance. Specifically, we first introduce the current development and application frontiers of SLAM technology, and then analyze and compare the application of SLAM technology in fire scenarios. In addition, the performance changes of the two continuous A* algorithms and the RRT algorithm during global path planning for fire scenarios are compared. Finally, we discuss the development trend of SLAM in future fire escape and rescue.

Existing image translation methods already enable style transfer on unpaired data. Although these methods have yielded satisfactory results, they still result in changing the background while changing the object. One reason is that when using convolutional neural networks, global information is lost as the number of network layers increases, and the absence of an effective sensory field leads to the failure to generate high-quality results. This paper proposed a Non-Local-Attention-Cycle-Consistent Adversarial Networks for unpaired images style transfer. The no-local-attention can quickly capture long-range dependencies, better extracts global information, ensures effective focus on the foreground while preserving the background, and can be easily embedded into the current network architecture. Experiments are conducted on neural style transfer task with public dataset, this model can obtain the better result than CycleGAN. It allows better attention to structural features rather than just textural features. It can reconstruct some of the content lost by CycleGAN. Recent research has also demonstrated that the optimizer has an impact on the performance of the network. This paper applies the Nadam optimizer and find that this improves training process.

Gas turbine engine is mainly composed of compressor, inlet , tail nozzle, combustion chamber and turbine. The compressor pressurizes the air from the inlet into high-pressure air, which is used to mix and burn with fuel in the combustion chamber to form high-pressure gas. In this paper, numerical analysis of compressor stator performance and corresponding optimization were conducted using COMSOL Multiphysics 5.6. Firstly, an overview of compressor blade design is given. Then, the physical model and computational model are introduced and established by COMSOL. Afterwards, after the validation of the computational model, the numerical computations and results are generated and used to compare with NASA67 stator experimental data, with several key performance indicators including turning angle and loss coefficient. The polynomial expression is used to construct the blade shape/profile with different maximum thickness and its position, maximum bending and its position, trailing and leading edge angle and chord length are chosen as parameters for optimization. Then, I choose the maximum thickness and its position, maximum bending and its position, trailing and leading edge angle as the optimization parameter. Finally, we reach some conclusions of a better stator design possible for newer compressor requirements and further studies are recommended.