neural network

摘要： A large amount of data can partly assure good fitting quality for the trained neural networks.When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality data.Once the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation tools.Correspondingly,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and programmers.In this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by images.The temporal and spatial information is kept and the data are greatly compressed.In addition,the results are readable for not only computers but also human resources.Some examples are given,indicating the effectiveness of the proposed strategy.

摘要： We present interesting application of artificial intelligence for investigating effect of the COVID-19 lockdown on 3-dimensional temperature variation across Nigeria(2°-15°E,4°-14°N),in equatorial Africa.Artificial neural networks were trained to learn time-series temperature variation patterns using radio occultation measurements of atmospheric temperature from the Constellation Observing System for Meteorology,Ionosphere,and Climate(COSMIC).Data used for training,validation and testing of the neural networks covered period prior to the lockdown.There was also an investigation into the viability of solar activity indicator(represented by the sunspot number)as an input for the process.The results indicated that including the sunspot number as an input for the training did not improve the network prediction accuracy.The trained network was then used to predict values for the lockdown period.Since the network was trained using pre-lockdown dataset,predictions from the network are regarded as expected temperatures,should there have been no lockdown.By comparing with the actual COSMIC measurements during the lockdown period,effects of the lockdown on atmospheric temperatures were deduced.In overall,the mean altitudinal temperatures rose by about 1.1°C above expected values during the lockdown.An altitudinal breakdown,at 1 km resolution,reveals that the values were typically below0.5°C at most of the altitudes,but exceeded 1°C at 28 and 29 km altitudes.The temperatures were also observed to drop below expected values at altitudes of 0-2 km,and 17-20 km.

摘要： Time series data is a kind of data accumulated over time,which can describe the change of phenomenon.This kind of data reflects the degree of change of a certain thing or phenomenon.The existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction,but they are not enough to mine the periodicity of data.In this article,we perform periodic analysis on two types of time series data,select time metrics with high periodic characteristics,and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data.A loss calculation method for traffic time series characteristics is proposed as well.Multiple experiments have been conducted on actual data sets.The experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods(ARIMA,LSTM,etc.)and 3%-5%increase on MAPE.

摘要： Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization problems. This paper presents a data-free deep neural network(DNN) based trajectory optimization method for intercepting noncooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard constrained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to integrate interception dynamic model into the network and involve it in the process of gradient descent, which makes the network endowed with the knowledge of physical constraints and reduces the learning burden of the network. Thus, a DNN based method is proposed, which completely eliminates the demand of training datasets and improves the generalization capacity. Finally, numerical results demonstrate the feasibility and efficiency of our proposed method.

摘要： The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

摘要： Convolutional neural networks(CNNs)are the specific architecture of feed-forward artificial neural networks.It is the de-facto standard for various operations in ma-chine learning and computer vision.To transform this performance towards the task of network anomaly detection in cyber-security,this study proposes a model using one-dimensional CNN architecture.The authors'approach divides network traffic data into transmission control protocol(TCP),user datagram protocol(UDP),and OTHER protocol categories in the first phase,then each category is treated inde-pendently.Before training the model,feature selection is performed using the Chi-square technique,and then,over-sampling is conducted using the synthetic minority over-sampling technique to tackle a class imbalance problem.The authors'method yields the weighted average f-score 0.85,0.97,0.86,and 0.78 for TCP,UDP,OTHER,and ALL categories,respectively.The model is tested on the UNSW-NB15 dataset.

摘要： The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.

摘要： The global nuclear mass based on the macroscopic-microscopic model was studied by applying a newly designed multi-task learning artificial neural network(MTL-ANN). First, the reported nuclear binding energies of 2095 nuclei(Z ≥ 8, N ≥ 8) released in the latest Atomic Mass Evaluation AME2020 and the deviations between the fitting result of the liquid drop model(LDM)and data from AME2020 for each nucleus were obtained.To compensate for the deviations and investigate the possible ignored physics in the LDM, the MTL-ANN method was introduced in the model. Compared to the single-task learning(STL) method, this new network has a powerful ability to simultaneously learn multi-nuclear properties,such as the binding energies and single neutron and proton separation energies. Moreover, it is highly effective in reducing the risk of overfitting and achieving better predictions. Consequently, good predictions can be obtained using this nuclear mass model for both the training and validation datasets and for the testing dataset. In detail, the global root mean square(RMS) of the binding energy is effectively reduced from approximately 2.4 MeV of LDM to the current 0.2 MeV, and the RMS of Sn, Spcan also reach approximately 0.2 MeV. Moreover, compared to STL, for the training and validation sets, 3-9% improvement can be achieved with the binding energy, and 20-30% improvement for S_(n), S_(p);for the testing sets, the reduction in deviations can even reach 30-40%, which significantly illustrates the advantage of the current MTL.

摘要： Interference suppression is a challenge for radar researchers, especially when mainlobe and sidelobe interference coexist. We present a comprehensive anti-interference approach based on a cognitive bistatic airborne radar. The risk of interception is reduced by lowering the launch energy of the radar transmitting terminal in the direction of interference;main lobe and sidelobe interferences are suppressed via cooperation between the two radars. The interference received by a single radar is extracted from the overall radar signal using multiple signal classification(MUSIC), and the interference is cross-located using two different azimuthal angles. Neural networks allowing good, non-linear nonparametric approximations are used to predict the location of interference, and this information is then used to preset the transmitting notch antenna to reduce the likelihood of interception. To simultaneously suppress mainlobe and sidelobe interferences, a blocking matrix is used to mask mainlobe interference based on azimuthal information, and an adaptive process is used to suppress sidelobe interference. Mainlobe interference is eliminated using the data received by the two radars. Simulation verifies the performance of the model.

摘要： In this paper, the existence of almost periodic solutions to general BAM neural networks with leakage delays on time scales is first studied, by using the exponential dichotomy method of linear differential equations and fixed point theorem. Then, the exponential stability of almost periodic solutions to such BAM neural networks on time scales is discussed by utilizing differential inequality. Finally, an example is given to support our results in this paper and the results are up-to-date.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： Aiming at the trajectory tracking control of rnunderactuated vessels with parameters uncertain and rnexternal disturbance rnproblems, a stable adaptive neural network control rnmethod is proposed. Based on the diffeomorphism rntransformation, the new rntracking variables are given. First, the kinematics rncontroller is designed to track the new variables and rnthe velocity command is rnproposed. Second, the dynamics controller using rnneural network method is designed to make the real rnvelocity of the underactuated rnsurface vessel reach the desired velocity command. rnAll the parameters' update laws are selected in terms rnof Lyapunov functions,rnwhich guarantee the closed-loop systems to be rnuniformly ultimately bounded (UUB). Finally, a rnnumerical simulation illustrates the rneffectiveness of the proposed control method.