In this study, a 3D game “Dark Knight” is designed and developed by using unity3d game making engine as the development environment and combined with C# script. The game development chooses adventure puzzle solving ...
In this study, a 3D game “Dark Knight” is designed and developed by using unity3d game making engine as the development environment and combined with C# script. The game development chooses adventure puzzle solving games because adventure games have a good game plot, and the characters are usually given the concept of machinelearning. At the same time, this study adds mystery elements in the game scene design to increase the players' immersive experience in the game, better enjoy the game process and increase the richness of the game.
Since years machinelearning have played a significant part in the healthcare sector. Out of the various deep learning approaches, Convolutional Neural Networks has shown the most promising results in the classificati...
详细信息
With the emergence and development of Information Technology, language teaching andlearning methods have changed from traditional grammar translation to computer-assisted language teaching andlearning, and language ...
详细信息
With the development of computerapplications, machinelearning has become more widely used in the field of education, machinelearning can analyze the learning situation of a large number of students more efficiently...
详细信息
The fragmentation power is an important parameter to characterize the performance of a combatant. At present, the fragmentation power is mainly calculated by empirical formulae or simulation analysis, which has proble...
The fragmentation power is an important parameter to characterize the performance of a combatant. At present, the fragmentation power is mainly calculated by empirical formulae or simulation analysis, which has problems such as large calculation volume, slow calculation speed and low efficiency. In this paper, a machinelearning-based method for calculating the lethality of explosives is proposed. On the basis of analyzing the structure and material of explosive kill combat section, combining theoretical calculation data, simulation data and experimental data, the training model of machinelearning is constructed, and by introducing BP neural network algorithm, the rapid calculation of debris dynamic field characterization parameters is finally realized.
The widespread use of technology in hospitals and the difficulty of sterilizing computer controls has increased opportunities for the spread of pathogens. This leads to an interest in touchless user interfaces for com...
详细信息
machinelearning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing ...
详细信息
The competition for data resources has become an important part of the current market competition. The main sources of the college entrance examination data currently studied are third-party websites, or manually inpu...
The competition for data resources has become an important part of the current market competition. The main sources of the college entrance examination data currently studied are third-party websites, or manually input data from the official website. Both of these methods have major shortcomings. First of all, in terms of accuracy and limitation, there is no guarantee that the data is accurate. Secondly, in the process of using data, it will be clamped by a third party, so we use behavior simulation technology to collect data from the official website, and then resolve the data by solving the heterogeneity problem. Finally, the completeness and accuracy of the college entrance examination data can be guaranteed through this system platform.
With the rapid evolution of the field of computer science, automatic diagnosis of medical images has become possible. This paper introduces the latest research progress andapplication of deep learning in the medical ...
详细信息
In the study of transmission line fault location, most of the previous artificial intelligence-based location methods rely heavily on feature extraction of fault signals, which depend on the researcher's level of ...
详细信息
In the study of transmission line fault location, most of the previous artificial intelligence-based location methods rely heavily on feature extraction of fault signals, which depend on the researcher's level of analytical understanding of fault characteristics and require some experience. In addition, previous location methods are more sensitive to line parameters, and the machinelearning model obtained based on a specific line is not applicable to other lines, which restricts the application of the method. To solve the above problems, this paper proposes a double-ended combined fault location model based on Maximum Mean Discrepancy (MMD), which combined Convolutional Neural Network(CNN) and Long Short-Term Memory(LSTM). First, different transmission lines are categorized by MMD. Second, a double-ended CNN-LSTM combination model is built for similar lines, which autonomously extracts fault features in an end-to-end form, and then the weights of combination model are determined by the Q-learning algorithm. Finally, we obtain the fault distance prediction. Simulation studies show that the CNN-LSTM double-ended combined model based on MMD has good generalization performance for lines with different parameters, cracking the problem of specialized modeling of different lines while meeting the requirement of fault location accuracy. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-nd license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2ndinternationalconference on Power Engineering, ICPE, 2021.
暂无评论