As we can see in the real world, there are frequent public security accidents (earthquake, fire, virus, and flood) in the past years. When the ordinary people are facing the flood disaster, they often do not know how to avoid danger and save themselves. By using the most advanced VR technology, we can popularize public safety knowledge, and improve public security awareness. This paper first describes the related works in flood simulation modeling, VR training, and interacting techniques in VR systems. Then it discusses the implementation of a virtual training system for flood security education based on Unity3D engine which is widely used for the time being. The users can not only experience the horror of public safety disasters in the virtual world, but also learn how to save himself in the event of flood disasters in the virtual environment.
In order to build a large model database for fruit 3D printing and require (1) sampling data is as simple as possible; (2) data volume and data structure are unified; (3) modeling method has multi-resolution capabilities, this paper presents a fruit shape modeling method based on wavelet interpolation, trying to uniformly sample 2 to the power of m longitude lines of fruits with a upright axis, and uniformly sample 2 to the power of n points on each longitude line. Then, we use wavelet interpolation to obtain the longitude and latitude lines models that meet the required precision for 3D printing. Additionally, we discuss several special issues of fruit shape 3D printing and Gcode file generation. The experimental results show that the modeling method has the advantages of good model effect, short printing time, and saving of printing material, which can achieve simple and practical application requirements.
Online learning monitoring is still on the urgent demand to track and analyze the learning engagement of the learners. To this end, the multimodal behavioral data of online learners are collected during the online learning process from the psychological, physiological and behavioral dimension which are respectively dependent on the techniques of expression recognition, physiological heart rate acquisition with Internet of Things and operation events listening. Based on the dataset with 25 experimental p, online learning engagement states are statistically analyzed and assessed by applying the schema including both prior rules and data fitting method which can quantitatively evaluate the learning engagement. Through the questionnaire survey and statistical validation, the assessment results show the schema can measure learning engagement in multiple dimensions and evaluate the whole state more comprehensively and automatically.
Traditional public service advertisements mostly adopt one-way publicity, such as radio and television, which has limitations on them. With the rapid development of communication technology, public service advertising has a new mode of publicity, such as interactive advertising, incentive advertising. Based on the development status and problems of public service advertisements, this study takes care of the mental health of depressed patients as the starting point and expands the communication experience of public service advertisements from the perspectives of psychology and behavior. On the basis of analyzing the “inertia” of users’ subconscious behavior, a button model is designed to guide users’ subconscious behavior. By simulating the negative effects of users’ subconscious behaviors, such as bringing pain to depressed patients or the possibility of suicide, this study gives people a self-reflection and self-evaluation experience in a natural interactive way, so as to achieve the communication significance of public service advertisements.
Failures (including hardware failures, software failures or unexpected shutdowns, sudden power failures, etc.) are unavoidable problems in large-scale distributed systems, so current distributed systems are required to support systematic fault tolerance. Aiming at the requirements of strong real-time application scenarios, this paper proposes a distributed cache and recovery method based on memory database. SQLite memory database is adopted and election-based multi-node data synchronization is introduced, which ensures the strong consistency of data on each node and eliminates the bottleneck and failure problems caused by the setting of the central node; at the same time, a dynamic load balancing mechanism is adopted to reduce the amount of synchronized data in the entire system and ensure the smooth operation of the system. Finally, the effectiveness of the proposed method is proved by experiments.
Since the first video game console called Pong was released in the 1970s, game consoles have grown continuously with the economic and technological development. There are some leading gaming manufacturers in current market, including Sony, Microsoft and Nintendo. As the eighth generation of video game console published by Microsoft, Xbox one is one of the most popular game consoles and thus attracts numerous customers. It is noticeable, however, Xbox one can no longer be treated as consoles but rather as computer-like machines. In this paper we are going to analyze partition, file system, relevant file and other data of Xbox One concretely.
In recent years, motion capture devices have been widely used in 3D film special effects, animation generation, digital media and other virtual reality fields. The purpose of this paper is to achieve data acquisition, motion feature extraction and recognition of human motion by using motion capture device. First, we use the OptiTrack motion capture device and related data preprocessing methods to collect motion data, and realize the preview of joint point offset data. Then, a human motion feature representation method which combines the collected data with the key frame is designed. For the selection of key frames, we improve the frame subtraction algorithm by adding the second derivative calculation of reconstruction error to achieve the number of key frames automatic determination. In addition, in order to solve the problem of huge amount calculations and error existing in discretization of observation state, we use high-dimensional gaussian function to fit human motion data, and finally apply the method of Gaussian-Mixture Hidden Markov Model (GMM-HMM) for motion recognition. Experiments show that the method has achieved remarkable performances in human motion extraction and recognition.
Culture is created during the process of human activities, and the development and prosperity of culture highly depend on the dissemination of media. In the context of international communication, Chinese culture is facing up with challenges both from cultural prejudice and media bias. With the breakthrough of Web 2.0, new media technologies have subverted the communication mechanism of the traditional media, and cultural communication also changed dramatically. Since the Strategy of Chinese Culture Going-out is launched, it is high time to adopt new technology and creative thinking to promote Chinese culture overseas. This paper explores the communication mechanism of social media, by analyzing the three core components of Platform, User and Content of social media. Based on the principle of Acculturation, it posits that the key points to break personal information cocoon should include the following methods: 1. Enriching cultural communication forms (utilizing the characteristic of social platform); 2. Customizing cultural communication scheme (by User Typology analysis); 3. Creating cultural communication contents (converging multiple new media technologies). This paper further points out that a Cross Cultural Hierarchy Phenomenon would occur if before-mentioned methods are adopted to raise cultural communication effect, and it is believed that the passive situation of Chinese cultural communication should be changed to promote Chinese culture overseas.
This paper presented a virtual reality experience system based on the theme of human body and cold virus struggle. In order to construct a realistic human cell scene, we applied physic-based rendering game production and Unreal® Engine. The proposed project used virtual reality, combining educational theory. Plentiful functionalities of virtual system were discussed in detail. As such, users could well participate in a war of cells to against virus from different perspectives. It is thought that users can become active learners, with they might well taking the initiative to acquire scientific knowledge and summing up their thinking in a lifelike experience, ultimately cultivating the ability of exploration and innovation.
In view of the problems of high cost and many potential hazards in real middle school experiments, we construct a virtual experiment simulation situation with virtual human guidance in experiment operation, intelligent algorithm assistance in experiment failure, and systematic evaluation at the end of experiment. First of all, the experimental rule library is established, including: experimental process, trigger standard, coding standard and error content. Secondly, based on these libraries, the flow of system algorithm is designed. Finally, take the experiment of heating potassium permanganate to produce oxygen as an example. The flow design, scene design and virtual human design of the experiment are given. Realize intelligent assistance and interactive real-time guidance of the experimental process.
The driver's distracted attention will cause a huge safety hazard to the traffic. In different types of distraction, it is illegal to make phone calls and smoke while driving, which will be fined in China. In order to solve this problem, a method of driver's distracted behavior detection based on channel attention convolution neural network is proposed. SE module is added to the Xception network, which can distinguish the importance of different feature channels and enhance the expression ability of the network. SE module mainly assigns different weights to features to enhance more important features and suppress less influential features. The experiment uses Xception and SE-Xception for comparison. The experimental results show that the accuracy of SE-Xception is 92.60%, which has a good performance for the distracted driving behavior detection of drivers.
Owing to the complexity of the anatomical structure of the pancreas, surgeons must perform long-term observations and strict training before performing operations. The application of AR technology provides an ideal surgical training platform and has important innovative significance for the surgical training of interns. Through the AR animation of pancreatojejunostomy, training efficiency can be improved. This article focuses on the feasibility and development of AR technology for application to pancreatic surgery, especially the role of AR technology in medical teaching and clinical practice. The study explores the development status, features, design methods, and applications of augmented reality animation as it relates to pancreatic surgery.
In order to monitor the aging of transformers and ensure the operational safety in substations, a practical detection system for indoor substation transformers based on the analysis of audio signal is designed, which use computer technology instead of manpower to efficiently monitor the transformers working states in real-time. Our work consists of a small and low cost AI-STBOX and an intelligent AI Cloud Platform. AI-STBOX is installed directionally in each transformer room for continuously collecting, compressing and uploading the transformers audio data. The AI Cloud Platform receives audio data from AI-STBOX, analyses and organizes the data to low-dimensional speech features with STFT and Mel cepstrum analysis. Input the features into a powerful deep neural network, the system can quickly distinguish the working states of each substation transformer before is has serious faults. It can locate aging transformers, command the maintenance platform to quickly release the repair task, thus avoid unforeseeable outages and minimize planned downtimes. The approach has achieved excellent results in the substation aging transformers detection scene.
Foliage morphological features are important for plant recognition. However, the foliage shape generally presents big intra-class variations and small inter-class differences. This brings a great challenge to accurate plant foliage recognition. In this paper, we propose a deep residual squeeze-excitation network (R-SENet) for foliage recognition. Firstly, R-SENet learns and obtains the significance levels of each channel of the various convolutional layers in a residual block to recognition tasks via squeeze-excitation strategy. Then, the weights of each channel are rescaled by means of the significances to promote the relevant channels and inhibit non-important channels. Finally, we evaluate the proposed approach on the well-known Flavia dataset for foliage recognition. The experimental results indicate that our approach achieves more accurate average recognition rate (up to 97.86%) and more robustness to noise than other outstanding approaches.
Road scene segmentation has always been regarded as a pixel-wise task in computer vision studies. In this paper, we introduce a practical and new features fusion structure named “Dual Path Network” for road semantic segmentation. This form aims to reduce the gap between low-level and high-level information, thereby improving features fusion. The Dual Path consists of two subpaths: Context Path and Spatial Path. In the Context Path, we select a pre-trained ResNet-101 model as the backbone and use multi-scale convolution blocks comprise the Spatial Path. Then, we create a fusion residual block and channel attention model to further optimize the network. The results of the experiment confirm a state-of-the-art mean intersection-over-union of 68.5% using the CamVid dataset.
