KEYWORDS: Sensors, Virtual reality, Data modeling, 3D modeling, Statistical modeling, Instrument modeling, Data acquisition, Matrices, Control systems, Target detection
For the data of virtual scene, there are often heterogeneous data from different databases. If the same object is not processed in different databases, it will have a great impact on the subsequent data synchronization. After the synchronization of heterogeneous information, the differences between these heterogeneous information will be eliminated, which will bring great convenience to the subsequent data synchronization. Therefore, this paper is based on the synchronization of heterogeneous information data in remote control and unified description. The utilized heterogeneous data synchronization is feature layer synchronization. Feature sets represented by each data are extracted from each database and are synchronized into feature vectors. Then the synchronization of the feature vectors is carried out. The experimental results show that the method in this paper can accurately warn the abnormal behavior between data synchronization and realize the function of remote control stop. Through the research of virtual control problem and according to the result of data synchronization, warning is carried out to remind the remote operators to control the virtual scene position and posture in real time and to reduce the occurrence of collision accidents.
With the rapid development of computer technology and communication technology, computers have been applied to all areas of people's lives. In the field of education, it is a necessary function for the intellectualization of the education system to realize the automatic marking of examination papers by computer. In intelligent marking, the traditional method is used to segment the subjective question area of the test paper, which can not effectively segment different types of answer sheets and has the defects of low segmentation accuracy. The edge detection method is used to correct the subjective question area of the answer sheet, and the boundary box of the subjective question part is accurately screened by positioning the top, left, and bottom positioning lines of the answer sheet to complete the segmentation of the subjective question area in marking. The experimental results show that the correct rate of the new method is more than 95%, which is much higher than that of the traditional method. The new method can be widely used in intelligent marking work, and can effectively improve the efficiency of intelligent marking.
KEYWORDS: Speech recognition, Telecommunications, Signal processing, Data communications, Data conversion, Databases, Control systems, Computing systems, Medical research, Surgery
The traditional medical voice question and answer interactive system can not meet the core needs of patient consultation service, and the patient's satisfaction with the system answer is low, so the research of medical voice question and answer interactive system based on speech recognition technology is proposed. In the hardware of the system, the control core of the system is the MCU module, which controls the normal operation of the whole system, realizes the conversion of the analog signal and digital signal through the audio input device, designs the serial communication device to transmit the data information to the computer, and uses the speech recognition technology to realize the software part of the system. And through the system test, the medical voice question and answer interactive system based on speech recognition technology is compared with the traditional medical question and answer interactive system. The experimental results show that the average response time of the medical voice question and answer interaction system based on speech recognition technology to the user request is far less than the traditional system, there is no number of error requests, and the performance is better than the traditional system. Therefore, it can be proved that the performance of the medical question and answer interactive system based on speech recognition technology has reached the expected standard, and can meet the basic needs of users for medical consultation services
KEYWORDS: Particles, Particle swarm optimization, Optimization (mathematics), Detection and tracking algorithms, Data centers, Algorithm development, Statistical analysis, Machine learning, Internet, FDA class I medical device development
In the process of continuous development of e-commerce, to better meet the needs of users and tap the consumption potential of users, personalized recommendation systems have emerged on various e-commerce platforms. Although the clustering algorithm is suitable for solving the user segmentation problem, the traditional K-means algorithm has some shortcomings, such as the quality of the initial center point determined randomly is not high, and there is no definite criterion to select the value of K. Therefore, this paper proposes an optimized K-means algorithm, which uses the value of effectiveness index CH to determine the value of K, and combines with particle swarm algorithm to solve the initial center point. Experiments show that the optimization algorithm CH-PSO-K-means algorithm proposed in this paper has improved the DB index, accuracy rate, and error square index. The comprehensive performance of the clustering effect has been significantly improved, which can effectively solve the problem of e-commerce user segmentation with large data and multiple characteristics. The optimization algorithm not only makes up for the shortcomings of the K-means algorithm, but also improves the maintenance strategy of individual e-commerce users, which is conducive to enterprise cost control and profit improvement.
With the practical application of the self-marking system, it can give an objective and fair score, but it can not meet the needs of classroom teaching. It also needs to give students rapid feedback on the vocabulary, sentence, text structure, content relevance, and other dimensions presented in the test paper. The scoring method based on artificial features is the earliest self-marking scoring method, which uses experts to design some scoring features from the language quality, content quality, and text structure of the test paper. It takes the scoring task as a regression or classification task to score or rate the test paper. In this paper, based on the deep learning theory, a method for text similarity detection using a twin network is proposed. Considering the interaction between text pairs, we integrate expressivity pooling based on bidirectional GRU and measure the similarity by distance calculation formula. The experimental data show that the two-way GRU network integrated with expression pooling can obtain the interaction between text pairs so that the extracted features of the text are more comprehensive. The model has a better effect in the study of the similarity of text pairs. This method can reduce the difference in scoring results caused by different subjective consciousness of raters, make the scoring results more objective and persuasive, and improve the accuracy and efficiency of scoring.
KEYWORDS: Sensor networks, Sensors, Data processing, Data transmission, Data storage, Computer simulations, Data modeling, Wireless communications, Tin, Telecommunications
Wireless sensor network (WSN) is a wireless communication self-organizing network composed of multiple sensor nodes, which can be used to obtain the information of the monitored objects in the target area. However, due to the characteristics of the nodes that make up the wireless sensor network, the network has the characteristics of large scale, low energy consumption, limited computing and storage capacity. These characteristics play a decisive role in the application of wireless sensors. In this paper, a weight algorithm based on the adaptive sleep mechanism in wireless sensor networks is proposed to improve the network performance. The algorithm mainly aims at the adjustment of the weight, and then to evaluate the importance of the data. By applying the weight algorithm, the original algorithm based on the sleep scheduling mechanism is improved. The performance of the whole network is significantly improved compared with the network optimized by LEACH algorithm and GAF algorithm. The important data can be transmitted to the sink node as soon as possible, which effectively improves the link quality. The energy consumption of the nodes in the network is balanced through the adaptive sleep scheduling algorithm without location information, which improves the network performance and prolongs the network lifetime as much as possible. The simulation results show that the proposed network can perform better than the fully connected network in most cases. In the application and promotion of wireless sensors, there are more in-depth research and wider applications.
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