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Showing 1–12 of 141 results

  • Speech Recognition Signal Lamp Image Simulation

    Abstract:  Speech recognition technology is a method of computer sound signal processing, which determines human behavior by analyzing the characteristics of sound signal. It has a wide range of applications in modern science and technology, and is a new frontier science, also is known as intelligent language. With the continuous improvement of people’s requirements for the quality of life, intelligent devices have sprung up in various fields. It is very practical to apply speech recognition technology to smart home reasonably to make home life more comfortable, safe and effective. The recognition of speech signal is primarily completed by preprocessing, feature extraction, training and pattern matching; the user interface is established by using the function of Matlab GUI, and the signal lamp image based on speech recognition is simulated and controlled by using the software.

    Keywords: Speech recognition; Endpoint detection; Feature extraction; pattern recognition

  • Intelligent Ramp Patrol Car based on MSP430

    Abstract:  This article aims to use the MSP430 single-chip to design an intelligent car, which is suitable for the precision tracking of various inclined slopes. The patrol car uses infrared sensors to collect ramp trajectory information and to adjust the forward direction through the front-axle steering mechanism. The gyroscope collects the car status in real time so that the car is controlled to perform uphill acceleration, downhill deceleration. Such a design allows the patrol car to be stabilized in the ramp with the established route.

    Keywords: MPU6050, patrol car, front steering axle, ramp track

  • Research on Similar Odor Recognition Based on Bid Data Analysis

    Abstract: In the common olfactory system odor recognition is processed by the electronic nose collecting sensor data, but the odor data collection of substances is easily affected by the environment and the processing is complicated, which is prone to deviation. This paper proposes a method based on big data analysis. According to the different chemical structure characteristics of different odor substances, the BP neural network is used to build a model to classify and recognize similar odors, and compare it with the traditional PCA+LDA recognition method. The results show that the establishment of a similar odor recognition model can accurately classify substances with similar odors, and the BP neural network algorithm is used to identify different substances with a higher rate of odor recognition. This method is stable and simple, and can provide different ideas for odor identification.

    Keywords: Smell recognition; Big data analysis; BP neural network; Similar smell

  • Text Classification Based on Title Sematic Information

    Abstract: With the rapid development of big data technology, text classification plays an important role in practical application, its applications span a wide range of activities such as sentiment analysis, spam detection, etc. Traditionally, we model the relationship between document and label. However, in many scenarios, document have specific relationship with corresponding title. Inspired by this, a text classification model based on title Semantic Information is proposed in this study. In our model, long short-term memory (LSTM)is used to learn title embedding, document embedding is obtained by using promoted LSTM(TS-LSTM) which take into account the title information. The experimental results on the standard text classification datasets show that its performance is better than the existing state-of-the-art text classification algorithms.

    Keywords: Text classification; natural language processing; deep learning; LSTM

  • Design of the Automatic Control System for Restaurant Food Delivery Based On PLC

    Abstract: The paper designs an automatic control system for restaurant food delivery based on PLC, including the mechanical structure and automatic control system design. The mechanical structure of the system includes horizontal delivery subsystems and a vertical delivery subsystem. The automatic control system includes PLC control and the human-machine interface, which realizes the entire system’s automation. At the end of the paper, we analyze the whole system’s reliability and economy to reflect the characteristics and practicability of the automatic control system.

    Keywords: Food delivery system; PLC control; human-machine interface; reliability

  • License Plate Recognition Algorithm Based on Convolutional Neural Network

    Abstract: In order to improve the problem of unequal suspension positions in the traditional license plate recognition system, this paper introduces the convolutional neural network algorithm into the license plate recognition system, and conducts a series of tests and corrections to meet the current license plate recognition system. This paper proposes for the first time that the flood filling algorithm is applied to the preprocessing of the license plate image, the recognized contour is divided into regions, and then the license plate inclination angle is offset, and rough positioning and cutting are performed to make the vehicle shot from the side The picture can also fully identify the license plate, and finally judge according to the aspect ratio of the license plate and the standard aspect ratio, and get whether the recognized license plate is. The experimental results show that the model utilizes the advantages of convolutional neural network so that the model can recognize classification features more accurately.
    Keywords: License plate recognition, Convolutional neural network, Flood filling algorithm

  • Research and Implementation of FacialNet Based on Convolutional Neural Network

    Abstract: Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people’s daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition. In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.

    Keywords: Convolutional neural network, Face recognition, Gender recognition, Age recognition

  • Analysis and Prediction of COVID-19 in Xinjiang based on Machine Learning

    Abstract: Covid-19 has taken the world by storm, dramatically affecting the lives of people around the world. China is a major country in the fight against the epidemic. It has provided the world with a wealth of valuable experience in the prevention and treatment of COVID-19. Based on the data released by Xinjiang Health Commission, this study used mathematical modeling method to reasonably predict and analyze the trend of the number of coVID-19 confirmed in the recent outbreak in Xinjiang through machine learning polynomial regression under limited data conditions, aiming at the coVID-19 outbreak in Xinjiang in July.

    Keywords: COVID-19, estimates of the number of confirmed cases, Machine   learning, Polynomial regression.

  • Design of Smoke Alarm Device Based on STM32C8T6 Single-chip Microcomputer

    Open Access

    Abstract: At present, with the popularization of high-power electrical equipment, more and more fires occurred. In order to protect the safety of people’s lives and property, the prevention and monitoring of fires has become an urgent problem in today’s society. The system is a single-chip microcomputer smoke alarm system designed for schools, families, hotels, cinemas, office buildings and public places. It uses STM32C8T6 single-chip microcomputer as the controller. When the device detects signals through the MQ-2 smoke sensor, then inputs the signals to the single-chip microcomputer after A/D conversion. And the buzzer is also controlled through the single-chip microcomputer to realize the smoke alarm. The system has the characteristics of high reliability, low cost, and easy maintenance.

  • A Study on Smart Home Voice Control Terminal

    Open Access

    Abstract—With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control.
    This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.
    Keywords—Arm microcontroller; Speech recognition; Wireless transceiver; Voice broadcast

  • Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly Patients

    Open Access

    Abstract:  For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which  makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.

    Keywords: Soft Computing; Medical Decision Support; Triage Assessment; Fuzzy Cognitive Maps

  • Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination.

    Open Access

    Abstract:  The paper thoroughly analyzes the use of Principal Component Analysis (PCA) in comparison to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice with PCA, while decision trees were the classification of choice with IG. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. The 7 Infleunza-A segments generating the best results were used for comparison. Sequences from each segment were further divided into Adamantane-resistant, & non-Adamantane-resistant. Accuracy, sensitivity, specificity precision & time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. IG had higher accuracy. The best accuracy generated by PCA & NNs on the M1/M2 was 96.5%, while that of IG & DTs was 98.2% Using PCA features & DTs also generated a comparable accuracy to that of IG features & DT at 97.6% on the M1/M2 segment. There was a 88%  increase in feature selection processing speed when using PCA compared to IG on the M1/M2 segment alone

    Keywords: Influenza-A, Principle component analysis (PCA), Machine learning, Information gain, DNA classification, decision trees, neural networks.