Showing 37–48 of 180 results

  • Seashore Debris Detection Model with KaKaXi Camera Custom Dataset Using Instance Segmentation

    Abstract: Marine debris is impacting coastal landscapes majorly by affecting biodiversity, impairing recreational uses, causing losses to fishing industries, maritime industries, etc. Motivated by the need for automatic and cost-effective approaches for debris monitoring and removal, we employed computer vision technique together with deep learning-based model to identify and classify marine debris on several beach locations. This paper provides a comparative analysis of state-of-the-art deep learning architectures and proposed architecture which is used as feature extractor for debris image classification.

    The model is being proposed to detect seven categories of marine debris using a custom debris dataset, with the help of instance segmentation and a shape matching network, which can then be cleaned timely and efficiently. The manually constructed dataset for this system is created by annotating fixed KaKaXi camera images using CVAT with seven types of labels. A pre-trained HOG shape feature extractor is being used on LIBSVM along with template matching to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the recorded live debris data. The proposed network resulted in the improvement of misclassification of debris masks for objects with different illuminations, shape, occlusion and viewpoints.

    Keywords: debris; fixed camera images; computer vision; instance segmentation; deep learning; template matching; Histogram of Gradients (HOG)

  • Quantitative Evaluation of Orthodontic Treatment by Moment Measurement Device

    Abstract: The purpose of this study is to quantitatively evaluate orthodontic treatment. The forces and moments applied to the teeth during treatment are rarely measured. Therefore, dentists must rely on their own skills, experience, and senses to perform treatment, which may not be sufficient for some patients. To solve this problem, devices have been developed to measure the forces and moments generated in the teeth. However, there are disadvantages, such as the limitation of the direction in which the forces and moments can be measured and the fact that they do not consider the movement of the teeth during treatment. Therefore, we have developed a device that can measure forces and moments in all axes, reproducing the movement of the teeth during treatment. The developed device consists of two teeth model, a force sensor, and a stepping motor. Considering that the teeth move during treatment, this device was incorporated a motor to control the angle of the teeth. A force sensor is used to measure the forces and moments in the three axes applied to the teeth. This device can reproduce the treatment of abnormally tilted teeth until teeth return to normal position. Quantitative evaluation was performed using the device. A comparative study was conducted between the case of treatment with stainless steel wires and the case of treatment with nickel titanium wires. The result showed that nickel titanium wire has many advantages compare the conventional stainless-steel wires. The advantages are agreed with the dentist’s rule of thumb, and the experimental results suggest that this device was able to make a quantitative evaluation of orthodontic treatment.

    Keywords: Force and moment measurement device; Movement of teeth; Orthodontic treatment

  • Emergent Transition from Radial Foraging to Tree-like Raiding Patterns Induced by Complete Following for Pheromone

    Abstract:  Among ants that show complex and diverse collective actions, army ants are known to raid prey in groups. It has been confirmed that the characteristic pattern in a swarm raid of army ants changes from radial to tree-like with time. There are some simulation studies focusing only on the emergence of tree-like patterns. However, the models adopted in their simulations cannot represent the transition of patterns from radial to tree-like observed in the real world. In this study, we propose an army ant model with a modification of the model adopted by Solé et al. and clarify the conditions of the emergence of the transition. From the experimental simulation results of our model, we deduce that the simple modification of the Solé model can move individuals in eight directions, but it is not enough to express multidirectional to unidirectional convergence. Additionally, our simulation experiments showed that for the pattern transition from radial to tree-like, it is necessary for the individuals to keep moving forward until they get food, and for the returning individual to completely follow the pheromone.

    Keywords: army ant model, radial foraging pattern, tree-like raiding pattern, swarm raid, complete following for pheromone.

  • 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.