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  • 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.

  • 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.

  • The Impact of Trait Anxiety under a Painful Stimulus on the Chaotic Synchronization of Respiration and Pulse Waves

    Open Access

    Abstract: Thus far, attention has been paid to the relation between pain and anxiety, which has been studied. On the other hands, the earlier studies have hinted at the importance of considering mental and physical health from a holistic perspective, while taking into consideration the principles that prescribe the chaotic behavior of living organisms. Therefore, the purpose of this study is to use the respiration and pulse waves to examine the nature of the chaotic connection between biosignals involved in people’s mental and physical conditions, particularly those involved in the psychological trait of anxiety under a painful stimulus in this case. The results showed that with the high anxiety group, the extent of the synchronicity between their respiration and pulse waves under a painful stimulus increased, while this decreased for the low anxiety group. In other words, chaos dynamics for living systems are expressed in synchronous phenomena for the LLE for respiration and pulse waves. It also implied that these dynamics are prescribed by trait anxiety under a painful stimulus. This has opened up the possibility that, in the future, the cross-correlation function for LLE in pulse waves and respiration will make contributions to treating and assessing chronic pain in the field of clinical medicine.

    Keywords: Pain; Anxiety; Chaos; Synchronization

  • User Experience Evaluation on the Cryptocurrency Website by Trust Aspect

    Open Access

    Abstract:  A portal into the public ledger cryptocurrency makes a competition of the best web sites and easily trusted by the public. These are believed to constitute a measurable dimensions of User Experience (UX). This study aims to evaluate the user experience of the use of the three web sites most frequently accessed cryptocurrency from Indonesia. The evaluation conducted aimed at knowing the factors that influence user trust through the design of the interface and can be installed on a new design. Methods include Performance Metrics, Post-Task Rating, Post-Session Rating, and Experiential Overview and eye-tracking device. Based on the results of research, in the overall evaluation of the web site, the web site is the most superior of Indodax. The results of the evaluation are then applied on a new design using the software Invision and examined again to see a comparison of the respondent at the time of first use. The result of the research is the assessment, recommendations, and design the look of the web site cryptocurrency are trustworthy based on user experience

    Keywords: Cognitive Ergonomics, Human-Computer Interaction, User Experience, Cryptocurrency, Website, Emotional Design, Online Design

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    Formation of efficient and inefficient social convention driven by conformity bias

    Abstract: Social conventions govern social behavior in many ways, ranging from left- and right-hand traffic to greetings. We sometimes find that inefficient social conventions, such as bullying in a class, are spontaneously formed, where almost all the people in the group are at a disadvantage. Although such a convention can be disadvantageous for all the people in the group, why are those conventions formed and continue to be maintained? A conformity bias, the behavioral tendency with which people take an action that a majority of the group take, can be one of the key ingredients of this phenomenon. In this study, we investigated the impact of conformity bias and an individual’s trial-and-error learning on the spontaneous formation of social conventions. Analyzing the stationary states of the dynamics and the tipping points at which the dynamics are divided into one converging to the efficient and the other converging to the inefficient convention, we found that by increasing the extent of conformity bias, an inefficient convention tends to be formed. On the other hand, an individual’s trial-and-error learning can suppress conformity bias and promote the formation of efficient conventions.

     

    Keywords: Social convention; Conformity bias; Positive externality; Reinforcement learning model; Tipping points

  • Terahertz Spectrum Recognition of Pathogens Based on PCA-Siamese Neural Network

    Open Access

    Abstract: In the terahertz time-domain spectroscopy technique, 16 common pathogens were experimentally studied and their characteristics absorption spectra in the frequency range of 0.1 to 2.2THz were obtained. The terahertz absorption spectra of 16 common pathogens were trained and identified by Siamese neural network method. First, the terahertz absorption spectra of the 16 pathogens were reduced by PCA to construct training data. Then, the constructed Siamese neural network model was trained by back propagation. Finally, the pathogen measured at different times was used as the target spectrum to evaluate the model, after comparing with the training data, the matching absorption spectrum was obtained, and the recognition rate reached 97.34%. The recognition results fully indicate that the identification of different kinds of pathogens can be recognized by Siamese neural network, which provides an effective method of the detection and identification of pathogens by terahertz spectroscopy.

    Keywords: THz spectroscopy; machine learning; Siamese neural network; similarity learning

  • Application of vertical switching signal prediction based on ship networking in heterogeneous networks

    Open Access

    Abstract: With the rise of ship networking, in view of the coexistence of many heterogeneous networks, the vertical switching technology of heterogeneous networks is studied, and a vertical signal prediction algorithm is proposed by using grey theory. Through the simulation analysis of MATLAB test, it is verified that the algorithm can effectively improve the accuracy of the prediction signal and is suitable for ship terminals.

    Keywords: Ship Networking, Heterogeneous Network, Vertical Switching, Prediction Algorithm.